From 5b04b56fe26cac02519b15b582684d11e09e1788 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Tue, 23 Jan 2024 13:17:19 -0700 Subject: [PATCH 001/151] Add per kW and per kWh installed capacity based battery O&M --- src/core/energy_storage/electric_storage.jl | 8 ++++++++ src/core/reopt.jl | 8 +++++--- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 2d84bd281..26ea93737 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -150,6 +150,8 @@ end installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 replace_cost_per_kwh::Real = 318.0 + om_cost_per_kw::Real = 0.0 + om_cost_per_kwh::Real = 0.0 inverter_replacement_year::Int = 10 battery_replacement_year::Int = 10 macrs_option_years::Int = 7 @@ -182,6 +184,8 @@ Base.@kwdef struct ElectricStorageDefaults installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 replace_cost_per_kwh::Real = 318.0 + om_cost_per_kw::Real = 0.0 + om_cost_per_kwh::Real = 0.0 inverter_replacement_year::Int = 10 battery_replacement_year::Int = 10 macrs_option_years::Int = 7 @@ -220,6 +224,8 @@ struct ElectricStorage <: AbstractElectricStorage installed_cost_per_kwh::Real replace_cost_per_kw::Real replace_cost_per_kwh::Real + om_cost_per_kw::Real + om_cost_per_kwh::Real inverter_replacement_year::Int battery_replacement_year::Int macrs_option_years::Int @@ -307,6 +313,8 @@ struct ElectricStorage <: AbstractElectricStorage s.installed_cost_per_kwh, replace_cost_per_kw, replace_cost_per_kwh, + s.om_cost_per_kw, + s.om_cost_per_kwh, s.inverter_replacement_year, s.battery_replacement_year, s.macrs_option_years, diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 17d2b1082..11a8b096d 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -362,9 +362,11 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) sum( p.s.storage.attr[b].net_present_cost_per_kwh * m[:dvStorageEnergy][b] for b in p.s.storage.types.all ) )) - @expression(m, TotalPerUnitSizeOMCosts, p.third_party_factor * p.pwf_om * - sum( p.om_cost_per_kw[t] * m[:dvSize][t] for t in p.techs.all ) - ) + @expression(m, TotalPerUnitSizeOMCosts, p.third_party_factor * p.pwf_om * ( + sum(p.om_cost_per_kw[t] * m[:dvSize][t] for t in p.techs.all) + + sum(p.s.storage.attr[b].om_cost_per_kw * m[:dvStoragePower][b] for b in p.s.storage.types.elec) + + sum(p.s.storage.attr[b].om_cost_per_kwh * m[:dvStorageEnergy][b] for b in p.s.storage.types.elec) + )) add_elec_utility_expressions(m, p) From 8627a8d500de4a24af635dc3124e4482711bb8e1 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Wed, 24 Jan 2024 11:20:16 -0700 Subject: [PATCH 002/151] Add O&M financials to ElectricStorage results --- src/results/electric_storage.jl | 6 + test/Manifest.toml | 391 ++++++++++++++++++++++++++++++++ 2 files changed, 397 insertions(+) create mode 100644 test/Manifest.toml diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 4c2b1483c..ac24ee7c1 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -34,6 +34,12 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: r["initial_capital_cost"] = r["size_kwh"] * p.s.storage.attr[b].installed_cost_per_kwh + r["size_kw"] * p.s.storage.attr[b].installed_cost_per_kw + StoragePerUnitOMCosts = p.third_party_factor * p.pwf_om * (p.s.storage.attr[b].om_cost_per_kw * m[Symbol("dvStoragePower")][b] + + p.s.storage.attr[b].om_cost_per_kwh * m[Symbol("dvStorageEnergy")][b]) + + r["lifecycle_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) + r["year_one_om_cost_before_tax"] = round(value(StoragePerUnitOMCosts) / (p.pwf_om * p.third_party_factor), digits=0) + if p.s.storage.attr[b].model_degradation r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])["ElectricStorage"]; r["maintenance_cost"] = value(m[:degr_cost]) diff --git a/test/Manifest.toml b/test/Manifest.toml new file mode 100644 index 000000000..436a81cf2 --- /dev/null +++ b/test/Manifest.toml @@ -0,0 +1,391 @@ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.8.0" +manifest_format = "2.0" +project_hash = "cf327925f9ed4a23852062425dc15cc7a29452ac" + +[[deps.ArgTools]] +uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" +version = "1.1.1" + +[[deps.Artifacts]] +uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" + +[[deps.Base64]] +uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" + +[[deps.BenchmarkTools]] +deps = ["JSON", "Logging", "Printf", "Profile", "Statistics", "UUIDs"] +git-tree-sha1 = "f1f03a9fa24271160ed7e73051fba3c1a759b53f" +uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" +version = "1.4.0" + +[[deps.Bzip2_jll]] +deps = 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+[[deps.Unicode]] +uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" + +[[deps.Xpress]] +deps = ["Libdl", "LinearAlgebra", "MathOptInterface", "SparseArrays"] +git-tree-sha1 = "29c47b54b6938852a598fc6761ed927aad61f10e" +uuid = "9e70acf3-d6c9-5be6-b5bd-4e2c73e3e054" +version = "0.16.2" + +[[deps.Zlib_jll]] +deps = ["Libdl"] +uuid = "83775a58-1f1d-513f-b197-d71354ab007a" +version = "1.2.12+3" + +[[deps.libblastrampoline_jll]] +deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" +version = "5.1.1+0" + +[[deps.nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.48.0+0" + +[[deps.p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+0" From 564365f4f393f1e9ccc13119aa8101d88fedd537 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Wed, 28 Feb 2024 21:26:17 -0700 Subject: [PATCH 003/151] Add battery self discharge, defaulted to zero --- src/constraints/storage_constraints.jl | 2 ++ src/core/energy_storage/electric_storage.jl | 6 +++++- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index e13c7d8fb..c7e5b493e 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -57,6 +57,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - m[Symbol("dvDischargeFromStorage"*_n)][b,ts] / p.s.storage.attr[b].discharge_efficiency ) + - ((p.s.storage.attr[b].daily_self_discharge_fraction/24/p.hours_per_time_step) * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) # Constraint (4h): state-of-charge for electrical storage - no grid @@ -65,6 +66,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.elec) - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] / p.s.storage.attr[b].discharge_efficiency ) + - ((p.s.storage.attr[b].daily_self_discharge_fraction/24/p.hours_per_time_step) * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) # Constraint (4i)-1: Dispatch to electrical storage is no greater than power capacity diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 26ea93737..b496f1c2a 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -166,6 +166,7 @@ end model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 + daily_self_discharge_fraction::Float64 = 0.0 ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -200,6 +201,7 @@ Base.@kwdef struct ElectricStorageDefaults model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 + daily_self_discharge_fraction::Float64 = 0.0 end @@ -242,6 +244,7 @@ struct ElectricStorage <: AbstractElectricStorage model_degradation::Bool degradation::Degradation minimum_avg_soc_fraction::Float64 + daily_self_discharge_fraction::Float64 function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -330,7 +333,8 @@ struct ElectricStorage <: AbstractElectricStorage net_present_cost_per_kwh, s.model_degradation, degr, - s.minimum_avg_soc_fraction + s.minimum_avg_soc_fraction, + s.daily_self_discharge_fraction ) end end From c2f3c5d8c29918055a77d35c9103b7eaf7a81711 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Thu, 28 Mar 2024 17:54:40 -0600 Subject: [PATCH 004/151] Add battery self-discharge to MPC model --- src/mpc/structs.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index 14280665b..f6b8b8d1b 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -242,6 +242,7 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage max_kw::Float64 = size_kw max_kwh::Float64 = size_kwh minimum_avg_soc_fraction::Float64 = 0.0 + daily_self_discharge_fraction::Float64 = 0.0 end From 37e30836faa5b5f019371e993a681ab769167c5e Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Fri, 29 Mar 2024 11:45:16 -0600 Subject: [PATCH 005/151] Enable BESS O&M for multiple sites --- src/results/electric_storage.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index ac24ee7c1..5dfba4d09 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -34,8 +34,8 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: r["initial_capital_cost"] = r["size_kwh"] * p.s.storage.attr[b].installed_cost_per_kwh + r["size_kw"] * p.s.storage.attr[b].installed_cost_per_kw - StoragePerUnitOMCosts = p.third_party_factor * p.pwf_om * (p.s.storage.attr[b].om_cost_per_kw * m[Symbol("dvStoragePower")][b] + - p.s.storage.attr[b].om_cost_per_kwh * m[Symbol("dvStorageEnergy")][b]) + StoragePerUnitOMCosts = p.third_party_factor * p.pwf_om * (p.s.storage.attr[b].om_cost_per_kw * m[Symbol("dvStoragePower"*_n)][b] + + p.s.storage.attr[b].om_cost_per_kwh * m[Symbol("dvStorageEnergy"*_n)][b]) r["lifecycle_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) r["year_one_om_cost_before_tax"] = round(value(StoragePerUnitOMCosts) / (p.pwf_om * p.third_party_factor), digits=0) From 9ee741ec42c0b4b1d103ff87902f8beb529b6363 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Mon, 1 Apr 2024 20:25:31 -0600 Subject: [PATCH 006/151] Add code comments and tests for battery O&M and self-discharge; update the changelog --- CHANGELOG.md | 9 +++- src/core/energy_storage/electric_storage.jl | 8 ++-- test/runtests.jl | 52 +++++++++++++++++++++ test/scenarios/storage_om.json | 43 +++++++++++++++++ 4 files changed, 107 insertions(+), 5 deletions(-) create mode 100644 test/scenarios/storage_om.json diff --git a/CHANGELOG.md b/CHANGELOG.md index 8672c2ead..b1898e582 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,7 +23,14 @@ Classify the change according to the following categories: ### Deprecated ### Removed -## Develop 2024-03-26 +## Develop 2024-04-01 +### Added +- Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M +- Added new input **daily_self_discharge_fraction** to `ElectricStorage` for modeling battery self-discharge +- Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` +- Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` + +## v0.44.0 ### Added - in `src/settings.jl`, added new const **INDICATOR_COMPATIBLE_SOLVERS** - in `src/settings.jl`, added new member **solver_name** within the settings object. This is currently not connected to the solver but does determine whether indicator constraints are modeled or if their big-M workarounds are used. diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index c41995266..42ca244b8 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -150,8 +150,8 @@ end installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 replace_cost_per_kwh::Real = 318.0 - om_cost_per_kw::Real = 0.0 - om_cost_per_kwh::Real = 0.0 + om_cost_per_kw::Real = 0.0 # Capacity-based O&M costs in $/kW-rated/year. When including battery replacement costs, the user should ensure that O&M costs do not account for augmentation/replacement. + om_cost_per_kwh::Real = 0.0 # Capacity-based O&M costs in $/kWh-rated/year. When including battery replacement costs, the user should ensure that O&M costs do not account for augmentation/replacement. inverter_replacement_year::Int = 10 battery_replacement_year::Int = 10 macrs_option_years::Int = 7 @@ -166,8 +166,8 @@ end model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - daily_self_discharge_fraction::Float64 = 0.0 -``` + daily_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per day loss based on kWh stored in each timestep +``` """ Base.@kwdef struct ElectricStorageDefaults off_grid_flag::Bool = false diff --git a/test/runtests.jl b/test/runtests.jl index b59171d26..953e6c763 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -520,6 +520,58 @@ else # run HiGHS tests @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 end + @testset "Electric Storage O&M" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/storage_om.json") + + data["ElectricStorage"]["om_cost_per_kw"] = 10 + data["ElectricStorage"]["om_cost_per_kwh"] = 0 + + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(model, inputs) + + @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=0.01 + @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=0.01 + + data["ElectricStorage"]["om_cost_per_kw"] = 0 + data["ElectricStorage"]["om_cost_per_kwh"] = 5 + + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(model, inputs) + + @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=0.01 + @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=0.01 + end + + @testset "Electric Storage Self-Discharge" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/storage_om.json") + + data["ElectricStorage"]["soc_init_fraction"] = 1.0 + data["ElectricStorage"]["soc_min_fraction"] = 0.0 + data["ElectricStorage"]["daily_self_discharge_fraction"] = 0.0025 + + s = Scenario(data) + inputs = REoptInputs(s) + results_self_discharge = run_reopt(model, inputs) + + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data["ElectricStorage"]["daily_self_discharge_fraction"] = 0.0 + + s = Scenario(data) + inputs = REoptInputs(s) + results_no_self_discharge = run_reopt(model, inputs) + + @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 + @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 + @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - + sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + + end + @testset "Imported Xpress Test Suite" begin @testset "Heating loads and addressable load fraction" begin # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler diff --git a/test/scenarios/storage_om.json b/test/scenarios/storage_om.json new file mode 100644 index 000000000..167db02f9 --- /dev/null +++ b/test/scenarios/storage_om.json @@ -0,0 +1,43 @@ +{ + "Site": { + "longitude": -118.1164613, + "latitude": 34.5794343, + "roof_squarefeet": 5000.0, + "land_acres": 1.0 + }, + "ElectricLoad": { + "doe_reference_name": "RetailStore", + "annual_kwh": 10000000.0, + "year": 2017 + }, + "ElectricStorage": { + "min_kw": 40, + "max_kw": 40, + "min_kwh": 80, + "max_kwh": 80, + "can_grid_charge": false, + "replace_cost_per_kw": 460.0, + "replace_cost_per_kwh": 230.0, + "installed_cost_per_kw": 1000.0, + "installed_cost_per_kwh": 500.0, + "total_itc_fraction": 0.0, + "macrs_option_years": 0, + "macrs_bonus_fraction": 0 + }, + "ElectricTariff": { + "urdb_label": "5ed6c1a15457a3367add15ae" + }, + "PV": { + "min_kw": 0.0, + "max_kw": 0.0 + }, + "Financial": { + "elec_cost_escalation_rate_fraction": 0.026, + "offtaker_discount_rate_fraction": 0.05, + "owner_discount_rate_fraction": 0.05, + "analysis_years": 20, + "offtaker_tax_rate_fraction": 0, + "owner_tax_rate_fraction": 0, + "om_cost_escalation_rate_fraction": 0.025 + } +} From e2a0cacea0f8605278b28f5be1b9c643d6a502ce Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Tue, 2 Apr 2024 11:16:42 -0600 Subject: [PATCH 007/151] Update code comments --- src/core/energy_storage/electric_storage.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 42ca244b8..733d95fb4 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -150,8 +150,8 @@ end installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 replace_cost_per_kwh::Real = 318.0 - om_cost_per_kw::Real = 0.0 # Capacity-based O&M costs in $/kW-rated/year. When including battery replacement costs, the user should ensure that O&M costs do not account for augmentation/replacement. - om_cost_per_kwh::Real = 0.0 # Capacity-based O&M costs in $/kWh-rated/year. When including battery replacement costs, the user should ensure that O&M costs do not account for augmentation/replacement. + om_cost_per_kw::Real = 0.0 # Capacity-based O&M costs in \$/kW-rated/year. When including battery replacement costs, the user should ensure that O&M costs do not account for augmentation/replacement. + om_cost_per_kwh::Real = 0.0 # Capacity-based O&M costs in \$/kWh-rated/year. When including battery replacement costs, the user should ensure that O&M costs do not account for augmentation/replacement. inverter_replacement_year::Int = 10 battery_replacement_year::Int = 10 macrs_option_years::Int = 7 From 14f3ced04b5aa61f215e0a67808905993ad90bc2 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Fri, 19 Apr 2024 11:52:13 -0600 Subject: [PATCH 008/151] Update storage self-discharge input from daily to per timestep --- CHANGELOG.md | 2 +- src/constraints/storage_constraints.jl | 4 ++-- src/core/energy_storage/electric_storage.jl | 8 ++++---- src/mpc/structs.jl | 2 +- test/runtests.jl | 4 ++-- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 16ab3477f..cb49beefe 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,7 +26,7 @@ Classify the change according to the following categories: ## Develop 2024-04-19 ### Added - Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M -- Added new input **daily_self_discharge_fraction** to `ElectricStorage` for modeling battery self-discharge +- Added new input **per_timestep_self_discharge_fraction** to `ElectricStorage` for modeling battery self-discharge - Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index c7e5b493e..73ade5169 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -57,7 +57,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - m[Symbol("dvDischargeFromStorage"*_n)][b,ts] / p.s.storage.attr[b].discharge_efficiency ) - - ((p.s.storage.attr[b].daily_self_discharge_fraction/24/p.hours_per_time_step) * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) # Constraint (4h): state-of-charge for electrical storage - no grid @@ -66,7 +66,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.elec) - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] / p.s.storage.attr[b].discharge_efficiency ) - - ((p.s.storage.attr[b].daily_self_discharge_fraction/24/p.hours_per_time_step) * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) # Constraint (4i)-1: Dispatch to electrical storage is no greater than power capacity diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 64780e7cc..8e3f31d49 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -167,7 +167,7 @@ end model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - daily_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per day loss based on kWh stored in each timestep + per_timestep_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -203,7 +203,7 @@ Base.@kwdef struct ElectricStorageDefaults model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - daily_self_discharge_fraction::Float64 = 0.0 + per_timestep_self_discharge_fraction::Float64 = 0.0 end @@ -247,7 +247,7 @@ struct ElectricStorage <: AbstractElectricStorage model_degradation::Bool degradation::Degradation minimum_avg_soc_fraction::Float64 - daily_self_discharge_fraction::Float64 + per_timestep_self_discharge_fraction::Float64 function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -338,7 +338,7 @@ struct ElectricStorage <: AbstractElectricStorage s.model_degradation, degr, s.minimum_avg_soc_fraction, - s.daily_self_discharge_fraction + s.per_timestep_self_discharge_fraction ) end end diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index 6981a09c0..b8e16d244 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -242,7 +242,7 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage max_kw::Float64 = size_kw max_kwh::Float64 = size_kwh minimum_avg_soc_fraction::Float64 = 0.0 - daily_self_discharge_fraction::Float64 = 0.0 + per_timestep_self_discharge_fraction::Float64 = 0.0 end diff --git a/test/runtests.jl b/test/runtests.jl index d466a733b..e224b0785 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -552,14 +552,14 @@ else # run HiGHS tests data["ElectricStorage"]["soc_init_fraction"] = 1.0 data["ElectricStorage"]["soc_min_fraction"] = 0.0 - data["ElectricStorage"]["daily_self_discharge_fraction"] = 0.0025 + data["ElectricStorage"]["per_timestep_self_discharge_fraction"] = 0.0025/24 s = Scenario(data) inputs = REoptInputs(s) results_self_discharge = run_reopt(model, inputs) model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data["ElectricStorage"]["daily_self_discharge_fraction"] = 0.0 + data["ElectricStorage"]["per_timestep_self_discharge_fraction"] = 0.0 s = Scenario(data) inputs = REoptInputs(s) From b67bdcc6c5676f0d66b0f0ab729c750c96118700 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Fri, 19 Apr 2024 12:26:57 -0600 Subject: [PATCH 009/151] Add duration constraint --- src/constraints/storage_constraints.jl | 4 ++++ src/core/energy_storage/electric_storage.jl | 6 +++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 73ade5169..25e20aeb6 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -101,6 +101,10 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(m[Symbol("dvStorageEnergy"*_n)][b]) ) end + + if p.s.storage.attr[b].is_ldes + m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / 100 + end end function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 8e3f31d49..a1475e837 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -168,6 +168,7 @@ end degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 per_timestep_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep + is_ldes::Bool = false ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -204,6 +205,7 @@ Base.@kwdef struct ElectricStorageDefaults degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 per_timestep_self_discharge_fraction::Float64 = 0.0 + is_ldes::Bool = false end @@ -248,6 +250,7 @@ struct ElectricStorage <: AbstractElectricStorage degradation::Degradation minimum_avg_soc_fraction::Float64 per_timestep_self_discharge_fraction::Float64 + is_ldes::Bool function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -338,7 +341,8 @@ struct ElectricStorage <: AbstractElectricStorage s.model_degradation, degr, s.minimum_avg_soc_fraction, - s.per_timestep_self_discharge_fraction + s.per_timestep_self_discharge_fraction, + s.is_ldes ) end end From 7c339a1f486fe8114cbddabe6aa81998e052c099 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Sat, 20 Apr 2024 09:16:30 -0600 Subject: [PATCH 010/151] Fix ldes constraint, add duration input --- src/constraints/storage_constraints.jl | 2 +- src/core/energy_storage/electric_storage.jl | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 25e20aeb6..0b9e70689 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -103,7 +103,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") end if p.s.storage.attr[b].is_ldes - m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / 100 + @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].duration) end end diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index a1475e837..7f185fbcf 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -169,6 +169,7 @@ end minimum_avg_soc_fraction::Float64 = 0.0 per_timestep_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep is_ldes::Bool = false + duration::Real = 100.0 ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -206,6 +207,7 @@ Base.@kwdef struct ElectricStorageDefaults minimum_avg_soc_fraction::Float64 = 0.0 per_timestep_self_discharge_fraction::Float64 = 0.0 is_ldes::Bool = false + duration::Real = 100.0 end @@ -251,6 +253,7 @@ struct ElectricStorage <: AbstractElectricStorage minimum_avg_soc_fraction::Float64 per_timestep_self_discharge_fraction::Float64 is_ldes::Bool + duration::Real function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -342,7 +345,8 @@ struct ElectricStorage <: AbstractElectricStorage degr, s.minimum_avg_soc_fraction, s.per_timestep_self_discharge_fraction, - s.is_ldes + s.is_ldes, + s.duration ) end end From ac253e53a33cee8dc9158fafd4413ad351fb0374 Mon Sep 17 00:00:00 2001 From: Tucker Oddleifson Date: Sun, 28 Apr 2024 16:59:32 -0600 Subject: [PATCH 011/151] Allow battery wholesale export, preliminary implementation --- .../electric_utility_constraints.jl | 8 +++++-- src/constraints/storage_constraints.jl | 24 +++++++++++++++++-- src/core/reopt.jl | 11 +++++++++ src/results/electric_storage.jl | 6 +++++ 4 files changed, 45 insertions(+), 4 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 9fcc7ca88..209e6d5f8 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -116,8 +116,12 @@ function add_export_constraints(m, p; _n="") if typeof(binNEM) <: Real # no need for wholesale binary binWHL = 1 WHL_benefit = @expression(m, p.pwf_e * p.hours_per_time_step * - sum( sum(p.s.electric_tariff.export_rates[:WHL][ts] * m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] - for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) + #sum( sum(p.s.electric_tariff.export_rates[:WHL][ts] * m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] + # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) + ((sum(m[Symbol("dvStorageToGrid")][ts]*p.s.electric_tariff.export_rates[:WHL][ts] for ts in p.time_steps)) + + sum(sum(p.s.electric_tariff.export_rates[:WHL][ts] * m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] + for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) + ) ) else binWHL = @variable(m, binary = true) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 0b9e70689..8d9b6c02e 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -43,7 +43,8 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") #Constraint (4j): Dispatch from storage is no greater than power capacity @constraint(m, [ts in p.time_steps], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] - ) + + m[Symbol("dvStorageToGrid")][ts] # TODO: add "b" index to this decision variable + ) end @@ -55,7 +56,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") m[Symbol("dvStoredEnergy"*_n)][b, ts] == m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - - m[Symbol("dvDischargeFromStorage"*_n)][b,ts] / p.s.storage.attr[b].discharge_efficiency + - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) ) - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) @@ -79,8 +80,13 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, [ts in p.time_steps_with_grid], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + m[Symbol("dvGridToStorage"*_n)][b, ts] + + m[Symbol("dvStorageToGrid")][ts] ) + #Dispatch from electrical storage is no greater than power capacity + @constraint(m, [ts in p.time_steps_without_grid], + m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][ts]) + #Constraint (4l)-alt: Dispatch from electrical storage is no greater than power capacity (no grid connection) @constraint(m, [ts in p.time_steps_without_grid], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + @@ -105,6 +111,20 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") if p.s.storage.attr[b].is_ldes @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].duration) end + + # Prevent charging and discharging of the battery at the same time + @constraint(m, [ts in p.time_steps], m[Symbol("dvBattCharge_binary")][ts] + m[Symbol("dvBattDischarge_binary")][ts] <= 1 ) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvGridToStorage"*_n)][b, ts] + + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= + p.s.storage.attr[b].max_kw * m[Symbol("dvBattCharge_binary")][ts]) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvStorageToGrid")][ts] + + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= + p.s.storage.attr[b].max_kw * m[Symbol("dvBattDischarge_binary")][ts]) + + + end function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") diff --git a/src/core/reopt.jl b/src/core/reopt.jl index c236c8917..3b8c92b05 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -213,8 +213,16 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) @constraint(m, [ts in p.time_steps], m[:dvGridToStorage][b, ts] == 0) @constraint(m, [t in p.techs.elec, ts in p.time_steps_with_grid], m[:dvProductionToStorage][b, t, ts] == 0) + @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid")][ts] == 0) # if there isn't a battery, then the battery can't export power to the grid end else + # Additional variables for exporting storage energy to the grid + dv = "dvBattCharge_binary" + m[Symbol(dv)] = @variable(m, [0:p.time_steps[end]], base_name=dv, Bin) # Binary for battery charge + + dv = "dvBattDischarge_binary" + m[Symbol(dv)] = @variable(m, [0:p.time_steps[end]], base_name=dv, Bin) # Binary for battery discharge + add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) if b in p.s.storage.types.elec @@ -567,8 +575,11 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) dvPeakDemandMonth[p.months, 1:p.s.electric_tariff.n_monthly_demand_tiers] >= 0 # Peak electrical power demand during month m [kW] MinChargeAdder >= 0 binGHP[p.ghp_options], Bin # Can be <= 1 if require_ghp_purchase=0, and is ==1 if require_ghp_purchase=1 + dvStorageToGrid[p.time_steps] >= 0 # TODO, add: "p.StorageSalesTiers" as well? export of energy from storage to the grid + end + if !isempty(p.techs.gen) # Problem becomes a MILP @warn "Adding binary variable to model gas generator. Some solvers are very slow with integer variables." @variables m begin diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 5dfba4d09..857f9b2d0 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -49,9 +49,15 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: )) end end + # report the exported electricity from the battery: + + BattExport = (m[Symbol("dvStorageToGrid")][ts] for ts in p.time_steps) + r["storage_to_grid_series_kw"] = round.(value.(BattExport), digits = 3) + else r["soc_series_fraction"] = [] r["storage_to_load_series_kw"] = [] + r["storage_to_grid_series_kw"] = [] end d[b] = r From d66297f692775b13a904548c0d2270147c428faf Mon Sep 17 00:00:00 2001 From: Tucker Oddleifson Date: Tue, 14 May 2024 15:56:43 -0600 Subject: [PATCH 012/151] Added optional constraint for requiring start and end battery charge to be equivalent --- src/constraints/storage_constraints.jl | 4 ++++ src/core/energy_storage/electric_storage.jl | 6 +++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 8d9b6c02e..0852a941f 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -112,6 +112,10 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].duration) end + if p.s.storage.attr[b].require_start_and_end_charge_to_be_equivalent + @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + end + # Prevent charging and discharging of the battery at the same time @constraint(m, [ts in p.time_steps], m[Symbol("dvBattCharge_binary")][ts] + m[Symbol("dvBattDischarge_binary")][ts] <= 1 ) @constraint(m, [ts in p.time_steps], diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 7f185fbcf..a28f3aa8f 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -170,6 +170,7 @@ end per_timestep_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep is_ldes::Bool = false duration::Real = 100.0 + require_start_and_end_charge_to_be_equivalent::Bool = false ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -208,6 +209,7 @@ Base.@kwdef struct ElectricStorageDefaults per_timestep_self_discharge_fraction::Float64 = 0.0 is_ldes::Bool = false duration::Real = 100.0 + require_start_and_end_charge_to_be_equivalent::Bool = false end @@ -254,6 +256,7 @@ struct ElectricStorage <: AbstractElectricStorage per_timestep_self_discharge_fraction::Float64 is_ldes::Bool duration::Real + require_start_and_end_charge_to_be_equivalent::Bool function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -346,7 +349,8 @@ struct ElectricStorage <: AbstractElectricStorage s.minimum_avg_soc_fraction, s.per_timestep_self_discharge_fraction, s.is_ldes, - s.duration + s.duration, + s.require_start_and_end_charge_to_be_equivalent ) end end From cdc4da8750062e6121a7854fe49e9d31ccf82598 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Mon, 20 May 2024 14:11:49 -0600 Subject: [PATCH 013/151] Set initial SOC = final SOC for BESS and allow REopt to decide value, add outputs for PV/wind --- src/constraints/storage_constraints.jl | 12 ++++++++---- src/results/pv.jl | 2 ++ src/results/wind.jl | 1 + 3 files changed, 11 insertions(+), 4 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 0852a941f..024e14ab9 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -26,9 +26,9 @@ end function add_general_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4a): initial state of charge - @constraint(m, - m[Symbol("dvStoredEnergy"*_n)][b, 0] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] - ) + # @constraint(m, + # m[Symbol("dvStoredEnergy"*_n)][b, 0] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] + # ) #Constraint (4n): State of charge upper bound is storage system size @constraint(m, [ts in p.time_steps], @@ -113,7 +113,11 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") end if p.s.storage.attr[b].require_start_and_end_charge_to_be_equivalent - @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + print("\nSetting init SOC = final SOC\n") + @constraint(m, + m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] + ) end # Prevent charging and discharging of the battery at the same time diff --git a/src/results/pv.jl b/src/results/pv.jl index 7ce746f10..a2460d76d 100644 --- a/src/results/pv.jl +++ b/src/results/pv.jl @@ -66,6 +66,8 @@ function add_pv_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") PVPerUnitSizeOMCosts = p.om_cost_per_kw[t] * p.pwf_om * m[Symbol("dvSize"*_n)][t] r["lifecycle_om_cost_after_tax"] = round(value(PVPerUnitSizeOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) r["lcoe_per_kwh"] = calculate_lcoe(p, r, get_pv_by_name(t, p.s.pvs)) + r["year_one_om_cost_before_tax"] = round(value(PVPerUnitSizeOMCosts) / p.pwf_om, digits=0) + r["initial_capital_cost"] = value(m[Symbol("dvSize"*_n)][t]) * get_pv_by_name(t, p.s.pvs).installed_cost_per_kw d[t] = r end nothing diff --git a/src/results/wind.jl b/src/results/wind.jl index fe16dc065..492571fb3 100644 --- a/src/results/wind.jl +++ b/src/results/wind.jl @@ -66,6 +66,7 @@ function add_wind_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["annual_energy_produced_kwh"] = round(value(AvgWindProd), digits=0) r["lcoe_per_kwh"] = calculate_lcoe(p, r, p.s.wind) + r["initial_capital_cost"] = value(m[:dvSize][t]) * p.s.wind.installed_cost_per_kw d[t] = r nothing end From 0fbdb98316d6edb5ec11236e8e1d5b61bf00ea79 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 14:49:55 -0600 Subject: [PATCH 014/151] rename self discharge input for consistency --- CHANGELOG.md | 2 +- src/constraints/storage_constraints.jl | 4 ++-- src/core/energy_storage/electric_storage.jl | 8 ++++---- src/mpc/structs.jl | 2 +- test/runtests.jl | 4 ++-- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index cb49beefe..d0c6ca6d2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,7 +26,7 @@ Classify the change according to the following categories: ## Develop 2024-04-19 ### Added - Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M -- Added new input **per_timestep_self_discharge_fraction** to `ElectricStorage` for modeling battery self-discharge +- Added new input **self_discharge_fraction_per_timestep** to `ElectricStorage` for modeling battery self-discharge - Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 024e14ab9..39babd94e 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -58,7 +58,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) ) - - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) # Constraint (4h): state-of-charge for electrical storage - no grid @@ -67,7 +67,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.elec) - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] / p.s.storage.attr[b].discharge_efficiency ) - - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts]) ) # Constraint (4i)-1: Dispatch to electrical storage is no greater than power capacity diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index a28f3aa8f..697b75942 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -167,7 +167,7 @@ end model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - per_timestep_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep + self_discharge_fraction_per_timestep::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep is_ldes::Bool = false duration::Real = 100.0 require_start_and_end_charge_to_be_equivalent::Bool = false @@ -206,7 +206,7 @@ Base.@kwdef struct ElectricStorageDefaults model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - per_timestep_self_discharge_fraction::Float64 = 0.0 + self_discharge_fraction_per_timestep::Float64 = 0.0 is_ldes::Bool = false duration::Real = 100.0 require_start_and_end_charge_to_be_equivalent::Bool = false @@ -253,7 +253,7 @@ struct ElectricStorage <: AbstractElectricStorage model_degradation::Bool degradation::Degradation minimum_avg_soc_fraction::Float64 - per_timestep_self_discharge_fraction::Float64 + self_discharge_fraction_per_timestep::Float64 is_ldes::Bool duration::Real require_start_and_end_charge_to_be_equivalent::Bool @@ -347,7 +347,7 @@ struct ElectricStorage <: AbstractElectricStorage s.model_degradation, degr, s.minimum_avg_soc_fraction, - s.per_timestep_self_discharge_fraction, + s.self_discharge_fraction_per_timestep, s.is_ldes, s.duration, s.require_start_and_end_charge_to_be_equivalent diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index b8e16d244..0ec508176 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -242,7 +242,7 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage max_kw::Float64 = size_kw max_kwh::Float64 = size_kwh minimum_avg_soc_fraction::Float64 = 0.0 - per_timestep_self_discharge_fraction::Float64 = 0.0 + self_discharge_fraction_per_timestep::Float64 = 0.0 end diff --git a/test/runtests.jl b/test/runtests.jl index e224b0785..683dc3dc6 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -552,14 +552,14 @@ else # run HiGHS tests data["ElectricStorage"]["soc_init_fraction"] = 1.0 data["ElectricStorage"]["soc_min_fraction"] = 0.0 - data["ElectricStorage"]["per_timestep_self_discharge_fraction"] = 0.0025/24 + data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 s = Scenario(data) inputs = REoptInputs(s) results_self_discharge = run_reopt(model, inputs) model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data["ElectricStorage"]["per_timestep_self_discharge_fraction"] = 0.0 + data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0 s = Scenario(data) inputs = REoptInputs(s) From 58b67690d23f291cc3cb5dbac36904ffc85e7393 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 15:35:01 -0600 Subject: [PATCH 015/151] Update CHANGELOG.md --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d0c6ca6d2..06e74e7f5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,8 +26,8 @@ Classify the change according to the following categories: ## Develop 2024-04-19 ### Added - Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M -- Added new input **self_discharge_fraction_per_timestep** to `ElectricStorage` for modeling battery self-discharge - Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` +- Added new input **self_discharge_fraction_per_timestep** to `ElectricStorage` for modeling battery self-discharge - Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` ## v0.45.0 From 0e739abfceb89df4b64011dec3d633ec1c7ad267 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 15:35:10 -0600 Subject: [PATCH 016/151] simplify WHL_benefit expression --- src/constraints/electric_utility_constraints.jl | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 209e6d5f8..6038710d5 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -118,9 +118,11 @@ function add_export_constraints(m, p; _n="") WHL_benefit = @expression(m, p.pwf_e * p.hours_per_time_step * #sum( sum(p.s.electric_tariff.export_rates[:WHL][ts] * m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) - ((sum(m[Symbol("dvStorageToGrid")][ts]*p.s.electric_tariff.export_rates[:WHL][ts] for ts in p.time_steps)) + - sum(sum(p.s.electric_tariff.export_rates[:WHL][ts] * m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] - for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) + sum(p.s.electric_tariff.export_rates[:WHL][ts] * + ( + m[Symbol("dvStorageToGrid")][ts] + + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) + ) for ts in p.time_steps ) ) else From 25f310fa1447628bd456858e6381968fc760235f Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 15:56:32 -0600 Subject: [PATCH 017/151] correct self discharge term --- src/constraints/storage_constraints.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 39babd94e..f7a868816 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -58,7 +58,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) ) - - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts-1]) ) # Constraint (4h): state-of-charge for electrical storage - no grid @@ -67,7 +67,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.elec) - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] / p.s.storage.attr[b].discharge_efficiency ) - - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts-1]) ) # Constraint (4i)-1: Dispatch to electrical storage is no greater than power capacity From 4f987ac54a84669f45fe5ac2131321a62dc138ff Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 15:58:07 -0600 Subject: [PATCH 018/151] simplify soc expressions --- src/constraints/storage_constraints.jl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index f7a868816..89465c7b4 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -53,21 +53,21 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4g): state-of-charge for electrical storage - with grid @constraint(m, [ts in p.time_steps_with_grid], - m[Symbol("dvStoredEnergy"*_n)][b, ts] == m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + p.hours_per_time_step * ( + m[Symbol("dvStoredEnergy"*_n)][b, ts] == (1-p.s.storage.attr[b].self_discharge_fraction_per_timestep) * m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + + p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) ) - - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts-1]) ) # Constraint (4h): state-of-charge for electrical storage - no grid @constraint(m, [ts in p.time_steps_without_grid], - m[Symbol("dvStoredEnergy"*_n)][b, ts] == m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + p.hours_per_time_step * ( + m[Symbol("dvStoredEnergy"*_n)][b, ts] == (1-p.s.storage.attr[b].self_discharge_fraction_per_timestep) * m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + + p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.elec) - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] / p.s.storage.attr[b].discharge_efficiency ) - - (p.s.storage.attr[b].self_discharge_fraction_per_timestep * m[Symbol("dvStoredEnergy"*_n)][b, ts-1]) ) # Constraint (4i)-1: Dispatch to electrical storage is no greater than power capacity From 1f4ebe9c4d3d26b15d35bca014aa943c177249b9 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 16:29:53 -0600 Subject: [PATCH 019/151] picking nits --- src/constraints/storage_constraints.jl | 4 ++-- src/core/energy_storage/electric_storage.jl | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 89465c7b4..4d73cda4c 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -112,9 +112,9 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].duration) end - if p.s.storage.attr[b].require_start_and_end_charge_to_be_equivalent + if p.s.storage.attr[b].require_start_and_end_charge_to_be_equal # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) - print("\nSetting init SOC = final SOC\n") + print("\nSetting initial SOC = final SOC\n") @constraint(m, m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] ) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 697b75942..dbc4a5772 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -170,7 +170,7 @@ end self_discharge_fraction_per_timestep::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep is_ldes::Bool = false duration::Real = 100.0 - require_start_and_end_charge_to_be_equivalent::Bool = false + require_start_and_end_charge_to_be_equal::Bool = false ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -209,7 +209,7 @@ Base.@kwdef struct ElectricStorageDefaults self_discharge_fraction_per_timestep::Float64 = 0.0 is_ldes::Bool = false duration::Real = 100.0 - require_start_and_end_charge_to_be_equivalent::Bool = false + require_start_and_end_charge_to_be_equal::Bool = false end @@ -256,7 +256,7 @@ struct ElectricStorage <: AbstractElectricStorage self_discharge_fraction_per_timestep::Float64 is_ldes::Bool duration::Real - require_start_and_end_charge_to_be_equivalent::Bool + require_start_and_end_charge_to_be_equal::Bool function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -350,7 +350,7 @@ struct ElectricStorage <: AbstractElectricStorage s.self_discharge_fraction_per_timestep, s.is_ldes, s.duration, - s.require_start_and_end_charge_to_be_equivalent + s.require_start_and_end_charge_to_be_equal ) end end From e6f7cc49b34225b83fd8dfe6334bd50c694bc19d Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 16:46:08 -0600 Subject: [PATCH 020/151] only use 1 bin to prevent simultaneous charge/discharge --- src/constraints/storage_constraints.jl | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 4d73cda4c..91a04a102 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -121,15 +121,14 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") end # Prevent charging and discharging of the battery at the same time - @constraint(m, [ts in p.time_steps], m[Symbol("dvBattCharge_binary")][ts] + m[Symbol("dvBattDischarge_binary")][ts] <= 1 ) @constraint(m, [ts in p.time_steps], m[Symbol("dvGridToStorage"*_n)][b, ts] + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - p.s.storage.attr[b].max_kw * m[Symbol("dvBattCharge_binary")][ts]) + p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid")][ts] + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - p.s.storage.attr[b].max_kw * m[Symbol("dvBattDischarge_binary")][ts]) + p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) From 8f6ff4f73eb343f4128baec4b93207e295716769 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 16:51:04 -0600 Subject: [PATCH 021/151] change binBattCharging definition too --- src/core/reopt.jl | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 3b8c92b05..76afc1471 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -217,11 +217,8 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) end else # Additional variables for exporting storage energy to the grid - dv = "dvBattCharge_binary" - m[Symbol(dv)] = @variable(m, [0:p.time_steps[end]], base_name=dv, Bin) # Binary for battery charge - - dv = "dvBattDischarge_binary" - m[Symbol(dv)] = @variable(m, [0:p.time_steps[end]], base_name=dv, Bin) # Binary for battery discharge + dv = "binBattCharging" + m[Symbol(dv)] = @variable(m, [0:p.time_steps[end]], base_name=dv, Bin) # Binary for battery charging (vs discharging) add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) From 515a68414266c05cd5fbfa5f8328b73b5b6350ac Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 23 May 2024 16:54:38 -0600 Subject: [PATCH 022/151] dont need 0 index for binBattCharging --- src/core/reopt.jl | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 76afc1471..fe8001d72 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -217,8 +217,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) end else # Additional variables for exporting storage energy to the grid - dv = "binBattCharging" - m[Symbol(dv)] = @variable(m, [0:p.time_steps[end]], base_name=dv, Bin) # Binary for battery charging (vs discharging) + m[:binBattCharging] = @variable(m, p.time_steps], base_name=dv, Bin) # Binary for battery charging (vs discharging) add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) From fbd6d46df0e380770f4af074c8ff4d83012ab983 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 12:11:08 -0600 Subject: [PATCH 023/151] rm unnecessary loop --- src/results/electric_storage.jl | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 857f9b2d0..4dc8ee84c 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -50,9 +50,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - - BattExport = (m[Symbol("dvStorageToGrid")][ts] for ts in p.time_steps) - r["storage_to_grid_series_kw"] = round.(value.(BattExport), digits = 3) + r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")]), digits = 3) else r["soc_series_fraction"] = [] From 9954293f60448d1d9f0c3ba049dfffd965e069b2 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 12:15:55 -0600 Subject: [PATCH 024/151] rm manifest in test directory --- test/Manifest.toml | 391 --------------------------------------------- 1 file changed, 391 deletions(-) delete mode 100644 test/Manifest.toml diff --git a/test/Manifest.toml b/test/Manifest.toml deleted file mode 100644 index 436a81cf2..000000000 --- a/test/Manifest.toml +++ /dev/null @@ -1,391 +0,0 @@ -# This file is machine-generated - editing it directly is not advised - -julia_version = "1.8.0" -manifest_format = "2.0" -project_hash = "cf327925f9ed4a23852062425dc15cc7a29452ac" - -[[deps.ArgTools]] -uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" -version = "1.1.1" - 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-[[deps.TranscodingStreams]] -deps = ["Random", "Test"] -git-tree-sha1 = "1fbeaaca45801b4ba17c251dd8603ef24801dd84" -uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.10.2" - -[[deps.UUIDs]] -deps = ["Random", "SHA"] -uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" - -[[deps.Unicode]] -uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" - -[[deps.Xpress]] -deps = ["Libdl", "LinearAlgebra", "MathOptInterface", "SparseArrays"] -git-tree-sha1 = "29c47b54b6938852a598fc6761ed927aad61f10e" -uuid = "9e70acf3-d6c9-5be6-b5bd-4e2c73e3e054" -version = "0.16.2" - -[[deps.Zlib_jll]] -deps = ["Libdl"] -uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.12+3" - -[[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] -uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.1.1+0" - -[[deps.nghttp2_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.48.0+0" - -[[deps.p7zip_jll]] -deps = ["Artifacts", "Libdl"] -uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" -version = "17.4.0+0" From 019b6bc5988c87f156b33233e0f612d4b88636d8 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 12:31:46 -0600 Subject: [PATCH 025/151] fix extra ] typo --- src/core/reopt.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index fe8001d72..71215b435 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -217,7 +217,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) end else # Additional variables for exporting storage energy to the grid - m[:binBattCharging] = @variable(m, p.time_steps], base_name=dv, Bin) # Binary for battery charging (vs discharging) + m[:binBattCharging] = @variable(m, p.time_steps, base_name=dv, Bin) # Binary for battery charging (vs discharging) add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) From 371a76b720d916d70e17817c4fe600fae2ff2b8f Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 12:48:59 -0600 Subject: [PATCH 026/151] fix variable def syntax --- src/core/reopt.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 71215b435..9cc24a610 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -217,7 +217,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) end else # Additional variables for exporting storage energy to the grid - m[:binBattCharging] = @variable(m, p.time_steps, base_name=dv, Bin) # Binary for battery charging (vs discharging) + @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) From b84e917d4228a9800319dad620aa45d96d252bcd Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 13:00:32 -0600 Subject: [PATCH 027/151] make storage om test more comprehensive --- test/runtests.jl | 4436 +++++++++++++++++++++++----------------------- 1 file changed, 2219 insertions(+), 2217 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index 683dc3dc6..da950db86 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -23,502 +23,502 @@ elseif "CPLEX" in ARGS else # run HiGHS tests - @testset "Inputs" begin - @testset "hybrid profile" begin - electric_load = REopt.ElectricLoad(; - blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], - blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], - annual_kwh = 50000.0, - year = 2017, - city = "Atlanta", - latitude = 35.2468, - longitude = -91.7337 - ) - @test sum(electric_load.loads_kw) ≈ 50000.0 - end - end - - @testset "January Export Rates" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/monthly_rate.json") - - # create wholesale_rate with compensation in January > retail rate - jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - data["ElectricTariff"]["wholesale_rate"] = - append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(model, inputs) - - @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 - @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC - @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) - end - - @testset "Blended tariff" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/no_techs.json") - @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - end - - @testset "Solar and Storage" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(model, "./scenarios/pv_storage.json") - - @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 - @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 - @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - end - - @testset "Outage with Generator" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/generator.json") - @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - p = REoptInputs("./scenarios/generator.json") - simresults = simulate_outages(results, p) - @test simresults["resilience_hours_max"] == 11 - end - - # TODO test MPC with outages - @testset "MPC" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_mpc(model, "./scenarios/mpc.json") - @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - end - - @testset "MPC Multi-node" begin - # not doing much yet; just testing that two identical sites have the same costs - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - ps = MPCInputs[] - push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); - push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); - r = run_mpc(model, ps) - @test r[1]["Costs"] ≈ r[2]["Costs"] - end - - @testset "Complex Incentives" begin - """ - This test was compared against the API test: - reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives - when using the hardcoded levelization_factor in this package's REoptInputs function. - The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) - """ - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/incentives.json") - @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 - end - - @testset "Fifteen minute load" begin - d = JSON.parsefile("scenarios/no_techs.json") - d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) - d["Settings"] = Dict("time_steps_per_hour" => 4) - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, d) - @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 - end - - try - rm("Highs.log", force=true) - catch - @warn "Could not delete test/Highs.log" - end - - @testset "AVERT region abberviations" begin - """ - This test checks 5 scenarios (in order) - 1. Coordinate pair inside an AVERT polygon - 2. Coordinate pair near a US border - 3. Coordinate pair < 5 miles from US border - 4. Coordinate pair > 5 miles from US border - 5. Coordinate pair >> 5 miles from US border - """ - (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) - @test r == "AKGD" - (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) - @test r == "HIOA" - (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) - @test r == "HIMS" - (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) - @test r == "RM" - @test d ≈ 0.0 atol=1 - (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) - @test r == "NE" - @test d ≈ 7986 atol=1 - (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) - @test r === nothing - @test d ≈ 10297 atol=1 - (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) - @test r === nothing - end - - @testset "PVspecs" begin - ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) - post_name = "pv.json" - post = JSON.parsefile("./scenarios/$post_name") - scen = Scenario(post) + # @testset "Inputs" begin + # @testset "hybrid profile" begin + # electric_load = REopt.ElectricLoad(; + # blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], + # blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], + # annual_kwh = 50000.0, + # year = 2017, + # city = "Atlanta", + # latitude = 35.2468, + # longitude = -91.7337 + # ) + # @test sum(electric_load.loads_kw) ≈ 50000.0 + # end + # end + + # @testset "January Export Rates" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/monthly_rate.json") + + # # create wholesale_rate with compensation in January > retail rate + # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + # data["ElectricTariff"]["wholesale_rate"] = + # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(model, inputs) + + # @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 + # @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC + # @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) + # end + + # @testset "Blended tariff" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/no_techs.json") + # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + # end + + # @testset "Solar and Storage" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(model, "./scenarios/pv_storage.json") + + # @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + # @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 + # @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 + # @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + # end + + # @testset "Outage with Generator" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/generator.json") + # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + # p = REoptInputs("./scenarios/generator.json") + # simresults = simulate_outages(results, p) + # @test simresults["resilience_hours_max"] == 11 + # end + + # # TODO test MPC with outages + # @testset "MPC" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_mpc(model, "./scenarios/mpc.json") + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + # end + + # @testset "MPC Multi-node" begin + # # not doing much yet; just testing that two identical sites have the same costs + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # ps = MPCInputs[] + # push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); + # push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); + # r = run_mpc(model, ps) + # @test r[1]["Costs"] ≈ r[2]["Costs"] + # end + + # @testset "Complex Incentives" begin + # """ + # This test was compared against the API test: + # reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives + # when using the hardcoded levelization_factor in this package's REoptInputs function. + # The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) + # """ + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/incentives.json") + # @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 + # end + + # @testset "Fifteen minute load" begin + # d = JSON.parsefile("scenarios/no_techs.json") + # d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) + # d["Settings"] = Dict("time_steps_per_hour" => 4) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, d) + # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 + # end + + # try + # rm("Highs.log", force=true) + # catch + # @warn "Could not delete test/Highs.log" + # end + + # @testset "AVERT region abberviations" begin + # """ + # This test checks 5 scenarios (in order) + # 1. Coordinate pair inside an AVERT polygon + # 2. Coordinate pair near a US border + # 3. Coordinate pair < 5 miles from US border + # 4. Coordinate pair > 5 miles from US border + # 5. Coordinate pair >> 5 miles from US border + # """ + # (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) + # @test r == "AKGD" + # (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) + # @test r == "HIOA" + # (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) + # @test r == "HIMS" + # (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) + # @test r == "RM" + # @test d ≈ 0.0 atol=1 + # (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) + # @test r == "NE" + # @test d ≈ 7986 atol=1 + # (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) + # @test r === nothing + # @test d ≈ 10297 atol=1 + # (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) + # @test r === nothing + # end + + # @testset "PVspecs" begin + # ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) + # post_name = "pv.json" + # post = JSON.parsefile("./scenarios/$post_name") + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 20 - @test scen.pvs[1].azimuth ≈ 180 + # @test scen.pvs[1].tilt ≈ 20 + # @test scen.pvs[1].azimuth ≈ 180 - ## Scenario 2: Palmdale, CA; array-type = 1 (roof) - post["PV"]["array_type"] = 1 - scen = Scenario(post) + # ## Scenario 2: Palmdale, CA; array-type = 1 (roof) + # post["PV"]["array_type"] = 1 + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 + # @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 - ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) - post["PV"]["array_type"] = 2 - scen = Scenario(post) + # ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) + # post["PV"]["array_type"] = 2 + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 + # @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 - ## Scenario 4: Cape Town; array-type = 0 (ground) - post["Site"]["latitude"] = -33.974732 - post["Site"]["longitude"] = 19.130050 - post["PV"]["array_type"] = 0 - scen = Scenario(post) + # ## Scenario 4: Cape Town; array-type = 0 (ground) + # post["Site"]["latitude"] = -33.974732 + # post["Site"]["longitude"] = 19.130050 + # post["PV"]["array_type"] = 0 + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 20 - @test scen.pvs[1].azimuth ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - - ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) - post["PV"]["tilt"] = 17 - scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 17 + # @test scen.pvs[1].tilt ≈ 20 + # @test scen.pvs[1].azimuth ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + + # ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) + # post["PV"]["tilt"] = 17 + # scen = Scenario(post) + # @test scen.pvs[1].tilt ≈ 17 - end + # end - @testset "AlternativeFlatLoads" begin - input_data = JSON.parsefile("./scenarios/flatloads.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - - # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year - active_hours_8_5 = 8 * 5 * 52 - @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 - # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year - @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 - @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 - end + # @testset "AlternativeFlatLoads" begin + # input_data = JSON.parsefile("./scenarios/flatloads.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year + # active_hours_8_5 = 8 * 5 * 52 + # @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 + # # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year + # @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 + # @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 + # end - # removed Wind test for two reasons - # 1. reduce WindToolKit calls in tests - # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints + # # removed Wind test for two reasons + # # 1. reduce WindToolKit calls in tests + # # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints - # @testset "Minimize Unserved Load" is too slow with Cbc (killed after 8 hours) + # # @testset "Minimize Unserved Load" is too slow with Cbc (killed after 8 hours) - @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin - """ + # @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin + # """ - This tests the consistency between getting DOE commercial reference building (CRB) load data - from the simulated_load function and the processing of REoptInputs.s (Scenario struct). + # This tests the consistency between getting DOE commercial reference building (CRB) load data + # from the simulated_load function and the processing of REoptInputs.s (Scenario struct). - The simulated_load function is used for the /simulated_load endpoint in the REopt API, - in particular for the webtool/UI to display loads before running REopt, but is also generally - an external way to access CRB load data without running REopt. + # The simulated_load function is used for the /simulated_load endpoint in the REopt API, + # in particular for the webtool/UI to display loads before running REopt, but is also generally + # an external way to access CRB load data without running REopt. - One particular test specifically for the webtool/UI is for the heating load because there is just a - single heating load instead of separated space heating and domestic hot water loads. + # One particular test specifically for the webtool/UI is for the heating load because there is just a + # single heating load instead of separated space heating and domestic hot water loads. - """ - input_data = JSON.parsefile("./scenarios/simulated_load.json") + # """ + # input_data = JSON.parsefile("./scenarios/simulated_load.json") - input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), - ("blended_doe_reference_percents", [0.2, 0.8]) - ]) + # input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), + # ("blended_doe_reference_percents", [0.2, 0.8]) + # ]) - input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["LargeOffice", "FlatLoad"]), - ("blended_doe_reference_percents", [0.5, 0.5]) - ]) + # input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["LargeOffice", "FlatLoad"]), + # ("blended_doe_reference_percents", [0.5, 0.5]) + # ]) - # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu - annual_mmbtu = 10000.0 - doe_reference_name_heating = ["LargeOffice", "FlatLoad"] - percent_share_heating = [0.3, 0.7] + # # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu + # annual_mmbtu = 10000.0 + # doe_reference_name_heating = ["LargeOffice", "FlatLoad"] + # percent_share_heating = [0.3, 0.7] - d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), - ("longitude", input_data["Site"]["longitude"]), - ("load_type", "heating"), # since annual_tonhour is not given - ("doe_reference_name", doe_reference_name_heating), - ("percent_share", percent_share_heating), - ("annual_mmbtu", annual_mmbtu) - ]) + # d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), + # ("longitude", input_data["Site"]["longitude"]), + # ("load_type", "heating"), # since annual_tonhour is not given + # ("doe_reference_name", doe_reference_name_heating), + # ("percent_share", percent_share_heating), + # ("annual_mmbtu", annual_mmbtu) + # ]) - sim_load_response_heating = simulated_load(d_sim_load_heating) + # sim_load_response_heating = simulated_load(d_sim_load_heating) - input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - ("blended_doe_reference_percents", percent_share_heating), - ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) - ]) + # input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + # ("blended_doe_reference_percents", percent_share_heating), + # ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) + # ]) - input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - ("blended_doe_reference_percents", percent_share_heating), - ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) - ]) + # input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + # ("blended_doe_reference_percents", percent_share_heating), + # ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) + # ]) - s = Scenario(input_data) - inputs = REoptInputs(s) + # s = Scenario(input_data) + # inputs = REoptInputs(s) - # Call simulated_load function to check cooling - d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), - ("longitude", input_data["Site"]["longitude"]), - ("load_type", "electric"), # since annual_tonhour is not given - ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), - ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), - ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), - ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), - ]) + # # Call simulated_load function to check cooling + # d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), + # ("longitude", input_data["Site"]["longitude"]), + # ("load_type", "electric"), # since annual_tonhour is not given + # ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), + # ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), + # ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), + # ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), + # ]) - sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) - sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] - sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] + # sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) + # sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] + # sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] - total_heating_fuel_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY - @test sim_load_response_heating["loads_mmbtu_per_hour"] ≈ round.(total_heating_fuel_load_reopt_inputs, digits=3) atol=0.001 + # total_heating_fuel_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY + # @test sim_load_response_heating["loads_mmbtu_per_hour"] ≈ round.(total_heating_fuel_load_reopt_inputs, digits=3) atol=0.001 - @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 - @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 - end - - @testset "Backup Generator Reliability" begin - - @testset "Compare backup_reliability and simulate_outages" begin - # Tests ensure `backup_reliability()` consistent with `simulate_outages()` - # First, just battery - reopt_inputs = Dict( - "Site" => Dict( - "longitude" => -106.42077256104001, - "latitude" => 31.810468380036337 - ), - "ElectricStorage" => Dict( - "min_kw" => 4000, - "max_kw" => 4000, - "min_kwh" => 400000, - "max_kwh" => 400000, - "soc_min_fraction" => 0.8, - "soc_init_fraction" => 0.9 - ), - "ElectricLoad" => Dict( - "doe_reference_name" => "FlatLoad", - "annual_kwh" => 175200000.0, - "critical_load_fraction" => 0.2 - ), - "ElectricTariff" => Dict( - "urdb_label" => "5ed6c1a15457a3367add15ae" - ), - ) - p = REoptInputs(reopt_inputs) - model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, p) - simresults = simulate_outages(results, p) - - reliability_inputs = Dict( - "generator_size_kw" => 0, - "max_outage_duration" => 100, - "generator_operational_availability" => 1.0, - "generator_failure_to_start" => 0.0, - "generator_mean_time_to_failure" => 10000000000, - "fuel_limit" => 0, - "battery_size_kw" => 4000, - "battery_size_kwh" => 400000, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_operational_availability" => 1.0, - "battery_minimum_soc_fraction" => 0.0, - "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], - "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw - ) - reliability_results = backup_reliability(reliability_inputs) - - #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early - for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 - @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 - @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 - end - - # Second, gen, PV, Wind, battery - reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") - reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] - p = REoptInputs(reopt_inputs) - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, p) - simresults = simulate_outages(results, p) - reliability_inputs = Dict( - "max_outage_duration" => 48, - "generator_operational_availability" => 1.0, - "generator_failure_to_start" => 0.0, - "generator_mean_time_to_failure" => 10000000000, - "fuel_limit" => 1000000000, - "battery_operational_availability" => 1.0, - "battery_minimum_soc_fraction" => 0.0, - "pv_operational_availability" => 1.0, - "wind_operational_availability" => 1.0 - ) - reliability_results = backup_reliability(results, p, reliability_inputs) - for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 - end - end - - # Test survival with no generator decreasing and same as with generator but no fuel - reliability_inputs = Dict( - "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), - "num_generators" => 0, - "generator_size_kw" => 312.0, - "fuel_limit" => 0.0, - "max_outage_duration" => 10, - "battery_size_kw" => 428.0, - "battery_size_kwh" => 1585.0, - "num_battery_bins" => 5 - ) - reliability_results1 = backup_reliability(reliability_inputs) - reliability_inputs["generator_size_kw"] = 0 - reliability_inputs["fuel_limit"] = 1e10 - reliability_results2 = backup_reliability(reliability_inputs) - for i in 1:reliability_inputs["max_outage_duration"] - if i != 1 - @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] - @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] - end - @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] - end - - #test fuel limit - input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") - results = backup_reliability(input_dict) - @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 - @test results["cumulative_survival_final_time_step"][1] ≈ 1 - - input_dict = Dict( - "critical_loads_kw" => [1,2,2,1], - "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], - "max_outage_duration" => 3, - "num_generators" => 2, "generator_size_kw" => 1, - "generator_operational_availability" => 1, - "generator_failure_to_start" => 0.0, - "generator_mean_time_to_failure" => 5, - "battery_operational_availability" => 1, - "num_battery_bins" => 3, - "battery_size_kwh" => 4, - "battery_size_kw" => 1, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_minimum_soc_fraction" => 0.5) + # @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 + # @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 + # end + + # @testset "Backup Generator Reliability" begin + + # @testset "Compare backup_reliability and simulate_outages" begin + # # Tests ensure `backup_reliability()` consistent with `simulate_outages()` + # # First, just battery + # reopt_inputs = Dict( + # "Site" => Dict( + # "longitude" => -106.42077256104001, + # "latitude" => 31.810468380036337 + # ), + # "ElectricStorage" => Dict( + # "min_kw" => 4000, + # "max_kw" => 4000, + # "min_kwh" => 400000, + # "max_kwh" => 400000, + # "soc_min_fraction" => 0.8, + # "soc_init_fraction" => 0.9 + # ), + # "ElectricLoad" => Dict( + # "doe_reference_name" => "FlatLoad", + # "annual_kwh" => 175200000.0, + # "critical_load_fraction" => 0.2 + # ), + # "ElectricTariff" => Dict( + # "urdb_label" => "5ed6c1a15457a3367add15ae" + # ), + # ) + # p = REoptInputs(reopt_inputs) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, p) + # simresults = simulate_outages(results, p) + + # reliability_inputs = Dict( + # "generator_size_kw" => 0, + # "max_outage_duration" => 100, + # "generator_operational_availability" => 1.0, + # "generator_failure_to_start" => 0.0, + # "generator_mean_time_to_failure" => 10000000000, + # "fuel_limit" => 0, + # "battery_size_kw" => 4000, + # "battery_size_kwh" => 400000, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_operational_availability" => 1.0, + # "battery_minimum_soc_fraction" => 0.0, + # "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], + # "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw + # ) + # reliability_results = backup_reliability(reliability_inputs) + + # #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early + # for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 + # end + + # # Second, gen, PV, Wind, battery + # reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") + # reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] + # p = REoptInputs(reopt_inputs) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, p) + # simresults = simulate_outages(results, p) + # reliability_inputs = Dict( + # "max_outage_duration" => 48, + # "generator_operational_availability" => 1.0, + # "generator_failure_to_start" => 0.0, + # "generator_mean_time_to_failure" => 10000000000, + # "fuel_limit" => 1000000000, + # "battery_operational_availability" => 1.0, + # "battery_minimum_soc_fraction" => 0.0, + # "pv_operational_availability" => 1.0, + # "wind_operational_availability" => 1.0 + # ) + # reliability_results = backup_reliability(results, p, reliability_inputs) + # for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 + # end + # end + + # # Test survival with no generator decreasing and same as with generator but no fuel + # reliability_inputs = Dict( + # "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), + # "num_generators" => 0, + # "generator_size_kw" => 312.0, + # "fuel_limit" => 0.0, + # "max_outage_duration" => 10, + # "battery_size_kw" => 428.0, + # "battery_size_kwh" => 1585.0, + # "num_battery_bins" => 5 + # ) + # reliability_results1 = backup_reliability(reliability_inputs) + # reliability_inputs["generator_size_kw"] = 0 + # reliability_inputs["fuel_limit"] = 1e10 + # reliability_results2 = backup_reliability(reliability_inputs) + # for i in 1:reliability_inputs["max_outage_duration"] + # if i != 1 + # @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] + # @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] + # end + # @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] + # end + + # #test fuel limit + # input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") + # results = backup_reliability(input_dict) + # @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 + # @test results["cumulative_survival_final_time_step"][1] ≈ 1 + + # input_dict = Dict( + # "critical_loads_kw" => [1,2,2,1], + # "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], + # "max_outage_duration" => 3, + # "num_generators" => 2, "generator_size_kw" => 1, + # "generator_operational_availability" => 1, + # "generator_failure_to_start" => 0.0, + # "generator_mean_time_to_failure" => 5, + # "battery_operational_availability" => 1, + # "num_battery_bins" => 3, + # "battery_size_kwh" => 4, + # "battery_size_kw" => 1, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_minimum_soc_fraction" => 0.5) - #Given outage starts in time period 1 - #____________________________________ - #Outage hour 1: - #2 generators: Prob = 0.64, Battery = 2, Survived - #1 generator: Prob = 0.32, Battery = 1, Survived - #0 generator: Prob = 0.04, Battery = 0, Survived - #Survival Probability 1.0 - - #Outage hour 2: - #2 generators: Prob = 0.4096, Battery = 2, Survived - #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived - #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived - #0 generators: Prob = 0.1296, Battery = -1, Failed - #Survival Probability: 0.8704 - - #Outage hour 3: - #2 generators: Prob = 0.262144, Battery = 0, Survived - #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived - #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived - #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed - #0 generators Prob = 0.238144, Battery = -1, Failed - #Survival Probability: 0.557056 - @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - #Test multiple generator types - input_dict = Dict( - "critical_loads_kw" => [1,2,2,1], - "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], - "max_outage_duration" => 3, - "num_generators" => [1,1], - "generator_size_kw" => [1,1], - "generator_operational_availability" => [1,1], - "generator_failure_to_start" => [0.0, 0.0], - "generator_mean_time_to_failure" => [5, 5], - "battery_operational_availability" => 1.0, - "num_battery_bins" => 3, - "battery_size_kwh" => 2, - "battery_size_kw" => 1, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_minimum_soc_fraction" => 0) - - @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - #8760 of flat load. Battery can survive 4 hours. - #Survival after 24 hours should be chance of generator surviving 20 or more hours - input_dict = Dict( - "critical_loads_kw" => 100 .* ones(8760), - "max_outage_duration" => 24, - "num_generators" => 1, - "generator_size_kw" => 100, - "generator_operational_availability" => 0.98, - "generator_failure_to_start" => 0.1, - "generator_mean_time_to_failure" => 100, - "battery_operational_availability" => 1.0, - "num_battery_bins" => 101, - "battery_size_kwh" => 400, - "battery_size_kw" => 100, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_minimum_soc_fraction" => 0) - - reliability_results = backup_reliability(input_dict) - @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 - - #More complex case of hospital load with 2 generators, PV, wind, and battery - reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") - reliability_results = backup_reliability(reliability_inputs) - @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 + # #Given outage starts in time period 1 + # #____________________________________ + # #Outage hour 1: + # #2 generators: Prob = 0.64, Battery = 2, Survived + # #1 generator: Prob = 0.32, Battery = 1, Survived + # #0 generator: Prob = 0.04, Battery = 0, Survived + # #Survival Probability 1.0 + + # #Outage hour 2: + # #2 generators: Prob = 0.4096, Battery = 2, Survived + # #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived + # #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived + # #0 generators: Prob = 0.1296, Battery = -1, Failed + # #Survival Probability: 0.8704 + + # #Outage hour 3: + # #2 generators: Prob = 0.262144, Battery = 0, Survived + # #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived + # #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived + # #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed + # #0 generators Prob = 0.238144, Battery = -1, Failed + # #Survival Probability: 0.557056 + # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + # #Test multiple generator types + # input_dict = Dict( + # "critical_loads_kw" => [1,2,2,1], + # "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], + # "max_outage_duration" => 3, + # "num_generators" => [1,1], + # "generator_size_kw" => [1,1], + # "generator_operational_availability" => [1,1], + # "generator_failure_to_start" => [0.0, 0.0], + # "generator_mean_time_to_failure" => [5, 5], + # "battery_operational_availability" => 1.0, + # "num_battery_bins" => 3, + # "battery_size_kwh" => 2, + # "battery_size_kw" => 1, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_minimum_soc_fraction" => 0) + + # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + # #8760 of flat load. Battery can survive 4 hours. + # #Survival after 24 hours should be chance of generator surviving 20 or more hours + # input_dict = Dict( + # "critical_loads_kw" => 100 .* ones(8760), + # "max_outage_duration" => 24, + # "num_generators" => 1, + # "generator_size_kw" => 100, + # "generator_operational_availability" => 0.98, + # "generator_failure_to_start" => 0.1, + # "generator_mean_time_to_failure" => 100, + # "battery_operational_availability" => 1.0, + # "num_battery_bins" => 101, + # "battery_size_kwh" => 400, + # "battery_size_kw" => 100, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_minimum_soc_fraction" => 0) + + # reliability_results = backup_reliability(input_dict) + # @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 + + # #More complex case of hospital load with 2 generators, PV, wind, and battery + # reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") + # reliability_results = backup_reliability(reliability_inputs) + # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 - # Test gens+pv+wind+batt with 3 arg version of backup_reliability - # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json - # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: - # results = run_reopt(model, p) - # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f - # JSON.print(f, results, 4) - # end - for input_key in [ - "generator_size_kw", - "battery_size_kw", - "battery_size_kwh", - "pv_size_kw", - "wind_size_kw", - "critical_loads_kw", - "pv_production_factor_series", - "wind_production_factor_series" - ] - delete!(reliability_inputs, input_key) - end - # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) - p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") - results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") - reliability_results = backup_reliability(results, p, reliability_inputs) - - @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 - end + # # Test gens+pv+wind+batt with 3 arg version of backup_reliability + # # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json + # # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: + # # results = run_reopt(model, p) + # # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f + # # JSON.print(f, results, 4) + # # end + # for input_key in [ + # "generator_size_kw", + # "battery_size_kw", + # "battery_size_kwh", + # "pv_size_kw", + # "wind_size_kw", + # "critical_loads_kw", + # "pv_production_factor_series", + # "wind_production_factor_series" + # ] + # delete!(reliability_inputs, input_key) + # end + # # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) + # p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") + # results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") + # reliability_results = backup_reliability(results, p, reliability_inputs) + + # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 + # end @testset "Electric Storage O&M" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) @@ -531,10 +531,11 @@ else # run HiGHS tests inputs = REoptInputs(s) results = run_reopt(model, inputs) - @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=0.01 - @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=0.01 + @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 + @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 + first_run_lcc = results["Financial"]["lcc"] - data["ElectricStorage"]["om_cost_per_kw"] = 0 + data["ElectricStorage"]["om_cost_per_kw"] = 10 data["ElectricStorage"]["om_cost_per_kwh"] = 5 model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) @@ -542,1873 +543,1874 @@ else # run HiGHS tests inputs = REoptInputs(s) results = run_reopt(model, inputs) - @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=0.01 - @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=0.01 + @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 + @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 + @test results["Financial"]["lcc"] ≈ first_run_lcc + 12543 - 6272 atol=1 end - @testset "Electric Storage Self-Discharge" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/storage_om.json") + # @testset "Electric Storage Self-Discharge" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/storage_om.json") - data["ElectricStorage"]["soc_init_fraction"] = 1.0 - data["ElectricStorage"]["soc_min_fraction"] = 0.0 - data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 + # data["ElectricStorage"]["soc_init_fraction"] = 1.0 + # data["ElectricStorage"]["soc_min_fraction"] = 0.0 + # data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 - s = Scenario(data) - inputs = REoptInputs(s) - results_self_discharge = run_reopt(model, inputs) + # s = Scenario(data) + # inputs = REoptInputs(s) + # results_self_discharge = run_reopt(model, inputs) - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0 + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0 - s = Scenario(data) - inputs = REoptInputs(s) - results_no_self_discharge = run_reopt(model, inputs) + # s = Scenario(data) + # inputs = REoptInputs(s) + # results_no_self_discharge = run_reopt(model, inputs) - @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 - @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 - @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - - sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + # @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 + # @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 + # @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - + # sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 - end + # end - @testset "Imported Xpress Test Suite" begin - @testset "Heating loads and addressable load fraction" begin - # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, "./scenarios/thermal_load.json") + # @testset "Imported Xpress Test Suite" begin + # @testset "Heating loads and addressable load fraction" begin + # # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, "./scenarios/thermal_load.json") - @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 2904 + # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 2904 - # Hourly fuel load inputs with addressable_load_fraction are served as expected - data = JSON.parsefile("./scenarios/thermal_load.json") - data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 - data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 - s = Scenario(data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, inputs) - @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8) + # # Hourly fuel load inputs with addressable_load_fraction are served as expected + # data = JSON.parsefile("./scenarios/thermal_load.json") + # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 + # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + # data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 + # s = Scenario(data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, inputs) + # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8) - # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load - data = JSON.parsefile("./scenarios/thermal_load.json") - data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) - data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) - data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) - data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) + # # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load + # data = JSON.parsefile("./scenarios/thermal_load.json") + # data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) + # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) + # data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) + # data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) - s = Scenario(data) - inputs = REoptInputs(s) + # s = Scenario(data) + # inputs = REoptInputs(s) - dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency - space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency - @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) - @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) - end + # dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency + # space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency + # @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) + # @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) + # end - @testset "CHP" begin - @testset "CHP Sizing" begin - # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") - s = Scenario(data_sizing) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) + # @testset "CHP" begin + # @testset "CHP Sizing" begin + # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + # data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") + # s = Scenario(data_sizing) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) - @test round(results["CHP"]["size_kw"], digits=0) ≈ 342.0 atol=1.0 - @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 atol=1.0e7 - end + # @test round(results["CHP"]["size_kw"], digits=0) ≈ 342.0 atol=1.0 + # @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 atol=1.0e7 + # end - @testset "CHP Cost Curve and Min Allowable Size" begin - # Fixed size CHP with cost curve, no unavailability_periods - data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") - data_cost_curve["CHP"] = Dict() - data_cost_curve["CHP"]["prime_mover"] = "recip_engine" - data_cost_curve["CHP"]["size_class"] = 1 - data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 - data_cost_curve["CHP"]["min_kw"] = 0 - data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 - data_cost_curve["CHP"]["max_kw"] = 555.51 - data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 - data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] - data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] + # @testset "CHP Cost Curve and Min Allowable Size" begin + # # Fixed size CHP with cost curve, no unavailability_periods + # data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") + # data_cost_curve["CHP"] = Dict() + # data_cost_curve["CHP"]["prime_mover"] = "recip_engine" + # data_cost_curve["CHP"]["size_class"] = 1 + # data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 + # data_cost_curve["CHP"]["min_kw"] = 0 + # data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 + # data_cost_curve["CHP"]["max_kw"] = 555.51 + # data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 + # data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] + # data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] - data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 - data_cost_curve["CHP"]["macrs_option_years"] = 0 - data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 - data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 + # data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 + # data_cost_curve["CHP"]["macrs_option_years"] = 0 + # data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 + # data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 - expected_x = data_cost_curve["CHP"]["min_allowable_kw"] - cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] - cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] - slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) - init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope - lifecycle_capex_chp_expected = init_capex_chp_expected - - REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) + # expected_x = data_cost_curve["CHP"]["min_allowable_kw"] + # cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] + # cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] + # slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) + # init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope + # lifecycle_capex_chp_expected = init_capex_chp_expected - + # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + # [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) - #PV - data_cost_curve["PV"]["min_kw"] = 1500 - data_cost_curve["PV"]["max_kw"] = 1500 - data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 - data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 - data_cost_curve["PV"]["macrs_option_years"] = 0 - data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 - data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 + # #PV + # data_cost_curve["PV"]["min_kw"] = 1500 + # data_cost_curve["PV"]["max_kw"] = 1500 + # data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 + # data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 + # data_cost_curve["PV"]["macrs_option_years"] = 0 + # data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 + # data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 - init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] - lifecycle_capex_pv_expected = init_capex_pv_expected - - REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) + # init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] + # lifecycle_capex_pv_expected = init_capex_pv_expected - + # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + # [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) - s = Scenario(data_cost_curve) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) + # s = Scenario(data_cost_curve) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) - init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected - lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected + # init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected + # lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected - init_capex_total = results["Financial"]["initial_capital_costs"] - lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] + # init_capex_total = results["Financial"]["initial_capital_costs"] + # lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] - # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect - @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected - @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected + # # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect + # @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected + # @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected - # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero - @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 - end + # # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero + # @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 + # end - @testset "CHP Unavailability and Outage" begin - """ - Validation to ensure that: - 1) CHP meets load during outage without exporting - 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) - 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) - 4) CHP min_turn_down_fraction is ignored during an outage - 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage - 6) Unavailability intervals that intersect with grid-outages get ignored - 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production - """ - # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") + # @testset "CHP Unavailability and Outage" begin + # """ + # Validation to ensure that: + # 1) CHP meets load during outage without exporting + # 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) + # 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) + # 4) CHP min_turn_down_fraction is ignored during an outage + # 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage + # 6) Unavailability intervals that intersect with grid-outages get ignored + # 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production + # """ + # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + # data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") - # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period - data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), - ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), - Dict([("month", 1), ("start_week_of_month", 2), - ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] + # # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period + # data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), + # ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), + # Dict([("month", 1), ("start_week_of_month", 2), + # ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] - # Manually doing the math from the unavailability defined above - unavail_1_start = 24 + 1 - unavail_1_end = unavail_1_start + 8 - 1 - unavail_2_start = 24*3 + 9 - unavail_2_end = unavail_2_start + 8 - 1 + # # Manually doing the math from the unavailability defined above + # unavail_1_start = 24 + 1 + # unavail_1_end = unavail_1_start + 8 - 1 + # unavail_2_start = 24*3 + 9 + # unavail_2_end = unavail_2_start + 8 - 1 - # Specify the CHP.min_turn_down_fraction which is NOT used during an outage - data["CHP"]["min_turn_down_fraction"] = 0.5 - # Specify outage period; outage time_steps are 1-indexed - outage_start = unavail_1_start - data["ElectricUtility"]["outage_start_time_step"] = outage_start - outage_end = unavail_1_end - data["ElectricUtility"]["outage_end_time_step"] = outage_end - data["ElectricLoad"]["critical_load_fraction"] = 0.25 + # # Specify the CHP.min_turn_down_fraction which is NOT used during an outage + # data["CHP"]["min_turn_down_fraction"] = 0.5 + # # Specify outage period; outage time_steps are 1-indexed + # outage_start = unavail_1_start + # data["ElectricUtility"]["outage_start_time_step"] = outage_start + # outage_end = unavail_1_end + # data["ElectricUtility"]["outage_end_time_step"] = outage_end + # data["ElectricLoad"]["critical_load_fraction"] = 0.25 - s = Scenario(data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) + # s = Scenario(data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) - tot_elec_load = results["ElectricLoad"]["load_series_kw"] - chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] - chp_to_load = results["CHP"]["electric_to_load_series_kw"] - chp_export = results["CHP"]["electric_to_grid_series_kw"] - cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] + # tot_elec_load = results["ElectricLoad"]["load_series_kw"] + # chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] + # chp_to_load = results["CHP"]["electric_to_load_series_kw"] + # chp_export = results["CHP"]["electric_to_grid_series_kw"] + # cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] - # The values compared to the expected values - @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 - critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] - @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 - @test sum(chp_export) == 0.0 - @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) - @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 - @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 - end + # # The values compared to the expected values + # @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 + # critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] + # @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 + # @test sum(chp_export) == 0.0 + # @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) + # @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 + # @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 + # end - @testset "CHP Supplementary firing and standby" begin - """ - Test to ensure that supplementary firing and standby charges work as intended. The thermal and - electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a - similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower - value than that of the supplementary firing. The test also ensures that demand charges are - correctly calculated when CHP is and is not allowed to reduce demand charges. - """ - data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") - data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 - data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) - data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - #part 1: supplementary firing not used when less efficient than the boiler and expensive - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(m1, inputs) - @test results["CHP"]["size_kw"] == 800 - @test results["CHP"]["size_supplemental_firing_kw"] == 0 - @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 - @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 - @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 - @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] - @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + # @testset "CHP Supplementary firing and standby" begin + # """ + # Test to ensure that supplementary firing and standby charges work as intended. The thermal and + # electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a + # similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower + # value than that of the supplementary firing. The test also ensures that demand charges are + # correctly calculated when CHP is and is not allowed to reduce demand charges. + # """ + # data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") + # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 + # data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) + # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + # #part 1: supplementary firing not used when less efficient than the boiler and expensive + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(m1, inputs) + # @test results["CHP"]["size_kw"] == 800 + # @test results["CHP"]["size_supplemental_firing_kw"] == 0 + # @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 + # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 + # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 + # @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] + # @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + # @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP - data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 - data["CHP"]["reduces_demand_charges"] = false - data["ExistingBoiler"]["efficiency"] = 0.85 - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(m2, inputs) - @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 - @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 - @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 - end - end + # #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP + # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 + # data["CHP"]["reduces_demand_charges"] = false + # data["ExistingBoiler"]["efficiency"] = 0.85 + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(m2, inputs) + # @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 + # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 + # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 + # end + # end - @testset "FlexibleHVAC" begin + # @testset "FlexibleHVAC" begin - @testset "Single RC Model heating only" begin - #= - Single RC model: - 1 state/control node - 2 inputs: Ta and Qheat - A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] - NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here + # @testset "Single RC Model heating only" begin + # #= + # Single RC model: + # 1 state/control node + # 2 inputs: Ta and Qheat + # A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] + # NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here - We start with no technologies except ExistingBoiler and ExistingChiller. - FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) - or if there is a time-varying fuel and/or electricity cost - (and the FlexibleHVAC installed_cost is less than the achievable savings). - =# + # We start with no technologies except ExistingBoiler and ExistingChiller. + # FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) + # or if there is a time-varying fuel and/or electricity cost + # (and the FlexibleHVAC installed_cost is less than the achievable savings). + # =# - # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC - pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); - R = 0.00025 # K/kW - C = 1e5 # kJ/K - # the starting scenario has flat fuel and electricty costs - d = JSON.parsefile("./scenarios/thermal_load.json"); - A = reshape([-1/(R*C)], 1,1) - B = [1/(R*C) 1/C] - u = [tamb zeros(8760)]'; - d["FlexibleHVAC"] = Dict( - "control_node" => 1, - "initial_temperatures" => [21], - "temperature_upper_bound_degC" => 22.0, - "temperature_lower_bound_degC" => 19.8, - "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it - "system_matrix" => A, - "input_matrix" => B, - "exogenous_inputs" => u - ) + # # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC + # pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); + # R = 0.00025 # K/kW + # C = 1e5 # kJ/K + # # the starting scenario has flat fuel and electricty costs + # d = JSON.parsefile("./scenarios/thermal_load.json"); + # A = reshape([-1/(R*C)], 1,1) + # B = [1/(R*C) 1/C] + # u = [tamb zeros(8760)]'; + # d["FlexibleHVAC"] = Dict( + # "control_node" => 1, + # "initial_temperatures" => [21], + # "temperature_upper_bound_degC" => 22.0, + # "temperature_lower_bound_degC" => 19.8, + # "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it + # "system_matrix" => A, + # "input_matrix" => B, + # "exogenous_inputs" => u + # ) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # @test r["Financial"]["npv"] == 0 + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # # @test r["Financial"]["npv"] == 0 - # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical - # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC - d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # all of the savings are from the ExistingBoiler fuel costs - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true - # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical + # # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC + # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # all of the savings are from the ExistingBoiler fuel costs + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true + # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased - # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # @test r["Financial"]["npv"] == 0 + # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased + # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # # @test r["Financial"]["npv"] == 0 - # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently - d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") + # # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently + # d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + - # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - - # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - - # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] + # # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + + # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - + # # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - + # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] - # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings - # + 100 and expect that the FlexibleHVAC is not purchased - # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # @test r["Financial"]["npv"] == 0 + # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings + # # + 100 and expect that the FlexibleHVAC is not purchased + # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # # @test r["Financial"]["npv"] == 0 - end - end - - #= - add a time-of-export rate that is greater than retail rate for the month of January, - check to make sure that PV does NOT export unless the site load is met first for the month of January. - =# - @testset "Do not allow_simultaneous_export_import" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/monthly_rate.json") - - # create wholesale_rate with compensation in January > retail rate - jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - data["ElectricTariff"]["wholesale_rate"] = - append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) - - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(model, inputs) + # end + # end + + # #= + # add a time-of-export rate that is greater than retail rate for the month of January, + # check to make sure that PV does NOT export unless the site load is met first for the month of January. + # =# + # @testset "Do not allow_simultaneous_export_import" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/monthly_rate.json") + + # # create wholesale_rate with compensation in January > retail rate + # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + # data["ElectricTariff"]["wholesale_rate"] = + # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + # data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) + + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(model, inputs) - @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) - if results["PV"]["electric_to_grid_series_kw"][i] > 0) - end - - @testset "Solar and ElectricStorage w/BAU and degradation" begin - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - d = JSON.parsefile("scenarios/pv_storage.json"); - d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) - results = run_reopt([m1,m2], d) - - @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 - @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 - @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 - @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 - @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - - results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) - @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 - - # compare avg soc with and without degradation, - # using default augmentation battery maintenance strategy - avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 - d["ElectricStorage"]["model_degradation"] = true - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r_degr = run_reopt(m, d) - avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 - @test avg_soc_no_degr > avg_soc_degr - - # test the replacement strategy - d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - set_optimizer_attribute(m, "mip_rel_gap", 0.01) - r = run_reopt(m, d) - @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # #optimal SOH at end of horizon is 80\% to prevent any replacement - # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 - # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 - # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match - # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 - # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 - # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 - - # test minimum_avg_soc_fraction - d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - set_optimizer_attribute(m, "mip_rel_gap", 0.01) - r = run_reopt(m, d) - @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 - end - - @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - p = REoptInputs("./scenarios/generator.json") - results = run_reopt([m1,m2], p) - @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - @test results["ElectricLoad"]["bau_critical_load_met"] == false - @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 + # @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) + # if results["PV"]["electric_to_grid_series_kw"][i] > 0) + # end + + # @testset "Solar and ElectricStorage w/BAU and degradation" begin + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # d = JSON.parsefile("scenarios/pv_storage.json"); + # d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) + # results = run_reopt([m1,m2], d) + + # @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + # @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 + # @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 + # @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 + # @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 + # @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + # proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - + # results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) + # @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 + + # # compare avg soc with and without degradation, + # # using default augmentation battery maintenance strategy + # avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 + # d["ElectricStorage"]["model_degradation"] = true + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r_degr = run_reopt(m, d) + # avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 + # @test avg_soc_no_degr > avg_soc_degr + + # # test the replacement strategy + # d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # set_optimizer_attribute(m, "mip_rel_gap", 0.01) + # r = run_reopt(m, d) + # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # # #optimal SOH at end of horizon is 80\% to prevent any replacement + # # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 + # # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 + # # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match + # # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 + # # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 + # # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 + + # # test minimum_avg_soc_fraction + # d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # set_optimizer_attribute(m, "mip_rel_gap", 0.01) + # r = run_reopt(m, d) + # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 + # end + + # @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # p = REoptInputs("./scenarios/generator.json") + # results = run_reopt([m1,m2], p) + # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + # @test results["ElectricLoad"]["bau_critical_load_met"] == false + # @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 - simresults = simulate_outages(results, p) - @test simresults["resilience_hours_max"] == 11 - end + # simresults = simulate_outages(results, p) + # @test simresults["resilience_hours_max"] == 11 + # end - @testset "Minimize Unserved Load" begin + # @testset "Minimize Unserved Load" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outage.json") - - @test results["Outages"]["expected_outage_cost"] ≈ 0 - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 - @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 - @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - @test value(m[:binMGStorageUsed]) ≈ 1 - @test results["Financial"]["lcc"] ≈ 6.83633907986e7 rtol=0.01 - - #= - Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 - - should meet 168 kWh in each outage such that the total unserved load is 12 kWh - =# - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/outage.json") + + # @test results["Outages"]["expected_outage_cost"] ≈ 0 + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 + # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + # @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 + # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + # @test value(m[:binMGStorageUsed]) ≈ 1 + # @test results["Financial"]["lcc"] ≈ 6.83633907986e7 rtol=0.01 + + # #= + # Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 + # - should meet 168 kWh in each outage such that the total unserved load is 12 kWh + # =# + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 - # testing dvUnserved load, which would output 100 kWh for this scenario before output fix - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 - @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) - @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 - - # Scenario with generator, PV, electric storage - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") - @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 - @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 - - # Scenario with generator, PV, wind, electric storage - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") - @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 - @test results["Outages"]["expected_outage_cost"] ≈ 446899.75 atol=1.0 - @test results["Financial"]["lcc"] ≈ 6.71661825335e7 rtol=0.001 - end - - @testset "Outages with Wind and supply-to-load no greater than critical load" begin - input_data = JSON.parsefile("./scenarios/wind_outages.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt([m1,m2], inputs) + # # testing dvUnserved load, which would output 100 kWh for this scenario before output fix + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 + # @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) + # @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 + + # # Scenario with generator, PV, electric storage + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") + # @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 + # @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 + + # # Scenario with generator, PV, wind, electric storage + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") + # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + # @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 + # @test results["Outages"]["expected_outage_cost"] ≈ 446899.75 atol=1.0 + # @test results["Financial"]["lcc"] ≈ 6.71661825335e7 rtol=0.001 + # end + + # @testset "Outages with Wind and supply-to-load no greater than critical load" begin + # input_data = JSON.parsefile("./scenarios/wind_outages.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # results = run_reopt([m1,m2], inputs) - # Check that supply-to-load is equal to critical load during outages, including wind - supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] - supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] - critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] - check = .≈(supply_to_load, critical_load, atol=0.001) - @test !(0 in check) - - # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw - @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) - end - - @testset "Multiple Sites" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - ps = [ - REoptInputs("./scenarios/pv_storage.json"), - REoptInputs("./scenarios/monthly_rate.json"), - ]; - results = run_reopt(m, ps) - @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 - end - - @testset "MPC" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_mpc(model, "./scenarios/mpc.json") - @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] - # the grid draw limit in the 10th time step is set to 90 - # without the 90 limit the grid draw is 98 in the 10th time step - @test grid_draw[10] <= 90 - end - - @testset "Complex Incentives" begin - """ - This test was compared against the API test: - reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives - when using the hardcoded levelization_factor in this package's REoptInputs function. - The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) - """ - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, "./scenarios/incentives.json") - @test results["Financial"]["lcc"] ≈ 1.094596365e7 atol=5e4 - end - - @testset verbose = true "Rate Structures" begin - - @testset "Tiered Energy" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, "./scenarios/tiered_rate.json") - @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 - @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 - end - - @testset "Lookback Demand Charges" begin - # 1. Testing rate from URDB - data = JSON.parsefile("./scenarios/lookback_rate.json") - # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand - # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month - data["ElectricLoad"]["loads_kw"] = ones(8760) - data["ElectricLoad"]["loads_kw"][8] = 100.0 - inputs = REoptInputs(Scenario(data)) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, inputs) - # Expected result is 100 kW demand for January, 35% of that for all other months and - # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) - - # 2. Testing custom rate from user with demand_lookback_months - d = JSON.parsefile("./scenarios/lookback_rate.json") - d["ElectricTariff"] = Dict() - d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec - d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, REoptInputs(Scenario(d))) - - monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June - expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - # 3. Testing custom rate from user with demand_lookback_range - d = JSON.parsefile("./scenarios/lookback_rate.json") - d["ElectricTariff"] = Dict() - d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - d["ElectricTariff"]["demand_lookback_range"] = 6 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, REoptInputs(Scenario(d))) - - monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] - expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - end - - @testset "Blended tariff" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/no_techs.json") - @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - end - - @testset "Coincident Peak Charges" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/coincident_peak.json") - @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 - end - - @testset "URDB sell rate" begin - #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) - =# - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - p = REoptInputs("./scenarios/URDB_customer_generation.json") - results = run_reopt(model, p) - @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] - end - - @testset "Custom URDB with Sub-Hourly" begin - # Testing a 15-min post with a urdb_response with multiple n_energy_tiers - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) - p = REoptInputs("./scenarios/subhourly_with_urdb.json") - results = run_reopt(model, p) - @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 - @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw - end - - - # # tiered monthly demand rate TODO: expected results? - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/tiered_rate.json") - # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(m, inputs) - - # TODO test for tiered TOU demand rates - end - - @testset "EASIUR" begin - d = JSON.parsefile("./scenarios/pv.json") - d["Site"]["latitude"] = 30.2672 - d["Site"]["longitude"] = -97.7431 - scen = Scenario(d) - @test scen.financial.NOx_grid_cost_per_tonne ≈ 4534.032470 atol=0.1 - end - - @testset "Wind" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d = JSON.parsefile("./scenarios/wind.json") - results = run_reopt(m, d) - @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 - @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 - #= - 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 - - both have zero curtailment - - same energy to grid: 5,839,317 vs 5,839,322 - - same energy to load: 4,160,683 vs 4,160,677 - - same city: Boulder - - same total wind prod factor + # # Check that supply-to-load is equal to critical load during outages, including wind + # supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] + # supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] + # critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] + # check = .≈(supply_to_load, critical_load, atol=0.001) + # @test !(0 in check) + + # # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw + # @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) + # end + + # @testset "Multiple Sites" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # ps = [ + # REoptInputs("./scenarios/pv_storage.json"), + # REoptInputs("./scenarios/monthly_rate.json"), + # ]; + # results = run_reopt(m, ps) + # @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 + # end + + # @testset "MPC" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_mpc(model, "./scenarios/mpc.json") + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + # grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] + # # the grid draw limit in the 10th time step is set to 90 + # # without the 90 limit the grid draw is 98 in the 10th time step + # @test grid_draw[10] <= 90 + # end + + # @testset "Complex Incentives" begin + # """ + # This test was compared against the API test: + # reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives + # when using the hardcoded levelization_factor in this package's REoptInputs function. + # The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) + # """ + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, "./scenarios/incentives.json") + # @test results["Financial"]["lcc"] ≈ 1.094596365e7 atol=5e4 + # end + + # @testset verbose = true "Rate Structures" begin + + # @testset "Tiered Energy" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, "./scenarios/tiered_rate.json") + # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 + # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 + # end + + # @testset "Lookback Demand Charges" begin + # # 1. Testing rate from URDB + # data = JSON.parsefile("./scenarios/lookback_rate.json") + # # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand + # # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month + # data["ElectricLoad"]["loads_kw"] = ones(8760) + # data["ElectricLoad"]["loads_kw"][8] = 100.0 + # inputs = REoptInputs(Scenario(data)) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, inputs) + # # Expected result is 100 kW demand for January, 35% of that for all other months and + # # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) + + # # 2. Testing custom rate from user with demand_lookback_months + # d = JSON.parsefile("./scenarios/lookback_rate.json") + # d["ElectricTariff"] = Dict() + # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + # d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec + # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, REoptInputs(Scenario(d))) + + # monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June + # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + # # 3. Testing custom rate from user with demand_lookback_range + # d = JSON.parsefile("./scenarios/lookback_rate.json") + # d["ElectricTariff"] = Dict() + # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + # d["ElectricTariff"]["demand_lookback_range"] = 6 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, REoptInputs(Scenario(d))) + + # monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] + # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + # end + + # @testset "Blended tariff" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/no_techs.json") + # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + # end + + # @testset "Coincident Peak Charges" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/coincident_peak.json") + # @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 + # end + + # @testset "URDB sell rate" begin + # #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) + # =# + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # p = REoptInputs("./scenarios/URDB_customer_generation.json") + # results = run_reopt(model, p) + # @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] + # end + + # @testset "Custom URDB with Sub-Hourly" begin + # # Testing a 15-min post with a urdb_response with multiple n_energy_tiers + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) + # p = REoptInputs("./scenarios/subhourly_with_urdb.json") + # results = run_reopt(model, p) + # @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 + # @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw + # end + + + # # # tiered monthly demand rate TODO: expected results? + # # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # # data = JSON.parsefile("./scenarios/tiered_rate.json") + # # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" + # # s = Scenario(data) + # # inputs = REoptInputs(s) + # # results = run_reopt(m, inputs) + + # # TODO test for tiered TOU demand rates + # end + + # @testset "EASIUR" begin + # d = JSON.parsefile("./scenarios/pv.json") + # d["Site"]["latitude"] = 30.2672 + # d["Site"]["longitude"] = -97.7431 + # scen = Scenario(d) + # @test scen.financial.NOx_grid_cost_per_tonne ≈ 4534.032470 atol=0.1 + # end + + # @testset "Wind" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d = JSON.parsefile("./scenarios/wind.json") + # results = run_reopt(m, d) + # @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 + # @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 + # #= + # 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 + # - both have zero curtailment + # - same energy to grid: 5,839,317 vs 5,839,322 + # - same energy to load: 4,160,683 vs 4,160,677 + # - same city: Boulder + # - same total wind prod factor - REopt.jl has: - - bigger turbine: 3752 vs 3735 - - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 - - TODO: will these discrepancies be addressed once NMIL binaries are added? - =# - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) - results = run_reopt(m, d) - @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error - results = run_reopt(m, d) - @test "errors" ∈ keys(results["Messages"]) - @test length(results["Messages"]["errors"]) > 0 - end - - @testset "Multiple PVs" begin - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") - - ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] - roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] - roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] - - @test ground_pv["size_kw"] ≈ 15 atol=0.1 - @test roof_west["size_kw"] ≈ 7 atol=0.1 - @test roof_east["size_kw"] ≈ 4 atol=0.1 - @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 - @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 - @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 - @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 - @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 - end - - @testset "Thermal Energy Storage + Absorption Chiller" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/thermal_storage.json") - s = Scenario(data) - p = REoptInputs(s) + # REopt.jl has: + # - bigger turbine: 3752 vs 3735 + # - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 + + # TODO: will these discrepancies be addressed once NMIL binaries are added? + # =# + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) + # results = run_reopt(m, d) + # @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error + # results = run_reopt(m, d) + # @test "errors" ∈ keys(results["Messages"]) + # @test length(results["Messages"]["errors"]) > 0 + # end + + # @testset "Multiple PVs" begin + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") + + # ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] + # roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] + # roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] + + # @test ground_pv["size_kw"] ≈ 15 atol=0.1 + # @test roof_west["size_kw"] ≈ 7 atol=0.1 + # @test roof_east["size_kw"] ≈ 4 atol=0.1 + # @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + # @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + # @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 + # @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 + # @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 + # @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 + # @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 + # end + + # @testset "Thermal Energy Storage + Absorption Chiller" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/thermal_storage.json") + # s = Scenario(data) + # p = REoptInputs(s) - #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. - htf_defaults_response = get_absorption_chiller_defaults(; - thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), - boiler_type=get(data["ExistingBoiler"], "production_type", nothing), - load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - ) + # #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. + # htf_defaults_response = get_absorption_chiller_defaults(; + # thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), + # boiler_type=get(data["ExistingBoiler"], "production_type", nothing), + # load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + # ) - expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] - expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] + # expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] + # expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] - @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 + # @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + # @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + # @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 - #load test values - p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR - p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR - p.s.absorption_chiller.cop_thermal = 0.7 + # #load test values + # p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR + # p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR + # p.s.absorption_chiller.cop_thermal = 0.7 - #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period - for ts in p.time_steps - #heating and cooling loads only - if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge - p.s.electric_load.loads_kw[ts] = 10 - p.s.dhw_load.loads_kw[ts] = 5 - p.s.space_heating_load.loads_kw[ts] = 5 - p.s.cooling_load.loads_kw_thermal[ts] = 10 - p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 - for tier in 1:p.s.electric_tariff.n_energy_tiers - p.s.electric_tariff.energy_rates[ts, tier] = 100 - end - else #in odd periods, there is no load and energy is cheaper - storage should charge - p.s.electric_load.loads_kw[ts] = 0 - p.s.dhw_load.loads_kw[ts] = 0 - p.s.space_heating_load.loads_kw[ts] = 0 - p.s.cooling_load.loads_kw_thermal[ts] = 0 - p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 - for tier in 1:p.s.electric_tariff.n_energy_tiers - p.s.electric_tariff.energy_rates[ts, tier] = 50 - end - end - end + # #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period + # for ts in p.time_steps + # #heating and cooling loads only + # if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge + # p.s.electric_load.loads_kw[ts] = 10 + # p.s.dhw_load.loads_kw[ts] = 5 + # p.s.space_heating_load.loads_kw[ts] = 5 + # p.s.cooling_load.loads_kw_thermal[ts] = 10 + # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 + # for tier in 1:p.s.electric_tariff.n_energy_tiers + # p.s.electric_tariff.energy_rates[ts, tier] = 100 + # end + # else #in odd periods, there is no load and energy is cheaper - storage should charge + # p.s.electric_load.loads_kw[ts] = 0 + # p.s.dhw_load.loads_kw[ts] = 0 + # p.s.space_heating_load.loads_kw[ts] = 0 + # p.s.cooling_load.loads_kw_thermal[ts] = 0 + # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 + # for tier in 1:p.s.electric_tariff.n_energy_tiers + # p.s.electric_tariff.energy_rates[ts, tier] = 50 + # end + # end + # end - r = run_reopt(model, p) + # r = run_reopt(model, p) - #dispatch to load should be 10kW every other period = 4,380 * 10 - @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 - @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 - #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC - @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 - @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 - #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. - @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 - @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 - @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 - end - - @testset "Heat and cool energy balance" begin - """ - - This is an "energy balance" type of test which tests the model formulation/math as opposed - to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting - - Validation to ensure that: - 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage - 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load - 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly - - """ - input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) - - # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load - # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load - crb_cop = REopt.get_existing_chiller_default_cop(; - existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, - max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) - cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) - cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR - - #Test cooling load results - @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 + # #dispatch to load should be 10kW every other period = 4,380 * 10 + # @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 + # @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 + # #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC + # @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 + # @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 + # #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. + # @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 + # @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 + # @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 + # end + + # @testset "Heat and cool energy balance" begin + # """ + + # This is an "energy balance" type of test which tests the model formulation/math as opposed + # to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting + + # Validation to ensure that: + # 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage + # 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load + # 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly + + # """ + # input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) + + # # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load + # # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load + # crb_cop = REopt.get_existing_chiller_default_cop(; + # existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, + # max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) + # cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) + # cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR + + # #Test cooling load results + # @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 - # Convert fuel input to thermal using user input boiler efficiency - boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) - boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency - - # Cooling outputs - cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] - cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] - cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] - cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] - cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) - cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) - cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] - cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) - tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total - cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total - cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses - - # Absorption Chiller electric consumption addition - absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series - absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) - absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] - absorpchl_cop_elec = s.absorption_chiller.cop_electric - - # Check if sum of electric and absorption chillers equals cooling thermal total - @test tes_effic_with_decay < 0.97 - @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 - @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 - - # Heating outputs - boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] - boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] - boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] - chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] - chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] - chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] - absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) - total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) - tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) - total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) - boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency - total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows - - @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 - @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 - - # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) - absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU - absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR - absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh - - @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 - end - - @testset "Heating and cooling inputs + CHP defaults" begin - """ - - This tests the various ways to input heating and cooling loads to make sure they are processed correctly. - There are no "new" technologies in this test, so heating is served by ExistingBoiler, and - cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. - - """ - input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - - # Heating load is input as **fuel**, not thermal - # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal - expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] - total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 - total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 - # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal - input_data["ExistingBoiler"]["efficiency"] = 0.72 - s = Scenario(input_data) - inputs = REoptInputs(s) - total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 - total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 - - # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input - # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile - total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop - @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** - - #Test CHP defaults use average fuel load, size class 2 for recip_engine - @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 - @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 - - delete!(input_data, "SpaceHeatingLoad") - delete!(input_data, "DomesticHotWaterLoad") - annual_fraction_of_electric_load_input = 0.5 - input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) - - s = Scenario(input_data) - inputs = REoptInputs(s) - - expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input - total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 - @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 - - # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load - @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 - - input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) - - s = Scenario(input_data) - inputs = REoptInputs(s) - - #Test CHP defaults use average fuel load, size class changes to 3 - @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 - @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 - #Update CHP prime_mover and test new defaults - input_data["CHP"]["prime_mover"] = "combustion_turbine" - input_data["CHP"]["size_class"] = 1 - # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw - input_data["CHP"]["max_kw"] = 2500.0 - - s = Scenario(input_data) - inputs = REoptInputs(s) - - @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 - @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 - - total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 - total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 - - input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) - - s = Scenario(input_data) - inputs = REoptInputs(s) - - total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 - @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 - - # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied - input_data["SpaceHeatingLoad"] = Dict{Any, Any}() - input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() - annual_tonhour = 25000.0 - input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", - "annual_tonhour" => annual_tonhour) - input_data["ExistingChiller"] = Dict{Any, Any}() - - s = Scenario(input_data) - inputs = REoptInputs(s) - - @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 + # # Convert fuel input to thermal using user input boiler efficiency + # boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) + # boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency + + # # Cooling outputs + # cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] + # cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] + # cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] + # cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] + # cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) + # cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) + # cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] + # cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) + # tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total + # cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total + # cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses + + # # Absorption Chiller electric consumption addition + # absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series + # absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) + # absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] + # absorpchl_cop_elec = s.absorption_chiller.cop_electric + + # # Check if sum of electric and absorption chillers equals cooling thermal total + # @test tes_effic_with_decay < 0.97 + # @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 + # @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 + + # # Heating outputs + # boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + # boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] + # boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + # boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] + # chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] + # chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] + # chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] + # absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + # hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + # tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) + # total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) + # tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) + # total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) + # boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency + # total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows + + # @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 + # @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 + + # # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) + # absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU + # absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR + # absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh + + # @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 + # end + + # @testset "Heating and cooling inputs + CHP defaults" begin + # """ + + # This tests the various ways to input heating and cooling loads to make sure they are processed correctly. + # There are no "new" technologies in this test, so heating is served by ExistingBoiler, and + # cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. + + # """ + # input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # # Heating load is input as **fuel**, not thermal + # # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal + # expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] + # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 + # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 + # # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal + # input_data["ExistingBoiler"]["efficiency"] = 0.72 + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 + # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 + + # # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input + # # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile + # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop + # @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** + + # #Test CHP defaults use average fuel load, size class 2 for recip_engine + # @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 + # @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 + + # delete!(input_data, "SpaceHeatingLoad") + # delete!(input_data, "DomesticHotWaterLoad") + # annual_fraction_of_electric_load_input = 0.5 + # input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input + # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + # @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 + # @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 + + # # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load + # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 + + # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + # input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # #Test CHP defaults use average fuel load, size class changes to 3 + # @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 + # @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 + # #Update CHP prime_mover and test new defaults + # input_data["CHP"]["prime_mover"] = "combustion_turbine" + # input_data["CHP"]["size_class"] = 1 + # # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw + # input_data["CHP"]["max_kw"] = 2500.0 + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 + # @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 + + # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 + # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + # @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 + + # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + # input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 + # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 + + # # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied + # input_data["SpaceHeatingLoad"] = Dict{Any, Any}() + # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() + # annual_tonhour = 25000.0 + # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", + # "annual_tonhour" => annual_tonhour) + # input_data["ExistingChiller"] = Dict{Any, Any}() + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 - # Test for prime generator CHP inputs (electric only) - # First get CHP cost to compare later with prime generator - input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", - "annual_kwh" => 876000) - input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, - "blended_annual_demand_rate" => 0.0 ) - s_chp = Scenario(input_data) - inputs_chp = REoptInputs(s) - installed_cost_chp = s_chp.chp.installed_cost_per_kw - - # Now get prime generator (electric only) - input_data["CHP"]["is_electric_only"] = true - delete!(input_data["CHP"], "max_kw") - s = Scenario(input_data) - inputs = REoptInputs(s) - # Costs are 75% of CHP - @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 - @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 - @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 - # Thermal efficiency set to zero - @test inputs.s.chp.thermal_efficiency_full_load == 0 - @test inputs.s.chp.thermal_efficiency_half_load == 0 - # Max size based on electric load, not heating load - @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 - end - - @testset "Hybrid/blended heating and cooling loads" begin - """ - - This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names - and blended_doe_reference_percents are given and blended to create an aggregate load profile - - """ - input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") - - hospital_fraction = 0.75 - hotel_fraction = 1.0 - hospital_fraction - - # Hospital only - input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 - input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 - input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" - input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" - - s = Scenario(input_data) - inputs = REoptInputs(s) - - elec_hospital = inputs.s.electric_load.loads_kw - space_hospital = inputs.s.space_heating_load.loads_kw # thermal - dhw_hospital = inputs.s.dhw_load.loads_kw # thermal - cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal - cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital - - # Hotel only - input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 - input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 - input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" - input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" - - s = Scenario(input_data) - inputs = REoptInputs(s) - - elec_hotel = inputs.s.electric_load.loads_kw - space_hotel = inputs.s.space_heating_load.loads_kw # thermal - dhw_hotel = inputs.s.dhw_load.loads_kw # thermal - cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal - cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel - - # Hybrid mix of hospital and hotel - # Remove previous assignment of doe_reference_name - for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] - delete!(input_data[load], "doe_reference_name") - end - annual_energy = (hospital_fraction + hotel_fraction) * 100 - building_list = ["Hospital", "LargeHotel"] - percent_share_list = [hospital_fraction, hotel_fraction] - input_data["ElectricLoad"]["annual_kwh"] = annual_energy - input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list - input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list - - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy - input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list - input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy - input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list - input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list - - # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided - input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list - input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list - - s = Scenario(input_data) - inputs = REoptInputs(s) - - elec_hybrid = inputs.s.electric_load.loads_kw - space_hybrid = inputs.s.space_heating_load.loads_kw # thermal - dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal - cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal - cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric - cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid - - # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step - - @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 - @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 - @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 - # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles - cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ - cooling_elec_frac_of_total_hotel * hotel_fraction) - @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 - end - - @testset "Boiler (new) test" begin - input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], inputs) + # # Test for prime generator CHP inputs (electric only) + # # First get CHP cost to compare later with prime generator + # input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", + # "annual_kwh" => 876000) + # input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, + # "blended_annual_demand_rate" => 0.0 ) + # s_chp = Scenario(input_data) + # inputs_chp = REoptInputs(s) + # installed_cost_chp = s_chp.chp.installed_cost_per_kw + + # # Now get prime generator (electric only) + # input_data["CHP"]["is_electric_only"] = true + # delete!(input_data["CHP"], "max_kw") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # # Costs are 75% of CHP + # @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 + # @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 + # @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 + # # Thermal efficiency set to zero + # @test inputs.s.chp.thermal_efficiency_full_load == 0 + # @test inputs.s.chp.thermal_efficiency_half_load == 0 + # # Max size based on electric load, not heating load + # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 + # end + + # @testset "Hybrid/blended heating and cooling loads" begin + # """ + + # This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names + # and blended_doe_reference_percents are given and blended to create an aggregate load profile + + # """ + # input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") + + # hospital_fraction = 0.75 + # hotel_fraction = 1.0 - hospital_fraction + + # # Hospital only + # input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 + # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 + # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" + # input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # elec_hospital = inputs.s.electric_load.loads_kw + # space_hospital = inputs.s.space_heating_load.loads_kw # thermal + # dhw_hospital = inputs.s.dhw_load.loads_kw # thermal + # cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal + # cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital + + # # Hotel only + # input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 + # input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 + # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" + # input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # elec_hotel = inputs.s.electric_load.loads_kw + # space_hotel = inputs.s.space_heating_load.loads_kw # thermal + # dhw_hotel = inputs.s.dhw_load.loads_kw # thermal + # cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal + # cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel + + # # Hybrid mix of hospital and hotel + # # Remove previous assignment of doe_reference_name + # for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] + # delete!(input_data[load], "doe_reference_name") + # end + # annual_energy = (hospital_fraction + hotel_fraction) * 100 + # building_list = ["Hospital", "LargeHotel"] + # percent_share_list = [hospital_fraction, hotel_fraction] + # input_data["ElectricLoad"]["annual_kwh"] = annual_energy + # input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list + # input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list + + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy + # input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list + # input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy + # input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list + # input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list + + # # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided + # input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list + # input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # elec_hybrid = inputs.s.electric_load.loads_kw + # space_hybrid = inputs.s.space_heating_load.loads_kw # thermal + # dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal + # cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal + # cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric + # cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid + + # # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step + + # @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 + # @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 + # @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 + # # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles + # cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ + # cooling_elec_frac_of_total_hotel * hotel_fraction) + # @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 + # end + + # @testset "Boiler (new) test" begin + # input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], inputs) - # BAU boiler loads - load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU - existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) + # # BAU boiler loads + # load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU + # existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + # boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) - # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only - # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year - @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 - @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 - end - - @testset "OffGrid" begin - ## Scenario 1: Solar, Storage, Fixed Generator - post_name = "off_grid.json" - post = JSON.parsefile("./scenarios/$post_name") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, post) - scen = Scenario(post) + # # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only + # # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year + # @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 + # @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 + # end + + # @testset "OffGrid" begin + # ## Scenario 1: Solar, Storage, Fixed Generator + # post_name = "off_grid.json" + # post = JSON.parsefile("./scenarios/$post_name") + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, post) + # scen = Scenario(post) - # Test default values - @test scen.electric_utility.outage_start_time_step ≈ 1 - @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour - @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 - @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false - @test scen.generator.fuel_avail_gal ≈ 1.0e9 - @test scen.generator.min_turn_down_fraction ≈ 0.15 - @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw - @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 - - # Test outputs - @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid - @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc - @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required - @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - @test r["PV"]["size_kw"] ≈ 5050.0 - f = r["Financial"] - @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + # # Test default values + # @test scen.electric_utility.outage_start_time_step ≈ 1 + # @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour + # @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 + # @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false + # @test scen.generator.fuel_avail_gal ≈ 1.0e9 + # @test scen.generator.min_turn_down_fraction ≈ 0.15 + # @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw + # @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 + + # # Test outputs + # @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid + # @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc + # @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required + # @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + # @test r["PV"]["size_kw"] ≈ 5050.0 + # f = r["Financial"] + # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - ## Scenario 2: Fixed Generator only - post["ElectricLoad"]["annual_kwh"] = 100.0 - post["PV"]["max_kw"] = 0.0 - post["ElectricStorage"]["max_kw"] = 0.0 - post["Generator"]["min_turn_down_fraction"] = 0.0 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, post) - - # Test generator outputs - @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh - @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 - @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 - @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 - @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) - @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible - @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 - @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 - @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 - - ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement - ## This test ensures the load operating reserve requirement is being enforced - post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" - post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen - post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity - post["PV"]["max_kw"] = 0.0 - post["ElectricStorage"]["max_kw"] = 0.0 - post["Generator"]["min_turn_down_fraction"] = 0.0 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, post) - - # Test generator outputs - @test typeof(r) == Model # this is true when the model is infeasible - - ### Scenario 3: Indonesia. Wind (custom prod) and Generator only - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - post_name = "wind_intl_offgrid.json" - post = JSON.parsefile("./scenarios/$post_name") - post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] - scen = Scenario(post) - post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) - - results = run_reopt(m, post) + # ## Scenario 2: Fixed Generator only + # post["ElectricLoad"]["annual_kwh"] = 100.0 + # post["PV"]["max_kw"] = 0.0 + # post["ElectricStorage"]["max_kw"] = 0.0 + # post["Generator"]["min_turn_down_fraction"] = 0.0 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, post) + + # # Test generator outputs + # @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh + # @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 + # @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 + # @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 + # @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) + # @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible + # @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 + # @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 + # @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 + + # ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement + # ## This test ensures the load operating reserve requirement is being enforced + # post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" + # post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen + # post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity + # post["PV"]["max_kw"] = 0.0 + # post["ElectricStorage"]["max_kw"] = 0.0 + # post["Generator"]["min_turn_down_fraction"] = 0.0 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, post) + + # # Test generator outputs + # @test typeof(r) == Model # this is true when the model is infeasible + + # ### Scenario 3: Indonesia. Wind (custom prod) and Generator only + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # post_name = "wind_intl_offgrid.json" + # post = JSON.parsefile("./scenarios/$post_name") + # post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] + # scen = Scenario(post) + # post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) + + # results = run_reopt(m, post) - @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - f = results["Financial"] - @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - - windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) - loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) - @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 - - end - - @testset "GHP" begin - """ - - This tests multiple unique aspects of GHP: - 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one - 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load - 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW - 4. GHP serves all the Cooling load - 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly - 6. Hybrid GHP capability functions as expected - - """ - # Load base inputs - input_data = JSON.parsefile("scenarios/ghp_inputs.json") + # @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + # f = results["Financial"] + # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + + # windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) + # loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) + # @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 + + # end + + # @testset "GHP" begin + # """ + + # This tests multiple unique aspects of GHP: + # 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one + # 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load + # 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW + # 4. GHP serves all the Cooling load + # 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly + # 6. Hybrid GHP capability functions as expected + + # """ + # # Load base inputs + # input_data = JSON.parsefile("scenarios/ghp_inputs.json") - # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl - # Heat pump performance maps - cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) - data = cop_map_mat_header[1] - headers = cop_map_mat_header[2] - # Generate a "records" style dictionary from the - cop_map_list = [] - for i in axes(data,1) - dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) - push!(cop_map_list, dict_record) - end - input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list + # # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl + # # Heat pump performance maps + # cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) + # data = cop_map_mat_header[1] + # headers = cop_map_mat_header[2] + # # Generate a "records" style dictionary from the + # cop_map_list = [] + # for i in axes(data,1) + # dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) + # push!(cop_map_list, dict_record) + # end + # input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list - # Due to GhpGhx not being a registered package (no OSI-approved license), - # the registered REopt package cannot have GhpGhx as a "normal" dependency; - # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an - # input to REopt to avoid GhpGhx module calls - response_1 = JSON.parsefile("scenarios/ghpghx_response.json") - response_2 = deepcopy(response_1) - # Reduce the electric consumption of response 2 which should then be the chosen system - response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 - input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] + # # Due to GhpGhx not being a registered package (no OSI-approved license), + # # the registered REopt package cannot have GhpGhx as a "normal" dependency; + # # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an + # # input to REopt to avoid GhpGhx module calls + # response_1 = JSON.parsefile("scenarios/ghpghx_response.json") + # response_2 = deepcopy(response_1) + # # Reduce the electric consumption of response 2 which should then be the chosen system + # response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 + # input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] - # Heating load - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) - input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 - input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 + # # Heating load + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + # input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) + # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 + # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 - # Call REopt - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt([m1,m2], inputs) + # # Call REopt + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt([m1,m2], inputs) - ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - @test ghp_option_chosen == 2 - - # Test GHP heating and cooling load reduced - hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) - cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) - @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 - @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 - - # Test GHP serving space heating with VAV thermal efficiency improvements - heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) - expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) - @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 + # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + # @test ghp_option_chosen == 2 + + # # Test GHP heating and cooling load reduced + # hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) + # cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) + # @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 + # @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 + + # # Test GHP serving space heating with VAV thermal efficiency improvements + # heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) + # expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) + # @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 - # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit - boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) - @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 + # # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit + # boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + # expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) + # @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 - # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production - bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) - @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 + # # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production + # bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + # ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) + # @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 - # Custom heat pump COP map is used properly - ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] - cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] - # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map - @test heating_cop_avg <= 4.0 - @test cooling_cop_avg <= 8.0 - end - - @testset "Hybrid GHX and GHP calculated costs validation" begin - ## Hybrid GHP validation. - # Load base inputs - input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") - - inputs = REoptInputs(input_data) - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt([m1,m2], inputs) - - calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* - input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* - inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + - (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* - input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* - inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + - (inputs.s.ghp_option_list[1].building_sqft* - inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) - - @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 + # # Custom heat pump COP map is used properly + # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + # heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] + # cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] + # # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map + # @test heating_cop_avg <= 4.0 + # @test cooling_cop_avg <= 8.0 + # end + + # @testset "Hybrid GHX and GHP calculated costs validation" begin + # ## Hybrid GHP validation. + # # Load base inputs + # input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") + + # inputs = REoptInputs(input_data) + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt([m1,m2], inputs) + + # calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* + # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* + # inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + + # (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* + # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* + # inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + + # (inputs.s.ghp_option_list[1].building_sqft* + # inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) + + # @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 - calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* - inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om + # calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* + # inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om - @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 + # @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 - calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) - @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 + # calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) + # @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 - @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 + # @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 - @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 + # @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 - @test abs(results["Financial"]["npv"] - 840621) < 1.0 - @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 - @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 + # @test abs(results["Financial"]["npv"] - 840621) < 1.0 + # @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 + # @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 - @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") + # @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") - ## Hybrid - input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] - input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 - input_data["GHP"]["ghx_useful_life_years"] = 35 + # ## Hybrid + # input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] + # input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 + # input_data["GHP"]["ghx_useful_life_years"] = 35 - inputs = REoptInputs(input_data) + # inputs = REoptInputs(input_data) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt([m1,m2], inputs) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt([m1,m2], inputs) - pop!(input_data["GHP"], "ghpghx_inputs", nothing) - pop!(input_data["GHP"], "ghpghx_responses", nothing) - ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) + # pop!(input_data["GHP"], "ghpghx_inputs", nothing) + # pop!(input_data["GHP"], "ghpghx_responses", nothing) + # ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) - calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* - ( - (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years - )/( - (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years - ) + # calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* + # ( + # (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years + # )/( + # (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years + # ) - @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 - @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true - - # Test centralized GHP cost calculations - input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") - response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") - input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] - - s_wwhp = Scenario(input_data_wwhp) - inputs_wwhp = REoptInputs(s_wwhp) - m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results_wwhp = run_reopt(m3, inputs_wwhp) - - - heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * - input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] - - cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * - input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] - - ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] - - # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount - capex_reduction_factor = 0.455005797 - - calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) - - reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] - @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 - end - - @testset "Cambium Emissions" begin - """ - 1) Location in contiguous US - - Correct data from Cambium (returned location and values) - - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) - - co2 pct increase should be zero - 2) HI and AK locations - - Should use AVERT data and give an "info" message - - Adjust for load year vs. AVERT year - - co2 pct increase should be the default value unless user provided value - 3) International - - all emissions should be zero unless provided - """ - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 + # @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true + + # # Test centralized GHP cost calculations + # input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") + # response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") + # input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] + + # s_wwhp = Scenario(input_data_wwhp) + # inputs_wwhp = REoptInputs(s_wwhp) + # m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results_wwhp = run_reopt(m3, inputs_wwhp) + + + # heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * + # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] + + # cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * + # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] + + # ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] + + # # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount + # capex_reduction_factor = 0.455005797 + + # calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) + + # reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] + # @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 + # end + + # @testset "Cambium Emissions" begin + # """ + # 1) Location in contiguous US + # - Correct data from Cambium (returned location and values) + # - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) + # - co2 pct increase should be zero + # 2) HI and AK locations + # - Should use AVERT data and give an "info" message + # - Adjust for load year vs. AVERT year + # - co2 pct increase should be the default value unless user provided value + # 3) International + # - all emissions should be zero unless provided + # """ + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - post_name = "cambium.json" - post = JSON.parsefile("./scenarios/$post_name") + # post_name = "cambium.json" + # post = JSON.parsefile("./scenarios/$post_name") - cities = Dict( - "Denver" => (39.7413753050447, -104.99965032911328), - "Fairbanks" => (64.84053664406181, -147.71913656313163), - "Santiago" => (-33.44485437650408, -70.69031905547853) - ) + # cities = Dict( + # "Denver" => (39.7413753050447, -104.99965032911328), + # "Fairbanks" => (64.84053664406181, -147.71913656313163), + # "Santiago" => (-33.44485437650408, -70.69031905547853) + # ) - # 1) Location in contiguous US - city = "Denver" - post["Site"]["latitude"] = cities[city][1] - post["Site"]["longitude"] = cities[city][2] - post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] - post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri - scen = Scenario(post) + # # 1) Location in contiguous US + # city = "Denver" + # post["Site"]["latitude"] = cities[city][1] + # post["Site"]["longitude"] = cities[city][2] + # post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] + # post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri + # scen = Scenario(post) - @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" - @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - @test scen.electric_utility.cambium_emissions_region == "RMPAc" - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 - @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday - @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday - @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region - @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data - @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # 2) AK location - city = "Fairbanks" - post["Site"]["latitude"] = cities[city][1] - post["Site"]["longitude"] = cities[city][2] - scen = Scenario(post) + # @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" + # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + # @test scen.electric_utility.cambium_emissions_region == "RMPAc" + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 + # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday + # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday + # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region + # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data + # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # # 2) AK location + # city = "Fairbanks" + # post["Site"]["latitude"] = cities[city][1] + # post["Site"]["longitude"] = cities[city][2] + # scen = Scenario(post) - @test scen.electric_utility.avert_emissions_region == "Alaska" - @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used - @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value - @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # 3) International location - city = "Santiago" - post["Site"]["latitude"] = cities[city][1] - post["Site"]["longitude"] = cities[city][2] - scen = Scenario(post) + # @test scen.electric_utility.avert_emissions_region == "Alaska" + # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used + # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value + # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # # 3) International location + # city = "Santiago" + # post["Site"]["latitude"] = cities[city][1] + # post["Site"]["longitude"] = cities[city][2] + # scen = Scenario(post) - @test scen.electric_utility.avert_emissions_region == "" - @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 - @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 + # @test scen.electric_utility.avert_emissions_region == "" + # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 + # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 - end - - @testset "Emissions and Renewable Energy Percent" begin - #renewable energy and emissions reduction targets - include_exported_RE_in_total = [true,false,true] - include_exported_ER_in_total = [true,false,true] - RE_target = [0.8,nothing,nothing] - ER_target = [nothing,0.8,nothing] - with_outage = [true,false,false] - - for i in range(1, stop=3) - if i == 3 - inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") - else - inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") - end - if i == 1 - inputs["Site"]["latitude"] = 37.746 - inputs["Site"]["longitude"] = -122.448 - # inputs["ElectricUtility"]["emissions_region"] = "California" - end - inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] - inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] - inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end - inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] - inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] - inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] - if with_outage[i] - outage_start_hour = 4032 - outage_duration = 2000 #hrs - inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 - inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration - inputs["Generator"]["max_kw"] = 20 - inputs["Generator"]["existing_kw"] = 2 - inputs["Generator"]["fuel_avail_gal"] = 1000 - end - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt([m1, m2], inputs) - - if !isnothing(ER_target[i]) - ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] - @test ER_target[i] ≈ ER_fraction_out atol=1e-3 - lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] - lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] - ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out - ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) - @test ER_fraction_diff ≈ 0.0 atol=1e-2 - end - - annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] - yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] - yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] - yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out - @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 - if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 - end + # end + + # @testset "Emissions and Renewable Energy Percent" begin + # #renewable energy and emissions reduction targets + # include_exported_RE_in_total = [true,false,true] + # include_exported_ER_in_total = [true,false,true] + # RE_target = [0.8,nothing,nothing] + # ER_target = [nothing,0.8,nothing] + # with_outage = [true,false,false] + + # for i in range(1, stop=3) + # if i == 3 + # inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") + # else + # inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") + # end + # if i == 1 + # inputs["Site"]["latitude"] = 37.746 + # inputs["Site"]["longitude"] = -122.448 + # # inputs["ElectricUtility"]["emissions_region"] = "California" + # end + # inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] + # inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] + # inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end + # inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] + # inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] + # inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] + # if with_outage[i] + # outage_start_hour = 4032 + # outage_duration = 2000 #hrs + # inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 + # inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration + # inputs["Generator"]["max_kw"] = 20 + # inputs["Generator"]["existing_kw"] = 2 + # inputs["Generator"]["fuel_avail_gal"] = 1000 + # end + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt([m1, m2], inputs) + + # if !isnothing(ER_target[i]) + # ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] + # @test ER_target[i] ≈ ER_fraction_out atol=1e-3 + # lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] + # lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] + # ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out + # ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) + # @test ER_fraction_diff ≈ 0.0 atol=1e-2 + # end + + # annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] + # yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] + # yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] + # yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out + # @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 + # if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 + # end - if i == 1 - @test results["PV"]["size_kw"] ≈ 60.12 atol=1e-1 - @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 - @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 - @test results["Generator"]["size_kw"] ≈ 21.52 atol=1e-1 - @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 - @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.147576 atol=1e-4 - @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.58694032 atol=1e-4 - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 355.8 atol=1 - @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.64 atol=1e-2 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.0605 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8315.69 atol=1 - @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 232.85 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 141.21 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 28.186 atol=1e-1 - @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 563.72 - elseif i == 2 - #commented out values are results using same levelization factor as API - @test results["PV"]["size_kw"] ≈ 106.13 atol=1 - @test results["ElectricStorage"]["size_kw"] ≈ 21.58 atol=1 # 20.29 - @test results["ElectricStorage"]["size_kwh"] ≈ 166.29 atol=1 - @test !haskey(results, "Generator") - # Renewable energy - @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 - @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 - @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13211.78 atol=10 # 13542.62 atol=10 - @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 - # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven - @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 460.7 atol=1e-1 - @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 - @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8401.1 atol=1 - @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 - @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 + # if i == 1 + # @test results["PV"]["size_kw"] ≈ 60.12 atol=1e-1 + # @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 + # @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 + # @test results["Generator"]["size_kw"] ≈ 21.52 atol=1e-1 + # @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 + # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.147576 atol=1e-4 + # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.58694032 atol=1e-4 + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 355.8 atol=1 + # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.64 atol=1e-2 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.0605 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8315.69 atol=1 + # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 232.85 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 141.21 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 28.186 atol=1e-1 + # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 563.72 + # elseif i == 2 + # #commented out values are results using same levelization factor as API + # @test results["PV"]["size_kw"] ≈ 106.13 atol=1 + # @test results["ElectricStorage"]["size_kw"] ≈ 21.58 atol=1 # 20.29 + # @test results["ElectricStorage"]["size_kwh"] ≈ 166.29 atol=1 + # @test !haskey(results, "Generator") + # # Renewable energy + # @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 + # @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 + # @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13211.78 atol=10 # 13542.62 atol=10 + # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 + # # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven + # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 460.7 atol=1e-1 + # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 + # @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8401.1 atol=1 + # @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 + # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 - #also test CO2 breakeven cost - inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] - inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] - inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] - inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] - inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] - inputs["Settings"]["include_climate_in_objective"] = true - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt([m1, m2], inputs) - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 - elseif i == 3 - @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 - @test !haskey(results, "Wind") - @test !haskey(results, "ElectricStorage") - @test !haskey(results, "Generator") - @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 - @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 - @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 - @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 - yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] - nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) - TONNE_PER_LB = 1/2204.62 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 - @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 - @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 - @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 - @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 - @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 - KWH_PER_MMBTU = 293.07107 - annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] - annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU - @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 - end - end - end - - @testset "Back pressure steam turbine" begin - """ - Validation to ensure that: - 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application - 2) SteamTurbine serves the heating load with the condensing steam - - """ - # Setup inputs, make heating load large to entice SteamTurbine - input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - latitude = input_data["Site"]["latitude"] - longitude = input_data["Site"]["longitude"] - building = "Hospital" - elec_load_multiplier = 5.0 - heat_load_multiplier = 100.0 - input_data["ElectricLoad"]["doe_reference_name"] = building - input_data["SpaceHeatingLoad"]["doe_reference_name"] = building - input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building - elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) - input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) - space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], inputs) - - # The expected values below were directly copied from the REopt_API V2 expected values - @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 - @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 - @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 - @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 - @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 - @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 - - # BAU boiler loads - load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency - load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency - - # ExistingBoiler and SteamTurbine production - boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] - boiler_total = boiler_to_load + boiler_to_st - st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] - - # Fuel/thermal **consumption** - boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - - # Check that all thermal supply to load meets the BAU load - thermal_to_load = sum(boiler_to_load) + sum(st_to_load) - @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 - - # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet - steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] - net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) - @test net_electric_efficiency ≈ 0.052 atol=0.005 - - # Check that the max production of the boiler is still less than peak heating load times thermal factor - factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] - boiler_capacity = maximum(load_boiler_thermal) * factor - @test maximum(boiler_total) <= boiler_capacity - end - - @testset "All heating supply/demand/storage energy balance" begin - """ - Validation to ensure that: - 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included - 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption - 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False + # #also test CO2 breakeven cost + # inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + # inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + # inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] + # inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] + # inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] + # inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] + # inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] + # inputs["Settings"]["include_climate_in_objective"] = true + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt([m1, m2], inputs) + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 + # elseif i == 3 + # @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 + # @test !haskey(results, "Wind") + # @test !haskey(results, "ElectricStorage") + # @test !haskey(results, "Generator") + # @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 + # @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 + # @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 + # @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 + # yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] + # nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) + # TONNE_PER_LB = 1/2204.62 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 + # @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 + # @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 + # @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 + # @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 + # @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 + # KWH_PER_MMBTU = 293.07107 + # annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] + # annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU + # @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 + # end + # end + # end + + # @testset "Back pressure steam turbine" begin + # """ + # Validation to ensure that: + # 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application + # 2) SteamTurbine serves the heating load with the condensing steam + + # """ + # # Setup inputs, make heating load large to entice SteamTurbine + # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + # latitude = input_data["Site"]["latitude"] + # longitude = input_data["Site"]["longitude"] + # building = "Hospital" + # elec_load_multiplier = 5.0 + # heat_load_multiplier = 100.0 + # input_data["ElectricLoad"]["doe_reference_name"] = building + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = building + # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building + # elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) + # input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) + # space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + # dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], inputs) + + # # The expected values below were directly copied from the REopt_API V2 expected values + # @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 + # @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 + # @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 + # @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 + # @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 + # @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 + + # # BAU boiler loads + # load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency + # load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency + + # # ExistingBoiler and SteamTurbine production + # boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + # boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] + # boiler_total = boiler_to_load + boiler_to_st + # st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] + + # # Fuel/thermal **consumption** + # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + + # # Check that all thermal supply to load meets the BAU load + # thermal_to_load = sum(boiler_to_load) + sum(st_to_load) + # @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 + + # # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet + # steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] + # net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) + # @test net_electric_efficiency ≈ 0.052 atol=0.005 + + # # Check that the max production of the boiler is still less than peak heating load times thermal factor + # factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] + # boiler_capacity = maximum(load_boiler_thermal) * factor + # @test maximum(boiler_total) <= boiler_capacity + # end + + # @testset "All heating supply/demand/storage energy balance" begin + # """ + # Validation to ensure that: + # 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included + # 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption + # 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False - :return: - """ + # :return: + # """ - # Start with steam turbine inputs, but adding a bunch below - input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - delete!(input_data, "DomesticHotWaterLoad") + # # Start with steam turbine inputs, but adding a bunch below + # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + # # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + # delete!(input_data, "DomesticHotWaterLoad") - # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances - input_data["SteamTurbine"]["min_kw"] = 30.0 - input_data["SteamTurbine"]["max_kw"] = 30.0 + # # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances + # input_data["SteamTurbine"]["min_kw"] = 30.0 + # input_data["SteamTurbine"]["max_kw"] = 30.0 - # Add CHP - input_data["CHP"] = Dict{Any, Any}([ - ("prime_mover", "recip_engine"), - ("size_class", 4), - ("min_kw", 250.0), - ("min_allowable_kw", 0.0), - ("max_kw", 250.0), - ("can_supply_steam_turbine", false), - ("fuel_cost_per_mmbtu", 8.0), - ("cooling_thermal_factor", 1.0) - ]) + # # Add CHP + # input_data["CHP"] = Dict{Any, Any}([ + # ("prime_mover", "recip_engine"), + # ("size_class", 4), + # ("min_kw", 250.0), + # ("min_allowable_kw", 0.0), + # ("max_kw", 250.0), + # ("can_supply_steam_turbine", false), + # ("fuel_cost_per_mmbtu", 8.0), + # ("cooling_thermal_factor", 1.0) + # ]) - input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 + # input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 - # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) - input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") - input_data["AbsorptionChiller"] = Dict{Any, Any}([ - ("min_ton", 600.0), - ("max_ton", 600.0), - ("cop_thermal", 0.7), - ("installed_cost_per_ton", 500.0), - ("om_cost_per_ton", 0.5) - ]) + # # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) + # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") + # input_data["AbsorptionChiller"] = Dict{Any, Any}([ + # ("min_ton", 600.0), + # ("max_ton", 600.0), + # ("cop_thermal", 0.7), + # ("installed_cost_per_ton", 500.0), + # ("om_cost_per_ton", 0.5) + # ]) - # Add Hot TES - input_data["HotThermalStorage"] = Dict{Any, Any}([ - ("min_gal", 50000.0), - ("max_gal", 50000.0) - ]) + # # Add Hot TES + # input_data["HotThermalStorage"] = Dict{Any, Any}([ + # ("min_gal", 50000.0), + # ("max_gal", 50000.0) + # ]) - s = Scenario(input_data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) - thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] - thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech - tech_to_thermal_load = Dict{Any, Any}() - for tech in thermal_techs - tech_to_thermal_load[tech] = Dict{Any, Any}() - for load in thermal_loads - if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") - tech_to_thermal_load[tech][load] = [0.0] * 8760 - else - if load == "waste" - tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] - else - tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] - end - end - end - end - # Hot TES is the other thermal supply - hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + # thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] + # thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech + # tech_to_thermal_load = Dict{Any, Any}() + # for tech in thermal_techs + # tech_to_thermal_load[tech] = Dict{Any, Any}() + # for load in thermal_loads + # if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") + # tech_to_thermal_load[tech][load] = [0.0] * 8760 + # else + # if load == "waste" + # tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] + # else + # tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] + # end + # end + # end + # end + # # Hot TES is the other thermal supply + # hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - # BAU boiler loads - load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU - load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] + # # BAU boiler loads + # load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU + # load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] - # Fuel/thermal **consumption** - boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] - steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + # # Fuel/thermal **consumption** + # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + # chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] + # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + # absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked - alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) - thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) - @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 + # # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked + # alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) + # thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) + # @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 - # Check that all thermal to steam turbine is equal to steam turbine thermal consumption - alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) - @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 + # # Check that all thermal to steam turbine is equal to steam turbine thermal consumption + # alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) + # @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 - # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False - for tech in ["ExistingBoiler", "CHP"] - if !(tech in inputs.techs.can_supply_steam_turbine) - @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 - end - end - end - - @testset "Electric Heater" begin - d = JSON.parsefile("./scenarios/electric_heater.json") - d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - - #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased - @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 - @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 + # # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False + # for tech in ["ExistingBoiler", "CHP"] + # if !(tech in inputs.techs.can_supply_steam_turbine) + # @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 + # end + # end + # end + + # @testset "Electric Heater" begin + # d = JSON.parsefile("./scenarios/electric_heater.json") + # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + + # #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased + # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 + # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 - d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 - d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 - d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - - annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour - annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP - annual_energy_supplied = 87600 + annual_electric_heater_consumption - - #Second run: ElectricHeater produces the required heat with free electricity - @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 - @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 - @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 - - end - - @testset "Custom REopt logger" begin + # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 + # d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 + # d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + + # annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour + # annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP + # annual_energy_supplied = 87600 + annual_electric_heater_consumption + + # #Second run: ElectricHeater produces the required heat with free electricity + # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 + # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 + # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 + + # end + + # @testset "Custom REopt logger" begin - # Throw a handled error - d = JSON.parsefile("./scenarios/logger.json") - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - @test r["Messages"]["has_stacktrace"] == false - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - # Type is dict when errors, otherwise type REoptInputs - @test isa(REoptInputs(d), Dict) - - # Using filepath - n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([n1,n2], "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(n, "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging - d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" - d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - # Type is dict when errors, otherwise type REoptInputs - @test isa(REoptInputs(d), Dict) - - # Using filepath - n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([n1,n2], "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(n, "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - end - end + # # Throw a handled error + # d = JSON.parsefile("./scenarios/logger.json") + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + # @test r["Messages"]["has_stacktrace"] == false + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # # Type is dict when errors, otherwise type REoptInputs + # @test isa(REoptInputs(d), Dict) + + # # Using filepath + # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([n1,n2], "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(n, "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging + # d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" + # d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # # Type is dict when errors, otherwise type REoptInputs + # @test isa(REoptInputs(d), Dict) + + # # Using filepath + # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([n1,n2], "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(n, "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + # end + # end end From 5ce036a2bd458ca8957e99548c9cbcc5a791fdce Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 13:08:13 -0600 Subject: [PATCH 028/151] consolidate storage om and self discharge tests --- test/runtests.jl | 25 ++++++++++++++++--------- test/scenarios/storage_om.json | 4 +++- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index da950db86..af93c52df 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -520,7 +520,7 @@ else # run HiGHS tests # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 # end - @testset "Electric Storage O&M" begin + @testset "Electric Storage O&M and Self-Discharge" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) data = JSON.parsefile("./scenarios/storage_om.json") @@ -529,11 +529,10 @@ else # run HiGHS tests s = Scenario(data) inputs = REoptInputs(s) - results = run_reopt(model, inputs) + results1 = run_reopt(model, inputs) - @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 - @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 - first_run_lcc = results["Financial"]["lcc"] + @test results1["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 + @test results1["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 data["ElectricStorage"]["om_cost_per_kw"] = 10 data["ElectricStorage"]["om_cost_per_kwh"] = 5 @@ -541,11 +540,19 @@ else # run HiGHS tests model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) s = Scenario(data) inputs = REoptInputs(s) - results = run_reopt(model, inputs) + results2 = run_reopt(model, inputs) - @test results["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 - @test results["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 - @test results["Financial"]["lcc"] ≈ first_run_lcc + 12543 - 6272 atol=1 + @test results2["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 + @test results2["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 + @test results2["Financial"]["lcc"] ≈ results1["Financial"]["lcc"] + 12543 - 6272 atol=1 + + data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + s = Scenario(data) + inputs = REoptInputs(s) + results3 = run_reopt(model, inputs) + @test sum(results2["ElectricStorage"]["storage_to_load_series_kw"]) - + sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 end # @testset "Electric Storage Self-Discharge" begin diff --git a/test/scenarios/storage_om.json b/test/scenarios/storage_om.json index 167db02f9..f14380219 100644 --- a/test/scenarios/storage_om.json +++ b/test/scenarios/storage_om.json @@ -22,7 +22,9 @@ "installed_cost_per_kwh": 500.0, "total_itc_fraction": 0.0, "macrs_option_years": 0, - "macrs_bonus_fraction": 0 + "macrs_bonus_fraction": 0, + "soc_init_fraction": 1.0, + "soc_min_fraction": 0.0 }, "ElectricTariff": { "urdb_label": "5ed6c1a15457a3367add15ae" From ad46c8973f1723b9f2a42826a5fac7834c00fda1 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 13:08:56 -0600 Subject: [PATCH 029/151] more concise --- test/runtests.jl | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index af93c52df..afcf6f195 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -527,8 +527,7 @@ else # run HiGHS tests data["ElectricStorage"]["om_cost_per_kw"] = 10 data["ElectricStorage"]["om_cost_per_kwh"] = 0 - s = Scenario(data) - inputs = REoptInputs(s) + inputs = REoptInputs(data) results1 = run_reopt(model, inputs) @test results1["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 @@ -538,8 +537,7 @@ else # run HiGHS tests data["ElectricStorage"]["om_cost_per_kwh"] = 5 model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - s = Scenario(data) - inputs = REoptInputs(s) + inputs = REoptInputs(data) results2 = run_reopt(model, inputs) @test results2["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 @@ -548,8 +546,7 @@ else # run HiGHS tests data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - s = Scenario(data) - inputs = REoptInputs(s) + inputs = REoptInputs(data) results3 = run_reopt(model, inputs) @test sum(results2["ElectricStorage"]["storage_to_load_series_kw"]) - sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 From ce5977afe9e14a148ad3f2b682b043f4641696bc Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 13:09:28 -0600 Subject: [PATCH 030/151] uncomment tests --- test/runtests.jl | 4424 +++++++++++++++++++++++----------------------- 1 file changed, 2212 insertions(+), 2212 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index afcf6f195..fe61d48de 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -23,502 +23,502 @@ elseif "CPLEX" in ARGS else # run HiGHS tests - # @testset "Inputs" begin - # @testset "hybrid profile" begin - # electric_load = REopt.ElectricLoad(; - # blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], - # blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], - # annual_kwh = 50000.0, - # year = 2017, - # city = "Atlanta", - # latitude = 35.2468, - # longitude = -91.7337 - # ) - # @test sum(electric_load.loads_kw) ≈ 50000.0 - # end - # end - - # @testset "January Export Rates" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/monthly_rate.json") - - # # create wholesale_rate with compensation in January > retail rate - # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - # data["ElectricTariff"]["wholesale_rate"] = - # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(model, inputs) - - # @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 - # @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC - # @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) - # end - - # @testset "Blended tariff" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/no_techs.json") - # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - # end - - # @testset "Solar and Storage" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(model, "./scenarios/pv_storage.json") - - # @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - # @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 - # @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 - # @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - # end - - # @testset "Outage with Generator" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/generator.json") - # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - # p = REoptInputs("./scenarios/generator.json") - # simresults = simulate_outages(results, p) - # @test simresults["resilience_hours_max"] == 11 - # end - - # # TODO test MPC with outages - # @testset "MPC" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_mpc(model, "./scenarios/mpc.json") - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - # end - - # @testset "MPC Multi-node" begin - # # not doing much yet; just testing that two identical sites have the same costs - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # ps = MPCInputs[] - # push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); - # push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); - # r = run_mpc(model, ps) - # @test r[1]["Costs"] ≈ r[2]["Costs"] - # end - - # @testset "Complex Incentives" begin - # """ - # This test was compared against the API test: - # reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives - # when using the hardcoded levelization_factor in this package's REoptInputs function. - # The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) - # """ - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/incentives.json") - # @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 - # end - - # @testset "Fifteen minute load" begin - # d = JSON.parsefile("scenarios/no_techs.json") - # d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) - # d["Settings"] = Dict("time_steps_per_hour" => 4) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, d) - # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 - # end - - # try - # rm("Highs.log", force=true) - # catch - # @warn "Could not delete test/Highs.log" - # end - - # @testset "AVERT region abberviations" begin - # """ - # This test checks 5 scenarios (in order) - # 1. Coordinate pair inside an AVERT polygon - # 2. Coordinate pair near a US border - # 3. Coordinate pair < 5 miles from US border - # 4. Coordinate pair > 5 miles from US border - # 5. Coordinate pair >> 5 miles from US border - # """ - # (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) - # @test r == "AKGD" - # (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) - # @test r == "HIOA" - # (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) - # @test r == "HIMS" - # (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) - # @test r == "RM" - # @test d ≈ 0.0 atol=1 - # (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) - # @test r == "NE" - # @test d ≈ 7986 atol=1 - # (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) - # @test r === nothing - # @test d ≈ 10297 atol=1 - # (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) - # @test r === nothing - # end - - # @testset "PVspecs" begin - # ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) - # post_name = "pv.json" - # post = JSON.parsefile("./scenarios/$post_name") - # scen = Scenario(post) + @testset "Inputs" begin + @testset "hybrid profile" begin + electric_load = REopt.ElectricLoad(; + blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], + blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], + annual_kwh = 50000.0, + year = 2017, + city = "Atlanta", + latitude = 35.2468, + longitude = -91.7337 + ) + @test sum(electric_load.loads_kw) ≈ 50000.0 + end + end + + @testset "January Export Rates" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/monthly_rate.json") + + # create wholesale_rate with compensation in January > retail rate + jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + data["ElectricTariff"]["wholesale_rate"] = + append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(model, inputs) + + @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 + @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC + @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) + end + + @testset "Blended tariff" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/no_techs.json") + @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + end + + @testset "Solar and Storage" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(model, "./scenarios/pv_storage.json") + + @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 + @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 + @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + end + + @testset "Outage with Generator" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/generator.json") + @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + p = REoptInputs("./scenarios/generator.json") + simresults = simulate_outages(results, p) + @test simresults["resilience_hours_max"] == 11 + end + + # TODO test MPC with outages + @testset "MPC" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_mpc(model, "./scenarios/mpc.json") + @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + end + + @testset "MPC Multi-node" begin + # not doing much yet; just testing that two identical sites have the same costs + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + ps = MPCInputs[] + push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); + push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); + r = run_mpc(model, ps) + @test r[1]["Costs"] ≈ r[2]["Costs"] + end + + @testset "Complex Incentives" begin + """ + This test was compared against the API test: + reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives + when using the hardcoded levelization_factor in this package's REoptInputs function. + The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) + """ + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/incentives.json") + @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 + end + + @testset "Fifteen minute load" begin + d = JSON.parsefile("scenarios/no_techs.json") + d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) + d["Settings"] = Dict("time_steps_per_hour" => 4) + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, d) + @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 + end + + try + rm("Highs.log", force=true) + catch + @warn "Could not delete test/Highs.log" + end + + @testset "AVERT region abberviations" begin + """ + This test checks 5 scenarios (in order) + 1. Coordinate pair inside an AVERT polygon + 2. Coordinate pair near a US border + 3. Coordinate pair < 5 miles from US border + 4. Coordinate pair > 5 miles from US border + 5. Coordinate pair >> 5 miles from US border + """ + (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) + @test r == "AKGD" + (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) + @test r == "HIOA" + (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) + @test r == "HIMS" + (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) + @test r == "RM" + @test d ≈ 0.0 atol=1 + (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) + @test r == "NE" + @test d ≈ 7986 atol=1 + (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) + @test r === nothing + @test d ≈ 10297 atol=1 + (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) + @test r === nothing + end + + @testset "PVspecs" begin + ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) + post_name = "pv.json" + post = JSON.parsefile("./scenarios/$post_name") + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 20 - # @test scen.pvs[1].azimuth ≈ 180 + @test scen.pvs[1].tilt ≈ 20 + @test scen.pvs[1].azimuth ≈ 180 - # ## Scenario 2: Palmdale, CA; array-type = 1 (roof) - # post["PV"]["array_type"] = 1 - # scen = Scenario(post) + ## Scenario 2: Palmdale, CA; array-type = 1 (roof) + post["PV"]["array_type"] = 1 + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 + @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 - # ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) - # post["PV"]["array_type"] = 2 - # scen = Scenario(post) + ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) + post["PV"]["array_type"] = 2 + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 + @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 - # ## Scenario 4: Cape Town; array-type = 0 (ground) - # post["Site"]["latitude"] = -33.974732 - # post["Site"]["longitude"] = 19.130050 - # post["PV"]["array_type"] = 0 - # scen = Scenario(post) + ## Scenario 4: Cape Town; array-type = 0 (ground) + post["Site"]["latitude"] = -33.974732 + post["Site"]["longitude"] = 19.130050 + post["PV"]["array_type"] = 0 + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 20 - # @test scen.pvs[1].azimuth ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - - # ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) - # post["PV"]["tilt"] = 17 - # scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 17 + @test scen.pvs[1].tilt ≈ 20 + @test scen.pvs[1].azimuth ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + + ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) + post["PV"]["tilt"] = 17 + scen = Scenario(post) + @test scen.pvs[1].tilt ≈ 17 - # end + end - # @testset "AlternativeFlatLoads" begin - # input_data = JSON.parsefile("./scenarios/flatloads.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year - # active_hours_8_5 = 8 * 5 * 52 - # @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 - # # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year - # @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 - # @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 - # end + @testset "AlternativeFlatLoads" begin + input_data = JSON.parsefile("./scenarios/flatloads.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + + # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year + active_hours_8_5 = 8 * 5 * 52 + @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 + # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year + @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 + @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 + end - # # removed Wind test for two reasons - # # 1. reduce WindToolKit calls in tests - # # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints + # removed Wind test for two reasons + # 1. reduce WindToolKit calls in tests + # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints - # # @testset "Minimize Unserved Load" is too slow with Cbc (killed after 8 hours) + # @testset "Minimize Unserved Load" is too slow with Cbc (killed after 8 hours) - # @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin - # """ + @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin + """ - # This tests the consistency between getting DOE commercial reference building (CRB) load data - # from the simulated_load function and the processing of REoptInputs.s (Scenario struct). + This tests the consistency between getting DOE commercial reference building (CRB) load data + from the simulated_load function and the processing of REoptInputs.s (Scenario struct). - # The simulated_load function is used for the /simulated_load endpoint in the REopt API, - # in particular for the webtool/UI to display loads before running REopt, but is also generally - # an external way to access CRB load data without running REopt. + The simulated_load function is used for the /simulated_load endpoint in the REopt API, + in particular for the webtool/UI to display loads before running REopt, but is also generally + an external way to access CRB load data without running REopt. - # One particular test specifically for the webtool/UI is for the heating load because there is just a - # single heating load instead of separated space heating and domestic hot water loads. + One particular test specifically for the webtool/UI is for the heating load because there is just a + single heating load instead of separated space heating and domestic hot water loads. - # """ - # input_data = JSON.parsefile("./scenarios/simulated_load.json") + """ + input_data = JSON.parsefile("./scenarios/simulated_load.json") - # input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), - # ("blended_doe_reference_percents", [0.2, 0.8]) - # ]) + input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), + ("blended_doe_reference_percents", [0.2, 0.8]) + ]) - # input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["LargeOffice", "FlatLoad"]), - # ("blended_doe_reference_percents", [0.5, 0.5]) - # ]) + input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["LargeOffice", "FlatLoad"]), + ("blended_doe_reference_percents", [0.5, 0.5]) + ]) - # # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu - # annual_mmbtu = 10000.0 - # doe_reference_name_heating = ["LargeOffice", "FlatLoad"] - # percent_share_heating = [0.3, 0.7] + # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu + annual_mmbtu = 10000.0 + doe_reference_name_heating = ["LargeOffice", "FlatLoad"] + percent_share_heating = [0.3, 0.7] - # d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), - # ("longitude", input_data["Site"]["longitude"]), - # ("load_type", "heating"), # since annual_tonhour is not given - # ("doe_reference_name", doe_reference_name_heating), - # ("percent_share", percent_share_heating), - # ("annual_mmbtu", annual_mmbtu) - # ]) + d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), + ("longitude", input_data["Site"]["longitude"]), + ("load_type", "heating"), # since annual_tonhour is not given + ("doe_reference_name", doe_reference_name_heating), + ("percent_share", percent_share_heating), + ("annual_mmbtu", annual_mmbtu) + ]) - # sim_load_response_heating = simulated_load(d_sim_load_heating) + sim_load_response_heating = simulated_load(d_sim_load_heating) - # input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - # ("blended_doe_reference_percents", percent_share_heating), - # ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) - # ]) + input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + ("blended_doe_reference_percents", percent_share_heating), + ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) + ]) - # input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - # ("blended_doe_reference_percents", percent_share_heating), - # ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) - # ]) + input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + ("blended_doe_reference_percents", percent_share_heating), + ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) + ]) - # s = Scenario(input_data) - # inputs = REoptInputs(s) + s = Scenario(input_data) + inputs = REoptInputs(s) - # # Call simulated_load function to check cooling - # d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), - # ("longitude", input_data["Site"]["longitude"]), - # ("load_type", "electric"), # since annual_tonhour is not given - # ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), - # ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), - # ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), - # ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), - # ]) + # Call simulated_load function to check cooling + d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), + ("longitude", input_data["Site"]["longitude"]), + ("load_type", "electric"), # since annual_tonhour is not given + ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), + ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), + ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), + ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), + ]) - # sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) - # sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] - # sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] + sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) + sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] + sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] - # total_heating_fuel_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY - # @test sim_load_response_heating["loads_mmbtu_per_hour"] ≈ round.(total_heating_fuel_load_reopt_inputs, digits=3) atol=0.001 + total_heating_fuel_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY + @test sim_load_response_heating["loads_mmbtu_per_hour"] ≈ round.(total_heating_fuel_load_reopt_inputs, digits=3) atol=0.001 - # @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 - # @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 - # end - - # @testset "Backup Generator Reliability" begin - - # @testset "Compare backup_reliability and simulate_outages" begin - # # Tests ensure `backup_reliability()` consistent with `simulate_outages()` - # # First, just battery - # reopt_inputs = Dict( - # "Site" => Dict( - # "longitude" => -106.42077256104001, - # "latitude" => 31.810468380036337 - # ), - # "ElectricStorage" => Dict( - # "min_kw" => 4000, - # "max_kw" => 4000, - # "min_kwh" => 400000, - # "max_kwh" => 400000, - # "soc_min_fraction" => 0.8, - # "soc_init_fraction" => 0.9 - # ), - # "ElectricLoad" => Dict( - # "doe_reference_name" => "FlatLoad", - # "annual_kwh" => 175200000.0, - # "critical_load_fraction" => 0.2 - # ), - # "ElectricTariff" => Dict( - # "urdb_label" => "5ed6c1a15457a3367add15ae" - # ), - # ) - # p = REoptInputs(reopt_inputs) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, p) - # simresults = simulate_outages(results, p) - - # reliability_inputs = Dict( - # "generator_size_kw" => 0, - # "max_outage_duration" => 100, - # "generator_operational_availability" => 1.0, - # "generator_failure_to_start" => 0.0, - # "generator_mean_time_to_failure" => 10000000000, - # "fuel_limit" => 0, - # "battery_size_kw" => 4000, - # "battery_size_kwh" => 400000, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_operational_availability" => 1.0, - # "battery_minimum_soc_fraction" => 0.0, - # "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], - # "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw - # ) - # reliability_results = backup_reliability(reliability_inputs) - - # #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early - # for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 - # end - - # # Second, gen, PV, Wind, battery - # reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") - # reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] - # p = REoptInputs(reopt_inputs) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, p) - # simresults = simulate_outages(results, p) - # reliability_inputs = Dict( - # "max_outage_duration" => 48, - # "generator_operational_availability" => 1.0, - # "generator_failure_to_start" => 0.0, - # "generator_mean_time_to_failure" => 10000000000, - # "fuel_limit" => 1000000000, - # "battery_operational_availability" => 1.0, - # "battery_minimum_soc_fraction" => 0.0, - # "pv_operational_availability" => 1.0, - # "wind_operational_availability" => 1.0 - # ) - # reliability_results = backup_reliability(results, p, reliability_inputs) - # for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 - # end - # end - - # # Test survival with no generator decreasing and same as with generator but no fuel - # reliability_inputs = Dict( - # "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), - # "num_generators" => 0, - # "generator_size_kw" => 312.0, - # "fuel_limit" => 0.0, - # "max_outage_duration" => 10, - # "battery_size_kw" => 428.0, - # "battery_size_kwh" => 1585.0, - # "num_battery_bins" => 5 - # ) - # reliability_results1 = backup_reliability(reliability_inputs) - # reliability_inputs["generator_size_kw"] = 0 - # reliability_inputs["fuel_limit"] = 1e10 - # reliability_results2 = backup_reliability(reliability_inputs) - # for i in 1:reliability_inputs["max_outage_duration"] - # if i != 1 - # @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] - # @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] - # end - # @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] - # end - - # #test fuel limit - # input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") - # results = backup_reliability(input_dict) - # @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 - # @test results["cumulative_survival_final_time_step"][1] ≈ 1 - - # input_dict = Dict( - # "critical_loads_kw" => [1,2,2,1], - # "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], - # "max_outage_duration" => 3, - # "num_generators" => 2, "generator_size_kw" => 1, - # "generator_operational_availability" => 1, - # "generator_failure_to_start" => 0.0, - # "generator_mean_time_to_failure" => 5, - # "battery_operational_availability" => 1, - # "num_battery_bins" => 3, - # "battery_size_kwh" => 4, - # "battery_size_kw" => 1, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_minimum_soc_fraction" => 0.5) + @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 + @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 + end + + @testset "Backup Generator Reliability" begin + + @testset "Compare backup_reliability and simulate_outages" begin + # Tests ensure `backup_reliability()` consistent with `simulate_outages()` + # First, just battery + reopt_inputs = Dict( + "Site" => Dict( + "longitude" => -106.42077256104001, + "latitude" => 31.810468380036337 + ), + "ElectricStorage" => Dict( + "min_kw" => 4000, + "max_kw" => 4000, + "min_kwh" => 400000, + "max_kwh" => 400000, + "soc_min_fraction" => 0.8, + "soc_init_fraction" => 0.9 + ), + "ElectricLoad" => Dict( + "doe_reference_name" => "FlatLoad", + "annual_kwh" => 175200000.0, + "critical_load_fraction" => 0.2 + ), + "ElectricTariff" => Dict( + "urdb_label" => "5ed6c1a15457a3367add15ae" + ), + ) + p = REoptInputs(reopt_inputs) + model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, p) + simresults = simulate_outages(results, p) + + reliability_inputs = Dict( + "generator_size_kw" => 0, + "max_outage_duration" => 100, + "generator_operational_availability" => 1.0, + "generator_failure_to_start" => 0.0, + "generator_mean_time_to_failure" => 10000000000, + "fuel_limit" => 0, + "battery_size_kw" => 4000, + "battery_size_kwh" => 400000, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_operational_availability" => 1.0, + "battery_minimum_soc_fraction" => 0.0, + "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], + "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw + ) + reliability_results = backup_reliability(reliability_inputs) + + #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early + for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 + @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 + @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 + end + + # Second, gen, PV, Wind, battery + reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") + reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] + p = REoptInputs(reopt_inputs) + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, p) + simresults = simulate_outages(results, p) + reliability_inputs = Dict( + "max_outage_duration" => 48, + "generator_operational_availability" => 1.0, + "generator_failure_to_start" => 0.0, + "generator_mean_time_to_failure" => 10000000000, + "fuel_limit" => 1000000000, + "battery_operational_availability" => 1.0, + "battery_minimum_soc_fraction" => 0.0, + "pv_operational_availability" => 1.0, + "wind_operational_availability" => 1.0 + ) + reliability_results = backup_reliability(results, p, reliability_inputs) + for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 + end + end + + # Test survival with no generator decreasing and same as with generator but no fuel + reliability_inputs = Dict( + "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), + "num_generators" => 0, + "generator_size_kw" => 312.0, + "fuel_limit" => 0.0, + "max_outage_duration" => 10, + "battery_size_kw" => 428.0, + "battery_size_kwh" => 1585.0, + "num_battery_bins" => 5 + ) + reliability_results1 = backup_reliability(reliability_inputs) + reliability_inputs["generator_size_kw"] = 0 + reliability_inputs["fuel_limit"] = 1e10 + reliability_results2 = backup_reliability(reliability_inputs) + for i in 1:reliability_inputs["max_outage_duration"] + if i != 1 + @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] + @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] + end + @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] + end + + #test fuel limit + input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") + results = backup_reliability(input_dict) + @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 + @test results["cumulative_survival_final_time_step"][1] ≈ 1 + + input_dict = Dict( + "critical_loads_kw" => [1,2,2,1], + "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], + "max_outage_duration" => 3, + "num_generators" => 2, "generator_size_kw" => 1, + "generator_operational_availability" => 1, + "generator_failure_to_start" => 0.0, + "generator_mean_time_to_failure" => 5, + "battery_operational_availability" => 1, + "num_battery_bins" => 3, + "battery_size_kwh" => 4, + "battery_size_kw" => 1, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_minimum_soc_fraction" => 0.5) - # #Given outage starts in time period 1 - # #____________________________________ - # #Outage hour 1: - # #2 generators: Prob = 0.64, Battery = 2, Survived - # #1 generator: Prob = 0.32, Battery = 1, Survived - # #0 generator: Prob = 0.04, Battery = 0, Survived - # #Survival Probability 1.0 - - # #Outage hour 2: - # #2 generators: Prob = 0.4096, Battery = 2, Survived - # #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived - # #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived - # #0 generators: Prob = 0.1296, Battery = -1, Failed - # #Survival Probability: 0.8704 - - # #Outage hour 3: - # #2 generators: Prob = 0.262144, Battery = 0, Survived - # #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived - # #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived - # #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed - # #0 generators Prob = 0.238144, Battery = -1, Failed - # #Survival Probability: 0.557056 - # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - # #Test multiple generator types - # input_dict = Dict( - # "critical_loads_kw" => [1,2,2,1], - # "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], - # "max_outage_duration" => 3, - # "num_generators" => [1,1], - # "generator_size_kw" => [1,1], - # "generator_operational_availability" => [1,1], - # "generator_failure_to_start" => [0.0, 0.0], - # "generator_mean_time_to_failure" => [5, 5], - # "battery_operational_availability" => 1.0, - # "num_battery_bins" => 3, - # "battery_size_kwh" => 2, - # "battery_size_kw" => 1, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_minimum_soc_fraction" => 0) - - # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - # #8760 of flat load. Battery can survive 4 hours. - # #Survival after 24 hours should be chance of generator surviving 20 or more hours - # input_dict = Dict( - # "critical_loads_kw" => 100 .* ones(8760), - # "max_outage_duration" => 24, - # "num_generators" => 1, - # "generator_size_kw" => 100, - # "generator_operational_availability" => 0.98, - # "generator_failure_to_start" => 0.1, - # "generator_mean_time_to_failure" => 100, - # "battery_operational_availability" => 1.0, - # "num_battery_bins" => 101, - # "battery_size_kwh" => 400, - # "battery_size_kw" => 100, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_minimum_soc_fraction" => 0) - - # reliability_results = backup_reliability(input_dict) - # @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 - - # #More complex case of hospital load with 2 generators, PV, wind, and battery - # reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") - # reliability_results = backup_reliability(reliability_inputs) - # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 + #Given outage starts in time period 1 + #____________________________________ + #Outage hour 1: + #2 generators: Prob = 0.64, Battery = 2, Survived + #1 generator: Prob = 0.32, Battery = 1, Survived + #0 generator: Prob = 0.04, Battery = 0, Survived + #Survival Probability 1.0 + + #Outage hour 2: + #2 generators: Prob = 0.4096, Battery = 2, Survived + #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived + #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived + #0 generators: Prob = 0.1296, Battery = -1, Failed + #Survival Probability: 0.8704 + + #Outage hour 3: + #2 generators: Prob = 0.262144, Battery = 0, Survived + #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived + #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived + #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed + #0 generators Prob = 0.238144, Battery = -1, Failed + #Survival Probability: 0.557056 + @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + #Test multiple generator types + input_dict = Dict( + "critical_loads_kw" => [1,2,2,1], + "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], + "max_outage_duration" => 3, + "num_generators" => [1,1], + "generator_size_kw" => [1,1], + "generator_operational_availability" => [1,1], + "generator_failure_to_start" => [0.0, 0.0], + "generator_mean_time_to_failure" => [5, 5], + "battery_operational_availability" => 1.0, + "num_battery_bins" => 3, + "battery_size_kwh" => 2, + "battery_size_kw" => 1, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_minimum_soc_fraction" => 0) + + @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + #8760 of flat load. Battery can survive 4 hours. + #Survival after 24 hours should be chance of generator surviving 20 or more hours + input_dict = Dict( + "critical_loads_kw" => 100 .* ones(8760), + "max_outage_duration" => 24, + "num_generators" => 1, + "generator_size_kw" => 100, + "generator_operational_availability" => 0.98, + "generator_failure_to_start" => 0.1, + "generator_mean_time_to_failure" => 100, + "battery_operational_availability" => 1.0, + "num_battery_bins" => 101, + "battery_size_kwh" => 400, + "battery_size_kw" => 100, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_minimum_soc_fraction" => 0) + + reliability_results = backup_reliability(input_dict) + @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 + + #More complex case of hospital load with 2 generators, PV, wind, and battery + reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") + reliability_results = backup_reliability(reliability_inputs) + @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 - # # Test gens+pv+wind+batt with 3 arg version of backup_reliability - # # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json - # # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: - # # results = run_reopt(model, p) - # # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f - # # JSON.print(f, results, 4) - # # end - # for input_key in [ - # "generator_size_kw", - # "battery_size_kw", - # "battery_size_kwh", - # "pv_size_kw", - # "wind_size_kw", - # "critical_loads_kw", - # "pv_production_factor_series", - # "wind_production_factor_series" - # ] - # delete!(reliability_inputs, input_key) - # end - # # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) - # p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") - # results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") - # reliability_results = backup_reliability(results, p, reliability_inputs) - - # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 - # end + # Test gens+pv+wind+batt with 3 arg version of backup_reliability + # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json + # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: + # results = run_reopt(model, p) + # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f + # JSON.print(f, results, 4) + # end + for input_key in [ + "generator_size_kw", + "battery_size_kw", + "battery_size_kwh", + "pv_size_kw", + "wind_size_kw", + "critical_loads_kw", + "pv_production_factor_series", + "wind_production_factor_series" + ] + delete!(reliability_inputs, input_key) + end + # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) + p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") + results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") + reliability_results = backup_reliability(results, p, reliability_inputs) + + @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 + end @testset "Electric Storage O&M and Self-Discharge" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) @@ -552,1869 +552,1869 @@ else # run HiGHS tests sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 end - # @testset "Electric Storage Self-Discharge" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/storage_om.json") + @testset "Electric Storage Self-Discharge" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/storage_om.json") - # data["ElectricStorage"]["soc_init_fraction"] = 1.0 - # data["ElectricStorage"]["soc_min_fraction"] = 0.0 - # data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 + data["ElectricStorage"]["soc_init_fraction"] = 1.0 + data["ElectricStorage"]["soc_min_fraction"] = 0.0 + data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 - # s = Scenario(data) - # inputs = REoptInputs(s) - # results_self_discharge = run_reopt(model, inputs) + s = Scenario(data) + inputs = REoptInputs(s) + results_self_discharge = run_reopt(model, inputs) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0 + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0 - # s = Scenario(data) - # inputs = REoptInputs(s) - # results_no_self_discharge = run_reopt(model, inputs) + s = Scenario(data) + inputs = REoptInputs(s) + results_no_self_discharge = run_reopt(model, inputs) - # @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 - # @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 - # @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - - # sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 + @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 + @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - + sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 - # end + end - # @testset "Imported Xpress Test Suite" begin - # @testset "Heating loads and addressable load fraction" begin - # # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, "./scenarios/thermal_load.json") + @testset "Imported Xpress Test Suite" begin + @testset "Heating loads and addressable load fraction" begin + # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, "./scenarios/thermal_load.json") - # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 2904 + @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 2904 - # # Hourly fuel load inputs with addressable_load_fraction are served as expected - # data = JSON.parsefile("./scenarios/thermal_load.json") - # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 - # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - # data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 - # s = Scenario(data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, inputs) - # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8) + # Hourly fuel load inputs with addressable_load_fraction are served as expected + data = JSON.parsefile("./scenarios/thermal_load.json") + data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 + data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 + s = Scenario(data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, inputs) + @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8) - # # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load - # data = JSON.parsefile("./scenarios/thermal_load.json") - # data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) - # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) - # data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) - # data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) + # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load + data = JSON.parsefile("./scenarios/thermal_load.json") + data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) + data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) + data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) + data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) - # s = Scenario(data) - # inputs = REoptInputs(s) + s = Scenario(data) + inputs = REoptInputs(s) - # dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency - # space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency - # @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) - # @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) - # end + dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency + space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) .* s.existing_boiler.efficiency + @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) + @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) + end - # @testset "CHP" begin - # @testset "CHP Sizing" begin - # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - # data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") - # s = Scenario(data_sizing) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) + @testset "CHP" begin + @testset "CHP Sizing" begin + # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") + s = Scenario(data_sizing) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) - # @test round(results["CHP"]["size_kw"], digits=0) ≈ 342.0 atol=1.0 - # @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 atol=1.0e7 - # end + @test round(results["CHP"]["size_kw"], digits=0) ≈ 342.0 atol=1.0 + @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 atol=1.0e7 + end - # @testset "CHP Cost Curve and Min Allowable Size" begin - # # Fixed size CHP with cost curve, no unavailability_periods - # data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") - # data_cost_curve["CHP"] = Dict() - # data_cost_curve["CHP"]["prime_mover"] = "recip_engine" - # data_cost_curve["CHP"]["size_class"] = 1 - # data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 - # data_cost_curve["CHP"]["min_kw"] = 0 - # data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 - # data_cost_curve["CHP"]["max_kw"] = 555.51 - # data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 - # data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] - # data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] + @testset "CHP Cost Curve and Min Allowable Size" begin + # Fixed size CHP with cost curve, no unavailability_periods + data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") + data_cost_curve["CHP"] = Dict() + data_cost_curve["CHP"]["prime_mover"] = "recip_engine" + data_cost_curve["CHP"]["size_class"] = 1 + data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 + data_cost_curve["CHP"]["min_kw"] = 0 + data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 + data_cost_curve["CHP"]["max_kw"] = 555.51 + data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 + data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] + data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] - # data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 - # data_cost_curve["CHP"]["macrs_option_years"] = 0 - # data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 - # data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 + data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 + data_cost_curve["CHP"]["macrs_option_years"] = 0 + data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 + data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 - # expected_x = data_cost_curve["CHP"]["min_allowable_kw"] - # cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] - # cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] - # slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) - # init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope - # lifecycle_capex_chp_expected = init_capex_chp_expected - - # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - # [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) + expected_x = data_cost_curve["CHP"]["min_allowable_kw"] + cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] + cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] + slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) + init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope + lifecycle_capex_chp_expected = init_capex_chp_expected - + REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) - # #PV - # data_cost_curve["PV"]["min_kw"] = 1500 - # data_cost_curve["PV"]["max_kw"] = 1500 - # data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 - # data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 - # data_cost_curve["PV"]["macrs_option_years"] = 0 - # data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 - # data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 + #PV + data_cost_curve["PV"]["min_kw"] = 1500 + data_cost_curve["PV"]["max_kw"] = 1500 + data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 + data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 + data_cost_curve["PV"]["macrs_option_years"] = 0 + data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 + data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 - # init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] - # lifecycle_capex_pv_expected = init_capex_pv_expected - - # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - # [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) + init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] + lifecycle_capex_pv_expected = init_capex_pv_expected - + REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) - # s = Scenario(data_cost_curve) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) + s = Scenario(data_cost_curve) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) - # init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected - # lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected + init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected + lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected - # init_capex_total = results["Financial"]["initial_capital_costs"] - # lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] + init_capex_total = results["Financial"]["initial_capital_costs"] + lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] - # # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect - # @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected - # @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected + # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect + @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected + @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected - # # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero - # @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 - # end + # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero + @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 + end - # @testset "CHP Unavailability and Outage" begin - # """ - # Validation to ensure that: - # 1) CHP meets load during outage without exporting - # 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) - # 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) - # 4) CHP min_turn_down_fraction is ignored during an outage - # 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage - # 6) Unavailability intervals that intersect with grid-outages get ignored - # 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production - # """ - # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - # data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") + @testset "CHP Unavailability and Outage" begin + """ + Validation to ensure that: + 1) CHP meets load during outage without exporting + 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) + 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) + 4) CHP min_turn_down_fraction is ignored during an outage + 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage + 6) Unavailability intervals that intersect with grid-outages get ignored + 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production + """ + # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") - # # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period - # data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), - # ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), - # Dict([("month", 1), ("start_week_of_month", 2), - # ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] + # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period + data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), + ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), + Dict([("month", 1), ("start_week_of_month", 2), + ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] - # # Manually doing the math from the unavailability defined above - # unavail_1_start = 24 + 1 - # unavail_1_end = unavail_1_start + 8 - 1 - # unavail_2_start = 24*3 + 9 - # unavail_2_end = unavail_2_start + 8 - 1 + # Manually doing the math from the unavailability defined above + unavail_1_start = 24 + 1 + unavail_1_end = unavail_1_start + 8 - 1 + unavail_2_start = 24*3 + 9 + unavail_2_end = unavail_2_start + 8 - 1 - # # Specify the CHP.min_turn_down_fraction which is NOT used during an outage - # data["CHP"]["min_turn_down_fraction"] = 0.5 - # # Specify outage period; outage time_steps are 1-indexed - # outage_start = unavail_1_start - # data["ElectricUtility"]["outage_start_time_step"] = outage_start - # outage_end = unavail_1_end - # data["ElectricUtility"]["outage_end_time_step"] = outage_end - # data["ElectricLoad"]["critical_load_fraction"] = 0.25 + # Specify the CHP.min_turn_down_fraction which is NOT used during an outage + data["CHP"]["min_turn_down_fraction"] = 0.5 + # Specify outage period; outage time_steps are 1-indexed + outage_start = unavail_1_start + data["ElectricUtility"]["outage_start_time_step"] = outage_start + outage_end = unavail_1_end + data["ElectricUtility"]["outage_end_time_step"] = outage_end + data["ElectricLoad"]["critical_load_fraction"] = 0.25 - # s = Scenario(data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) + s = Scenario(data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) - # tot_elec_load = results["ElectricLoad"]["load_series_kw"] - # chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] - # chp_to_load = results["CHP"]["electric_to_load_series_kw"] - # chp_export = results["CHP"]["electric_to_grid_series_kw"] - # cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] + tot_elec_load = results["ElectricLoad"]["load_series_kw"] + chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] + chp_to_load = results["CHP"]["electric_to_load_series_kw"] + chp_export = results["CHP"]["electric_to_grid_series_kw"] + cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] - # # The values compared to the expected values - # @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 - # critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] - # @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 - # @test sum(chp_export) == 0.0 - # @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) - # @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 - # @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 - # end + # The values compared to the expected values + @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 + critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] + @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 + @test sum(chp_export) == 0.0 + @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) + @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 + @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 + end - # @testset "CHP Supplementary firing and standby" begin - # """ - # Test to ensure that supplementary firing and standby charges work as intended. The thermal and - # electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a - # similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower - # value than that of the supplementary firing. The test also ensures that demand charges are - # correctly calculated when CHP is and is not allowed to reduce demand charges. - # """ - # data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") - # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 - # data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) - # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - # #part 1: supplementary firing not used when less efficient than the boiler and expensive - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(m1, inputs) - # @test results["CHP"]["size_kw"] == 800 - # @test results["CHP"]["size_supplemental_firing_kw"] == 0 - # @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 - # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 - # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 - # @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] - # @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - # @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + @testset "CHP Supplementary firing and standby" begin + """ + Test to ensure that supplementary firing and standby charges work as intended. The thermal and + electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a + similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower + value than that of the supplementary firing. The test also ensures that demand charges are + correctly calculated when CHP is and is not allowed to reduce demand charges. + """ + data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") + data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 + data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) + data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + #part 1: supplementary firing not used when less efficient than the boiler and expensive + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(m1, inputs) + @test results["CHP"]["size_kw"] == 800 + @test results["CHP"]["size_supplemental_firing_kw"] == 0 + @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 + @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 + @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 + @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] + @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - # #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP - # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 - # data["CHP"]["reduces_demand_charges"] = false - # data["ExistingBoiler"]["efficiency"] = 0.85 - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(m2, inputs) - # @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 - # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 - # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 - # end - # end + #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP + data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 + data["CHP"]["reduces_demand_charges"] = false + data["ExistingBoiler"]["efficiency"] = 0.85 + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(m2, inputs) + @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 + @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 + @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 + end + end - # @testset "FlexibleHVAC" begin + @testset "FlexibleHVAC" begin - # @testset "Single RC Model heating only" begin - # #= - # Single RC model: - # 1 state/control node - # 2 inputs: Ta and Qheat - # A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] - # NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here + @testset "Single RC Model heating only" begin + #= + Single RC model: + 1 state/control node + 2 inputs: Ta and Qheat + A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] + NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here - # We start with no technologies except ExistingBoiler and ExistingChiller. - # FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) - # or if there is a time-varying fuel and/or electricity cost - # (and the FlexibleHVAC installed_cost is less than the achievable savings). - # =# + We start with no technologies except ExistingBoiler and ExistingChiller. + FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) + or if there is a time-varying fuel and/or electricity cost + (and the FlexibleHVAC installed_cost is less than the achievable savings). + =# - # # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC - # pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); - # R = 0.00025 # K/kW - # C = 1e5 # kJ/K - # # the starting scenario has flat fuel and electricty costs - # d = JSON.parsefile("./scenarios/thermal_load.json"); - # A = reshape([-1/(R*C)], 1,1) - # B = [1/(R*C) 1/C] - # u = [tamb zeros(8760)]'; - # d["FlexibleHVAC"] = Dict( - # "control_node" => 1, - # "initial_temperatures" => [21], - # "temperature_upper_bound_degC" => 22.0, - # "temperature_lower_bound_degC" => 19.8, - # "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it - # "system_matrix" => A, - # "input_matrix" => B, - # "exogenous_inputs" => u - # ) + # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC + pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); + R = 0.00025 # K/kW + C = 1e5 # kJ/K + # the starting scenario has flat fuel and electricty costs + d = JSON.parsefile("./scenarios/thermal_load.json"); + A = reshape([-1/(R*C)], 1,1) + B = [1/(R*C) 1/C] + u = [tamb zeros(8760)]'; + d["FlexibleHVAC"] = Dict( + "control_node" => 1, + "initial_temperatures" => [21], + "temperature_upper_bound_degC" => 22.0, + "temperature_lower_bound_degC" => 19.8, + "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it + "system_matrix" => A, + "input_matrix" => B, + "exogenous_inputs" => u + ) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # # @test r["Financial"]["npv"] == 0 + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # @test r["Financial"]["npv"] == 0 - # # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical - # # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC - # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # all of the savings are from the ExistingBoiler fuel costs - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true - # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical + # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC + d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # all of the savings are from the ExistingBoiler fuel costs + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true + # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased - # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # # @test r["Financial"]["npv"] == 0 + # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased + # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # @test r["Financial"]["npv"] == 0 - # # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently - # d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") + # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently + d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + - # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - - # # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - - # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] + # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + + # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - + # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - + # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] - # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings - # # + 100 and expect that the FlexibleHVAC is not purchased - # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # # @test r["Financial"]["npv"] == 0 + # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings + # + 100 and expect that the FlexibleHVAC is not purchased + # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # @test r["Financial"]["npv"] == 0 - # end - # end - - # #= - # add a time-of-export rate that is greater than retail rate for the month of January, - # check to make sure that PV does NOT export unless the site load is met first for the month of January. - # =# - # @testset "Do not allow_simultaneous_export_import" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/monthly_rate.json") - - # # create wholesale_rate with compensation in January > retail rate - # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - # data["ElectricTariff"]["wholesale_rate"] = - # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - # data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) - - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(model, inputs) + end + end + + #= + add a time-of-export rate that is greater than retail rate for the month of January, + check to make sure that PV does NOT export unless the site load is met first for the month of January. + =# + @testset "Do not allow_simultaneous_export_import" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/monthly_rate.json") + + # create wholesale_rate with compensation in January > retail rate + jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + data["ElectricTariff"]["wholesale_rate"] = + append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) + + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(model, inputs) - # @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) - # if results["PV"]["electric_to_grid_series_kw"][i] > 0) - # end - - # @testset "Solar and ElectricStorage w/BAU and degradation" begin - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # d = JSON.parsefile("scenarios/pv_storage.json"); - # d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) - # results = run_reopt([m1,m2], d) - - # @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - # @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 - # @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 - # @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 - # @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 - # @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - # proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - - # results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) - # @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 - - # # compare avg soc with and without degradation, - # # using default augmentation battery maintenance strategy - # avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 - # d["ElectricStorage"]["model_degradation"] = true - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r_degr = run_reopt(m, d) - # avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 - # @test avg_soc_no_degr > avg_soc_degr - - # # test the replacement strategy - # d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # set_optimizer_attribute(m, "mip_rel_gap", 0.01) - # r = run_reopt(m, d) - # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # # #optimal SOH at end of horizon is 80\% to prevent any replacement - # # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 - # # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 - # # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match - # # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 - # # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 - # # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 - - # # test minimum_avg_soc_fraction - # d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # set_optimizer_attribute(m, "mip_rel_gap", 0.01) - # r = run_reopt(m, d) - # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 - # end - - # @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # p = REoptInputs("./scenarios/generator.json") - # results = run_reopt([m1,m2], p) - # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - # @test results["ElectricLoad"]["bau_critical_load_met"] == false - # @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 + @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) + if results["PV"]["electric_to_grid_series_kw"][i] > 0) + end + + @testset "Solar and ElectricStorage w/BAU and degradation" begin + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + d = JSON.parsefile("scenarios/pv_storage.json"); + d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) + results = run_reopt([m1,m2], d) + + @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 + @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 + @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 + @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 + @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - + results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) + @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 + + # compare avg soc with and without degradation, + # using default augmentation battery maintenance strategy + avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 + d["ElectricStorage"]["model_degradation"] = true + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r_degr = run_reopt(m, d) + avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 + @test avg_soc_no_degr > avg_soc_degr + + # test the replacement strategy + d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + set_optimizer_attribute(m, "mip_rel_gap", 0.01) + r = run_reopt(m, d) + @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # #optimal SOH at end of horizon is 80\% to prevent any replacement + # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 + # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 + # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match + # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 + # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 + # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 + + # test minimum_avg_soc_fraction + d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + set_optimizer_attribute(m, "mip_rel_gap", 0.01) + r = run_reopt(m, d) + @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 + end + + @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + p = REoptInputs("./scenarios/generator.json") + results = run_reopt([m1,m2], p) + @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + @test results["ElectricLoad"]["bau_critical_load_met"] == false + @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 - # simresults = simulate_outages(results, p) - # @test simresults["resilience_hours_max"] == 11 - # end + simresults = simulate_outages(results, p) + @test simresults["resilience_hours_max"] == 11 + end - # @testset "Minimize Unserved Load" begin + @testset "Minimize Unserved Load" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/outage.json") - - # @test results["Outages"]["expected_outage_cost"] ≈ 0 - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 - # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - # @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 - # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - # @test value(m[:binMGStorageUsed]) ≈ 1 - # @test results["Financial"]["lcc"] ≈ 6.83633907986e7 rtol=0.01 - - # #= - # Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 - # - should meet 168 kWh in each outage such that the total unserved load is 12 kWh - # =# - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outage.json") + + @test results["Outages"]["expected_outage_cost"] ≈ 0 + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 + @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 + @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + @test value(m[:binMGStorageUsed]) ≈ 1 + @test results["Financial"]["lcc"] ≈ 6.83633907986e7 rtol=0.01 + + #= + Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 + - should meet 168 kWh in each outage such that the total unserved load is 12 kWh + =# + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 - # # testing dvUnserved load, which would output 100 kWh for this scenario before output fix - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 - # @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) - # @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 - - # # Scenario with generator, PV, electric storage - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") - # @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 - # @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 - - # # Scenario with generator, PV, wind, electric storage - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") - # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - # @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 - # @test results["Outages"]["expected_outage_cost"] ≈ 446899.75 atol=1.0 - # @test results["Financial"]["lcc"] ≈ 6.71661825335e7 rtol=0.001 - # end - - # @testset "Outages with Wind and supply-to-load no greater than critical load" begin - # input_data = JSON.parsefile("./scenarios/wind_outages.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # results = run_reopt([m1,m2], inputs) + # testing dvUnserved load, which would output 100 kWh for this scenario before output fix + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 + @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) + @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 + + # Scenario with generator, PV, electric storage + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") + @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 + @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 + + # Scenario with generator, PV, wind, electric storage + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") + @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 + @test results["Outages"]["expected_outage_cost"] ≈ 446899.75 atol=1.0 + @test results["Financial"]["lcc"] ≈ 6.71661825335e7 rtol=0.001 + end + + @testset "Outages with Wind and supply-to-load no greater than critical load" begin + input_data = JSON.parsefile("./scenarios/wind_outages.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt([m1,m2], inputs) - # # Check that supply-to-load is equal to critical load during outages, including wind - # supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] - # supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] - # critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] - # check = .≈(supply_to_load, critical_load, atol=0.001) - # @test !(0 in check) - - # # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw - # @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) - # end - - # @testset "Multiple Sites" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # ps = [ - # REoptInputs("./scenarios/pv_storage.json"), - # REoptInputs("./scenarios/monthly_rate.json"), - # ]; - # results = run_reopt(m, ps) - # @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 - # end - - # @testset "MPC" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_mpc(model, "./scenarios/mpc.json") - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - # grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] - # # the grid draw limit in the 10th time step is set to 90 - # # without the 90 limit the grid draw is 98 in the 10th time step - # @test grid_draw[10] <= 90 - # end - - # @testset "Complex Incentives" begin - # """ - # This test was compared against the API test: - # reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives - # when using the hardcoded levelization_factor in this package's REoptInputs function. - # The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) - # """ - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, "./scenarios/incentives.json") - # @test results["Financial"]["lcc"] ≈ 1.094596365e7 atol=5e4 - # end - - # @testset verbose = true "Rate Structures" begin - - # @testset "Tiered Energy" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, "./scenarios/tiered_rate.json") - # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 - # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 - # end - - # @testset "Lookback Demand Charges" begin - # # 1. Testing rate from URDB - # data = JSON.parsefile("./scenarios/lookback_rate.json") - # # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand - # # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month - # data["ElectricLoad"]["loads_kw"] = ones(8760) - # data["ElectricLoad"]["loads_kw"][8] = 100.0 - # inputs = REoptInputs(Scenario(data)) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, inputs) - # # Expected result is 100 kW demand for January, 35% of that for all other months and - # # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) - - # # 2. Testing custom rate from user with demand_lookback_months - # d = JSON.parsefile("./scenarios/lookback_rate.json") - # d["ElectricTariff"] = Dict() - # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - # d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec - # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, REoptInputs(Scenario(d))) - - # monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June - # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - # # 3. Testing custom rate from user with demand_lookback_range - # d = JSON.parsefile("./scenarios/lookback_rate.json") - # d["ElectricTariff"] = Dict() - # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - # d["ElectricTariff"]["demand_lookback_range"] = 6 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, REoptInputs(Scenario(d))) - - # monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] - # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - # end - - # @testset "Blended tariff" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/no_techs.json") - # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - # end - - # @testset "Coincident Peak Charges" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/coincident_peak.json") - # @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 - # end - - # @testset "URDB sell rate" begin - # #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) - # =# - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # p = REoptInputs("./scenarios/URDB_customer_generation.json") - # results = run_reopt(model, p) - # @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] - # end - - # @testset "Custom URDB with Sub-Hourly" begin - # # Testing a 15-min post with a urdb_response with multiple n_energy_tiers - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) - # p = REoptInputs("./scenarios/subhourly_with_urdb.json") - # results = run_reopt(model, p) - # @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 - # @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw - # end - - - # # # tiered monthly demand rate TODO: expected results? - # # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # # data = JSON.parsefile("./scenarios/tiered_rate.json") - # # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" - # # s = Scenario(data) - # # inputs = REoptInputs(s) - # # results = run_reopt(m, inputs) - - # # TODO test for tiered TOU demand rates - # end - - # @testset "EASIUR" begin - # d = JSON.parsefile("./scenarios/pv.json") - # d["Site"]["latitude"] = 30.2672 - # d["Site"]["longitude"] = -97.7431 - # scen = Scenario(d) - # @test scen.financial.NOx_grid_cost_per_tonne ≈ 4534.032470 atol=0.1 - # end - - # @testset "Wind" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d = JSON.parsefile("./scenarios/wind.json") - # results = run_reopt(m, d) - # @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 - # @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 - # #= - # 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 - # - both have zero curtailment - # - same energy to grid: 5,839,317 vs 5,839,322 - # - same energy to load: 4,160,683 vs 4,160,677 - # - same city: Boulder - # - same total wind prod factor + # Check that supply-to-load is equal to critical load during outages, including wind + supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] + supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] + critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] + check = .≈(supply_to_load, critical_load, atol=0.001) + @test !(0 in check) + + # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw + @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) + end + + @testset "Multiple Sites" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + ps = [ + REoptInputs("./scenarios/pv_storage.json"), + REoptInputs("./scenarios/monthly_rate.json"), + ]; + results = run_reopt(m, ps) + @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 + end + + @testset "MPC" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_mpc(model, "./scenarios/mpc.json") + @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] + # the grid draw limit in the 10th time step is set to 90 + # without the 90 limit the grid draw is 98 in the 10th time step + @test grid_draw[10] <= 90 + end + + @testset "Complex Incentives" begin + """ + This test was compared against the API test: + reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives + when using the hardcoded levelization_factor in this package's REoptInputs function. + The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) + """ + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, "./scenarios/incentives.json") + @test results["Financial"]["lcc"] ≈ 1.094596365e7 atol=5e4 + end + + @testset verbose = true "Rate Structures" begin + + @testset "Tiered Energy" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, "./scenarios/tiered_rate.json") + @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 + @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 + end + + @testset "Lookback Demand Charges" begin + # 1. Testing rate from URDB + data = JSON.parsefile("./scenarios/lookback_rate.json") + # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand + # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month + data["ElectricLoad"]["loads_kw"] = ones(8760) + data["ElectricLoad"]["loads_kw"][8] = 100.0 + inputs = REoptInputs(Scenario(data)) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, inputs) + # Expected result is 100 kW demand for January, 35% of that for all other months and + # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) + + # 2. Testing custom rate from user with demand_lookback_months + d = JSON.parsefile("./scenarios/lookback_rate.json") + d["ElectricTariff"] = Dict() + d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec + d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, REoptInputs(Scenario(d))) + + monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June + expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + # 3. Testing custom rate from user with demand_lookback_range + d = JSON.parsefile("./scenarios/lookback_rate.json") + d["ElectricTariff"] = Dict() + d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + d["ElectricTariff"]["demand_lookback_range"] = 6 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, REoptInputs(Scenario(d))) + + monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] + expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + end + + @testset "Blended tariff" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/no_techs.json") + @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + end + + @testset "Coincident Peak Charges" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/coincident_peak.json") + @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 + end + + @testset "URDB sell rate" begin + #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) + =# + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + p = REoptInputs("./scenarios/URDB_customer_generation.json") + results = run_reopt(model, p) + @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] + end + + @testset "Custom URDB with Sub-Hourly" begin + # Testing a 15-min post with a urdb_response with multiple n_energy_tiers + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) + p = REoptInputs("./scenarios/subhourly_with_urdb.json") + results = run_reopt(model, p) + @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 + @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw + end + + + # # tiered monthly demand rate TODO: expected results? + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/tiered_rate.json") + # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(m, inputs) + + # TODO test for tiered TOU demand rates + end + + @testset "EASIUR" begin + d = JSON.parsefile("./scenarios/pv.json") + d["Site"]["latitude"] = 30.2672 + d["Site"]["longitude"] = -97.7431 + scen = Scenario(d) + @test scen.financial.NOx_grid_cost_per_tonne ≈ 4534.032470 atol=0.1 + end + + @testset "Wind" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d = JSON.parsefile("./scenarios/wind.json") + results = run_reopt(m, d) + @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 + @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 + #= + 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 + - both have zero curtailment + - same energy to grid: 5,839,317 vs 5,839,322 + - same energy to load: 4,160,683 vs 4,160,677 + - same city: Boulder + - same total wind prod factor - # REopt.jl has: - # - bigger turbine: 3752 vs 3735 - # - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 - - # TODO: will these discrepancies be addressed once NMIL binaries are added? - # =# - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) - # results = run_reopt(m, d) - # @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error - # results = run_reopt(m, d) - # @test "errors" ∈ keys(results["Messages"]) - # @test length(results["Messages"]["errors"]) > 0 - # end - - # @testset "Multiple PVs" begin - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") - - # ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] - # roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] - # roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] - - # @test ground_pv["size_kw"] ≈ 15 atol=0.1 - # @test roof_west["size_kw"] ≈ 7 atol=0.1 - # @test roof_east["size_kw"] ≈ 4 atol=0.1 - # @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - # @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - # @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 - # @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 - # @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 - # @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 - # @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 - # end - - # @testset "Thermal Energy Storage + Absorption Chiller" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/thermal_storage.json") - # s = Scenario(data) - # p = REoptInputs(s) + REopt.jl has: + - bigger turbine: 3752 vs 3735 + - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 + + TODO: will these discrepancies be addressed once NMIL binaries are added? + =# + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) + results = run_reopt(m, d) + @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error + results = run_reopt(m, d) + @test "errors" ∈ keys(results["Messages"]) + @test length(results["Messages"]["errors"]) > 0 + end + + @testset "Multiple PVs" begin + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") + + ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] + roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] + roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] + + @test ground_pv["size_kw"] ≈ 15 atol=0.1 + @test roof_west["size_kw"] ≈ 7 atol=0.1 + @test roof_east["size_kw"] ≈ 4 atol=0.1 + @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 + @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 + @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 + @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 + @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 + end + + @testset "Thermal Energy Storage + Absorption Chiller" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/thermal_storage.json") + s = Scenario(data) + p = REoptInputs(s) - # #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. - # htf_defaults_response = get_absorption_chiller_defaults(; - # thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), - # boiler_type=get(data["ExistingBoiler"], "production_type", nothing), - # load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - # ) + #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. + htf_defaults_response = get_absorption_chiller_defaults(; + thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), + boiler_type=get(data["ExistingBoiler"], "production_type", nothing), + load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + ) - # expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] - # expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] + expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] + expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] - # @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - # @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - # @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 + @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 - # #load test values - # p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR - # p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR - # p.s.absorption_chiller.cop_thermal = 0.7 + #load test values + p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR + p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR + p.s.absorption_chiller.cop_thermal = 0.7 - # #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period - # for ts in p.time_steps - # #heating and cooling loads only - # if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge - # p.s.electric_load.loads_kw[ts] = 10 - # p.s.dhw_load.loads_kw[ts] = 5 - # p.s.space_heating_load.loads_kw[ts] = 5 - # p.s.cooling_load.loads_kw_thermal[ts] = 10 - # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 - # for tier in 1:p.s.electric_tariff.n_energy_tiers - # p.s.electric_tariff.energy_rates[ts, tier] = 100 - # end - # else #in odd periods, there is no load and energy is cheaper - storage should charge - # p.s.electric_load.loads_kw[ts] = 0 - # p.s.dhw_load.loads_kw[ts] = 0 - # p.s.space_heating_load.loads_kw[ts] = 0 - # p.s.cooling_load.loads_kw_thermal[ts] = 0 - # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 - # for tier in 1:p.s.electric_tariff.n_energy_tiers - # p.s.electric_tariff.energy_rates[ts, tier] = 50 - # end - # end - # end + #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period + for ts in p.time_steps + #heating and cooling loads only + if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge + p.s.electric_load.loads_kw[ts] = 10 + p.s.dhw_load.loads_kw[ts] = 5 + p.s.space_heating_load.loads_kw[ts] = 5 + p.s.cooling_load.loads_kw_thermal[ts] = 10 + p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 + for tier in 1:p.s.electric_tariff.n_energy_tiers + p.s.electric_tariff.energy_rates[ts, tier] = 100 + end + else #in odd periods, there is no load and energy is cheaper - storage should charge + p.s.electric_load.loads_kw[ts] = 0 + p.s.dhw_load.loads_kw[ts] = 0 + p.s.space_heating_load.loads_kw[ts] = 0 + p.s.cooling_load.loads_kw_thermal[ts] = 0 + p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 + for tier in 1:p.s.electric_tariff.n_energy_tiers + p.s.electric_tariff.energy_rates[ts, tier] = 50 + end + end + end - # r = run_reopt(model, p) + r = run_reopt(model, p) - # #dispatch to load should be 10kW every other period = 4,380 * 10 - # @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 - # @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 - # #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC - # @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 - # @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 - # #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. - # @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 - # @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 - # @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 - # end - - # @testset "Heat and cool energy balance" begin - # """ - - # This is an "energy balance" type of test which tests the model formulation/math as opposed - # to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting - - # Validation to ensure that: - # 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage - # 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load - # 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly - - # """ - # input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) - - # # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load - # # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load - # crb_cop = REopt.get_existing_chiller_default_cop(; - # existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, - # max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) - # cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) - # cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR - - # #Test cooling load results - # @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 + #dispatch to load should be 10kW every other period = 4,380 * 10 + @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 + @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 + #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC + @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 + @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 + #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. + @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 + @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 + @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 + end + + @testset "Heat and cool energy balance" begin + """ + + This is an "energy balance" type of test which tests the model formulation/math as opposed + to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting + + Validation to ensure that: + 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage + 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load + 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly + + """ + input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) + + # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load + # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load + crb_cop = REopt.get_existing_chiller_default_cop(; + existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, + max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) + cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) + cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR + + #Test cooling load results + @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 - # # Convert fuel input to thermal using user input boiler efficiency - # boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) - # boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency - - # # Cooling outputs - # cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] - # cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] - # cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] - # cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] - # cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) - # cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) - # cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] - # cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) - # tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total - # cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total - # cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses - - # # Absorption Chiller electric consumption addition - # absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series - # absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) - # absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] - # absorpchl_cop_elec = s.absorption_chiller.cop_electric - - # # Check if sum of electric and absorption chillers equals cooling thermal total - # @test tes_effic_with_decay < 0.97 - # @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 - # @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 - - # # Heating outputs - # boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] - # boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] - # boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - # boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] - # chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] - # chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] - # chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] - # absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - # hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - # tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) - # total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) - # tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) - # total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) - # boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency - # total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows - - # @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 - # @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 - - # # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) - # absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU - # absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR - # absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh - - # @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 - # end - - # @testset "Heating and cooling inputs + CHP defaults" begin - # """ - - # This tests the various ways to input heating and cooling loads to make sure they are processed correctly. - # There are no "new" technologies in this test, so heating is served by ExistingBoiler, and - # cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. - - # """ - # input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # # Heating load is input as **fuel**, not thermal - # # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal - # expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] - # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 - # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 - # # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal - # input_data["ExistingBoiler"]["efficiency"] = 0.72 - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 - # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 - - # # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input - # # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile - # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop - # @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** - - # #Test CHP defaults use average fuel load, size class 2 for recip_engine - # @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 - # @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 - - # delete!(input_data, "SpaceHeatingLoad") - # delete!(input_data, "DomesticHotWaterLoad") - # annual_fraction_of_electric_load_input = 0.5 - # input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input - # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - # @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 - # @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 - - # # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load - # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 - - # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - # input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # #Test CHP defaults use average fuel load, size class changes to 3 - # @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 - # @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 - # #Update CHP prime_mover and test new defaults - # input_data["CHP"]["prime_mover"] = "combustion_turbine" - # input_data["CHP"]["size_class"] = 1 - # # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw - # input_data["CHP"]["max_kw"] = 2500.0 - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 - # @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 - - # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 - # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - # @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 - - # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - # input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 - # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 - - # # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied - # input_data["SpaceHeatingLoad"] = Dict{Any, Any}() - # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() - # annual_tonhour = 25000.0 - # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", - # "annual_tonhour" => annual_tonhour) - # input_data["ExistingChiller"] = Dict{Any, Any}() - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 + # Convert fuel input to thermal using user input boiler efficiency + boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) + boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency + + # Cooling outputs + cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] + cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] + cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] + cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] + cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) + cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) + cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] + cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) + tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total + cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total + cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses + + # Absorption Chiller electric consumption addition + absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series + absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) + absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] + absorpchl_cop_elec = s.absorption_chiller.cop_electric + + # Check if sum of electric and absorption chillers equals cooling thermal total + @test tes_effic_with_decay < 0.97 + @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 + @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 + + # Heating outputs + boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] + boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] + chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] + chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] + chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] + absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) + total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) + tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) + total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) + boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency + total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows + + @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 + @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 + + # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) + absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU + absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR + absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh + + @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 + end + + @testset "Heating and cooling inputs + CHP defaults" begin + """ + + This tests the various ways to input heating and cooling loads to make sure they are processed correctly. + There are no "new" technologies in this test, so heating is served by ExistingBoiler, and + cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. + + """ + input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + + # Heating load is input as **fuel**, not thermal + # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal + expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] + total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 + total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 + # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal + input_data["ExistingBoiler"]["efficiency"] = 0.72 + s = Scenario(input_data) + inputs = REoptInputs(s) + total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 + total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 + + # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input + # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile + total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop + @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** + + #Test CHP defaults use average fuel load, size class 2 for recip_engine + @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 + @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 + + delete!(input_data, "SpaceHeatingLoad") + delete!(input_data, "DomesticHotWaterLoad") + annual_fraction_of_electric_load_input = 0.5 + input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) + + s = Scenario(input_data) + inputs = REoptInputs(s) + + expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input + total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 + @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 + + # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load + @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 + + input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) + + s = Scenario(input_data) + inputs = REoptInputs(s) + + #Test CHP defaults use average fuel load, size class changes to 3 + @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 + @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 + #Update CHP prime_mover and test new defaults + input_data["CHP"]["prime_mover"] = "combustion_turbine" + input_data["CHP"]["size_class"] = 1 + # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw + input_data["CHP"]["max_kw"] = 2500.0 + + s = Scenario(input_data) + inputs = REoptInputs(s) + + @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 + @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 + + total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 + total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 + + input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) + + s = Scenario(input_data) + inputs = REoptInputs(s) + + total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 + @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 + + # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied + input_data["SpaceHeatingLoad"] = Dict{Any, Any}() + input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() + annual_tonhour = 25000.0 + input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", + "annual_tonhour" => annual_tonhour) + input_data["ExistingChiller"] = Dict{Any, Any}() + + s = Scenario(input_data) + inputs = REoptInputs(s) + + @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 - # # Test for prime generator CHP inputs (electric only) - # # First get CHP cost to compare later with prime generator - # input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", - # "annual_kwh" => 876000) - # input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, - # "blended_annual_demand_rate" => 0.0 ) - # s_chp = Scenario(input_data) - # inputs_chp = REoptInputs(s) - # installed_cost_chp = s_chp.chp.installed_cost_per_kw - - # # Now get prime generator (electric only) - # input_data["CHP"]["is_electric_only"] = true - # delete!(input_data["CHP"], "max_kw") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # # Costs are 75% of CHP - # @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 - # @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 - # @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 - # # Thermal efficiency set to zero - # @test inputs.s.chp.thermal_efficiency_full_load == 0 - # @test inputs.s.chp.thermal_efficiency_half_load == 0 - # # Max size based on electric load, not heating load - # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 - # end - - # @testset "Hybrid/blended heating and cooling loads" begin - # """ - - # This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names - # and blended_doe_reference_percents are given and blended to create an aggregate load profile - - # """ - # input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") - - # hospital_fraction = 0.75 - # hotel_fraction = 1.0 - hospital_fraction - - # # Hospital only - # input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 - # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 - # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" - # input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # elec_hospital = inputs.s.electric_load.loads_kw - # space_hospital = inputs.s.space_heating_load.loads_kw # thermal - # dhw_hospital = inputs.s.dhw_load.loads_kw # thermal - # cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal - # cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital - - # # Hotel only - # input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 - # input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 - # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" - # input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # elec_hotel = inputs.s.electric_load.loads_kw - # space_hotel = inputs.s.space_heating_load.loads_kw # thermal - # dhw_hotel = inputs.s.dhw_load.loads_kw # thermal - # cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal - # cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel - - # # Hybrid mix of hospital and hotel - # # Remove previous assignment of doe_reference_name - # for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] - # delete!(input_data[load], "doe_reference_name") - # end - # annual_energy = (hospital_fraction + hotel_fraction) * 100 - # building_list = ["Hospital", "LargeHotel"] - # percent_share_list = [hospital_fraction, hotel_fraction] - # input_data["ElectricLoad"]["annual_kwh"] = annual_energy - # input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list - # input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list - - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy - # input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list - # input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy - # input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list - # input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list - - # # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided - # input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list - # input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # elec_hybrid = inputs.s.electric_load.loads_kw - # space_hybrid = inputs.s.space_heating_load.loads_kw # thermal - # dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal - # cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal - # cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric - # cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid - - # # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step - - # @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 - # @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 - # @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 - # # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles - # cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ - # cooling_elec_frac_of_total_hotel * hotel_fraction) - # @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 - # end - - # @testset "Boiler (new) test" begin - # input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], inputs) + # Test for prime generator CHP inputs (electric only) + # First get CHP cost to compare later with prime generator + input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", + "annual_kwh" => 876000) + input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, + "blended_annual_demand_rate" => 0.0 ) + s_chp = Scenario(input_data) + inputs_chp = REoptInputs(s) + installed_cost_chp = s_chp.chp.installed_cost_per_kw + + # Now get prime generator (electric only) + input_data["CHP"]["is_electric_only"] = true + delete!(input_data["CHP"], "max_kw") + s = Scenario(input_data) + inputs = REoptInputs(s) + # Costs are 75% of CHP + @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 + @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 + @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 + # Thermal efficiency set to zero + @test inputs.s.chp.thermal_efficiency_full_load == 0 + @test inputs.s.chp.thermal_efficiency_half_load == 0 + # Max size based on electric load, not heating load + @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 + end + + @testset "Hybrid/blended heating and cooling loads" begin + """ + + This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names + and blended_doe_reference_percents are given and blended to create an aggregate load profile + + """ + input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") + + hospital_fraction = 0.75 + hotel_fraction = 1.0 - hospital_fraction + + # Hospital only + input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 + input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 + input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" + input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" + + s = Scenario(input_data) + inputs = REoptInputs(s) + + elec_hospital = inputs.s.electric_load.loads_kw + space_hospital = inputs.s.space_heating_load.loads_kw # thermal + dhw_hospital = inputs.s.dhw_load.loads_kw # thermal + cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal + cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital + + # Hotel only + input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 + input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 + input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" + input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" + + s = Scenario(input_data) + inputs = REoptInputs(s) + + elec_hotel = inputs.s.electric_load.loads_kw + space_hotel = inputs.s.space_heating_load.loads_kw # thermal + dhw_hotel = inputs.s.dhw_load.loads_kw # thermal + cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal + cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel + + # Hybrid mix of hospital and hotel + # Remove previous assignment of doe_reference_name + for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] + delete!(input_data[load], "doe_reference_name") + end + annual_energy = (hospital_fraction + hotel_fraction) * 100 + building_list = ["Hospital", "LargeHotel"] + percent_share_list = [hospital_fraction, hotel_fraction] + input_data["ElectricLoad"]["annual_kwh"] = annual_energy + input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list + input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list + + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy + input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list + input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy + input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list + input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list + + # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided + input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list + input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list + + s = Scenario(input_data) + inputs = REoptInputs(s) + + elec_hybrid = inputs.s.electric_load.loads_kw + space_hybrid = inputs.s.space_heating_load.loads_kw # thermal + dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal + cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal + cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric + cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid + + # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step + + @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 + @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 + @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 + # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles + cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ + cooling_elec_frac_of_total_hotel * hotel_fraction) + @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 + end + + @testset "Boiler (new) test" begin + input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], inputs) - # # BAU boiler loads - # load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU - # existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - # boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) + # BAU boiler loads + load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU + existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) - # # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only - # # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year - # @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 - # @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 - # end - - # @testset "OffGrid" begin - # ## Scenario 1: Solar, Storage, Fixed Generator - # post_name = "off_grid.json" - # post = JSON.parsefile("./scenarios/$post_name") - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, post) - # scen = Scenario(post) + # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only + # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year + @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 + @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 + end + + @testset "OffGrid" begin + ## Scenario 1: Solar, Storage, Fixed Generator + post_name = "off_grid.json" + post = JSON.parsefile("./scenarios/$post_name") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, post) + scen = Scenario(post) - # # Test default values - # @test scen.electric_utility.outage_start_time_step ≈ 1 - # @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour - # @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 - # @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false - # @test scen.generator.fuel_avail_gal ≈ 1.0e9 - # @test scen.generator.min_turn_down_fraction ≈ 0.15 - # @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw - # @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 - - # # Test outputs - # @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid - # @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc - # @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required - # @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - # @test r["PV"]["size_kw"] ≈ 5050.0 - # f = r["Financial"] - # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + # Test default values + @test scen.electric_utility.outage_start_time_step ≈ 1 + @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour + @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 + @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false + @test scen.generator.fuel_avail_gal ≈ 1.0e9 + @test scen.generator.min_turn_down_fraction ≈ 0.15 + @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw + @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 + + # Test outputs + @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid + @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc + @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required + @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + @test r["PV"]["size_kw"] ≈ 5050.0 + f = r["Financial"] + @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - # ## Scenario 2: Fixed Generator only - # post["ElectricLoad"]["annual_kwh"] = 100.0 - # post["PV"]["max_kw"] = 0.0 - # post["ElectricStorage"]["max_kw"] = 0.0 - # post["Generator"]["min_turn_down_fraction"] = 0.0 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, post) - - # # Test generator outputs - # @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh - # @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 - # @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 - # @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 - # @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) - # @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible - # @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 - # @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 - # @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 - - # ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement - # ## This test ensures the load operating reserve requirement is being enforced - # post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" - # post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen - # post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity - # post["PV"]["max_kw"] = 0.0 - # post["ElectricStorage"]["max_kw"] = 0.0 - # post["Generator"]["min_turn_down_fraction"] = 0.0 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, post) - - # # Test generator outputs - # @test typeof(r) == Model # this is true when the model is infeasible - - # ### Scenario 3: Indonesia. Wind (custom prod) and Generator only - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # post_name = "wind_intl_offgrid.json" - # post = JSON.parsefile("./scenarios/$post_name") - # post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] - # scen = Scenario(post) - # post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) - - # results = run_reopt(m, post) + ## Scenario 2: Fixed Generator only + post["ElectricLoad"]["annual_kwh"] = 100.0 + post["PV"]["max_kw"] = 0.0 + post["ElectricStorage"]["max_kw"] = 0.0 + post["Generator"]["min_turn_down_fraction"] = 0.0 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, post) + + # Test generator outputs + @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh + @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 + @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 + @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 + @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) + @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible + @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 + @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 + @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 + + ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement + ## This test ensures the load operating reserve requirement is being enforced + post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" + post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen + post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity + post["PV"]["max_kw"] = 0.0 + post["ElectricStorage"]["max_kw"] = 0.0 + post["Generator"]["min_turn_down_fraction"] = 0.0 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, post) + + # Test generator outputs + @test typeof(r) == Model # this is true when the model is infeasible + + ### Scenario 3: Indonesia. Wind (custom prod) and Generator only + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + post_name = "wind_intl_offgrid.json" + post = JSON.parsefile("./scenarios/$post_name") + post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] + scen = Scenario(post) + post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) + + results = run_reopt(m, post) - # @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - # f = results["Financial"] - # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - - # windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) - # loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) - # @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 - - # end - - # @testset "GHP" begin - # """ - - # This tests multiple unique aspects of GHP: - # 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one - # 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load - # 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW - # 4. GHP serves all the Cooling load - # 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly - # 6. Hybrid GHP capability functions as expected - - # """ - # # Load base inputs - # input_data = JSON.parsefile("scenarios/ghp_inputs.json") + @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + f = results["Financial"] + @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + + windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) + loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) + @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 + + end + + @testset "GHP" begin + """ + + This tests multiple unique aspects of GHP: + 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one + 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load + 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW + 4. GHP serves all the Cooling load + 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly + 6. Hybrid GHP capability functions as expected + + """ + # Load base inputs + input_data = JSON.parsefile("scenarios/ghp_inputs.json") - # # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl - # # Heat pump performance maps - # cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) - # data = cop_map_mat_header[1] - # headers = cop_map_mat_header[2] - # # Generate a "records" style dictionary from the - # cop_map_list = [] - # for i in axes(data,1) - # dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) - # push!(cop_map_list, dict_record) - # end - # input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list + # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl + # Heat pump performance maps + cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) + data = cop_map_mat_header[1] + headers = cop_map_mat_header[2] + # Generate a "records" style dictionary from the + cop_map_list = [] + for i in axes(data,1) + dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) + push!(cop_map_list, dict_record) + end + input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list - # # Due to GhpGhx not being a registered package (no OSI-approved license), - # # the registered REopt package cannot have GhpGhx as a "normal" dependency; - # # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an - # # input to REopt to avoid GhpGhx module calls - # response_1 = JSON.parsefile("scenarios/ghpghx_response.json") - # response_2 = deepcopy(response_1) - # # Reduce the electric consumption of response 2 which should then be the chosen system - # response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 - # input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] + # Due to GhpGhx not being a registered package (no OSI-approved license), + # the registered REopt package cannot have GhpGhx as a "normal" dependency; + # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an + # input to REopt to avoid GhpGhx module calls + response_1 = JSON.parsefile("scenarios/ghpghx_response.json") + response_2 = deepcopy(response_1) + # Reduce the electric consumption of response 2 which should then be the chosen system + response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 + input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] - # # Heating load - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - # input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) - # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 - # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 + # Heating load + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) + input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 + input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 - # # Call REopt - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt([m1,m2], inputs) + # Call REopt + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt([m1,m2], inputs) - # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - # @test ghp_option_chosen == 2 - - # # Test GHP heating and cooling load reduced - # hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) - # cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) - # @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 - # @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 - - # # Test GHP serving space heating with VAV thermal efficiency improvements - # heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) - # expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) - # @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 + ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + @test ghp_option_chosen == 2 + + # Test GHP heating and cooling load reduced + hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) + cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) + @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 + @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 + + # Test GHP serving space heating with VAV thermal efficiency improvements + heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) + expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) + @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 - # # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit - # boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - # expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) - # @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 + # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit + boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) + @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 - # # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production - # bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - # ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) - # @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 + # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production + bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) + @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 - # # Custom heat pump COP map is used properly - # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - # heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] - # cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] - # # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map - # @test heating_cop_avg <= 4.0 - # @test cooling_cop_avg <= 8.0 - # end - - # @testset "Hybrid GHX and GHP calculated costs validation" begin - # ## Hybrid GHP validation. - # # Load base inputs - # input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") - - # inputs = REoptInputs(input_data) - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt([m1,m2], inputs) - - # calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* - # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* - # inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + - # (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* - # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* - # inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + - # (inputs.s.ghp_option_list[1].building_sqft* - # inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) - - # @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 + # Custom heat pump COP map is used properly + ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] + cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] + # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map + @test heating_cop_avg <= 4.0 + @test cooling_cop_avg <= 8.0 + end + + @testset "Hybrid GHX and GHP calculated costs validation" begin + ## Hybrid GHP validation. + # Load base inputs + input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") + + inputs = REoptInputs(input_data) + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt([m1,m2], inputs) + + calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* + input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* + inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + + (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* + input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* + inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + + (inputs.s.ghp_option_list[1].building_sqft* + inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) + + @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 - # calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* - # inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om + calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* + inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om - # @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 + @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 - # calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) - # @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 + calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) + @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 - # @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 + @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 - # @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 + @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 - # @test abs(results["Financial"]["npv"] - 840621) < 1.0 - # @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 - # @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 + @test abs(results["Financial"]["npv"] - 840621) < 1.0 + @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 + @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 - # @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") + @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") - # ## Hybrid - # input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] - # input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 - # input_data["GHP"]["ghx_useful_life_years"] = 35 + ## Hybrid + input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] + input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 + input_data["GHP"]["ghx_useful_life_years"] = 35 - # inputs = REoptInputs(input_data) + inputs = REoptInputs(input_data) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt([m1,m2], inputs) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt([m1,m2], inputs) - # pop!(input_data["GHP"], "ghpghx_inputs", nothing) - # pop!(input_data["GHP"], "ghpghx_responses", nothing) - # ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) + pop!(input_data["GHP"], "ghpghx_inputs", nothing) + pop!(input_data["GHP"], "ghpghx_responses", nothing) + ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) - # calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* - # ( - # (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years - # )/( - # (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years - # ) + calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* + ( + (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years + )/( + (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years + ) - # @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 - # @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true - - # # Test centralized GHP cost calculations - # input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") - # response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") - # input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] - - # s_wwhp = Scenario(input_data_wwhp) - # inputs_wwhp = REoptInputs(s_wwhp) - # m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results_wwhp = run_reopt(m3, inputs_wwhp) - - - # heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * - # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] - - # cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * - # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] - - # ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] - - # # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount - # capex_reduction_factor = 0.455005797 - - # calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) - - # reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] - # @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 - # end - - # @testset "Cambium Emissions" begin - # """ - # 1) Location in contiguous US - # - Correct data from Cambium (returned location and values) - # - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) - # - co2 pct increase should be zero - # 2) HI and AK locations - # - Should use AVERT data and give an "info" message - # - Adjust for load year vs. AVERT year - # - co2 pct increase should be the default value unless user provided value - # 3) International - # - all emissions should be zero unless provided - # """ - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 + @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true + + # Test centralized GHP cost calculations + input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") + response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") + input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] + + s_wwhp = Scenario(input_data_wwhp) + inputs_wwhp = REoptInputs(s_wwhp) + m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results_wwhp = run_reopt(m3, inputs_wwhp) + + + heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * + input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] + + cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * + input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] + + ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] + + # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount + capex_reduction_factor = 0.455005797 + + calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) + + reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] + @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 + end + + @testset "Cambium Emissions" begin + """ + 1) Location in contiguous US + - Correct data from Cambium (returned location and values) + - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) + - co2 pct increase should be zero + 2) HI and AK locations + - Should use AVERT data and give an "info" message + - Adjust for load year vs. AVERT year + - co2 pct increase should be the default value unless user provided value + 3) International + - all emissions should be zero unless provided + """ + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # post_name = "cambium.json" - # post = JSON.parsefile("./scenarios/$post_name") + post_name = "cambium.json" + post = JSON.parsefile("./scenarios/$post_name") - # cities = Dict( - # "Denver" => (39.7413753050447, -104.99965032911328), - # "Fairbanks" => (64.84053664406181, -147.71913656313163), - # "Santiago" => (-33.44485437650408, -70.69031905547853) - # ) + cities = Dict( + "Denver" => (39.7413753050447, -104.99965032911328), + "Fairbanks" => (64.84053664406181, -147.71913656313163), + "Santiago" => (-33.44485437650408, -70.69031905547853) + ) - # # 1) Location in contiguous US - # city = "Denver" - # post["Site"]["latitude"] = cities[city][1] - # post["Site"]["longitude"] = cities[city][2] - # post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] - # post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri - # scen = Scenario(post) + # 1) Location in contiguous US + city = "Denver" + post["Site"]["latitude"] = cities[city][1] + post["Site"]["longitude"] = cities[city][2] + post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] + post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri + scen = Scenario(post) - # @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" - # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - # @test scen.electric_utility.cambium_emissions_region == "RMPAc" - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 - # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday - # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday - # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region - # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data - # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # # 2) AK location - # city = "Fairbanks" - # post["Site"]["latitude"] = cities[city][1] - # post["Site"]["longitude"] = cities[city][2] - # scen = Scenario(post) + @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" + @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + @test scen.electric_utility.cambium_emissions_region == "RMPAc" + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 + @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday + @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday + @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region + @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data + @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # 2) AK location + city = "Fairbanks" + post["Site"]["latitude"] = cities[city][1] + post["Site"]["longitude"] = cities[city][2] + scen = Scenario(post) - # @test scen.electric_utility.avert_emissions_region == "Alaska" - # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used - # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value - # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # # 3) International location - # city = "Santiago" - # post["Site"]["latitude"] = cities[city][1] - # post["Site"]["longitude"] = cities[city][2] - # scen = Scenario(post) + @test scen.electric_utility.avert_emissions_region == "Alaska" + @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used + @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value + @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # 3) International location + city = "Santiago" + post["Site"]["latitude"] = cities[city][1] + post["Site"]["longitude"] = cities[city][2] + scen = Scenario(post) - # @test scen.electric_utility.avert_emissions_region == "" - # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 - # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 + @test scen.electric_utility.avert_emissions_region == "" + @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 + @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 - # end - - # @testset "Emissions and Renewable Energy Percent" begin - # #renewable energy and emissions reduction targets - # include_exported_RE_in_total = [true,false,true] - # include_exported_ER_in_total = [true,false,true] - # RE_target = [0.8,nothing,nothing] - # ER_target = [nothing,0.8,nothing] - # with_outage = [true,false,false] - - # for i in range(1, stop=3) - # if i == 3 - # inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") - # else - # inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") - # end - # if i == 1 - # inputs["Site"]["latitude"] = 37.746 - # inputs["Site"]["longitude"] = -122.448 - # # inputs["ElectricUtility"]["emissions_region"] = "California" - # end - # inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] - # inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] - # inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end - # inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] - # inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] - # inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] - # if with_outage[i] - # outage_start_hour = 4032 - # outage_duration = 2000 #hrs - # inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 - # inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration - # inputs["Generator"]["max_kw"] = 20 - # inputs["Generator"]["existing_kw"] = 2 - # inputs["Generator"]["fuel_avail_gal"] = 1000 - # end - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt([m1, m2], inputs) - - # if !isnothing(ER_target[i]) - # ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] - # @test ER_target[i] ≈ ER_fraction_out atol=1e-3 - # lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] - # lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] - # ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out - # ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) - # @test ER_fraction_diff ≈ 0.0 atol=1e-2 - # end - - # annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] - # yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] - # yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] - # yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out - # @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 - # if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 - # end + end + + @testset "Emissions and Renewable Energy Percent" begin + #renewable energy and emissions reduction targets + include_exported_RE_in_total = [true,false,true] + include_exported_ER_in_total = [true,false,true] + RE_target = [0.8,nothing,nothing] + ER_target = [nothing,0.8,nothing] + with_outage = [true,false,false] + + for i in range(1, stop=3) + if i == 3 + inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") + else + inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") + end + if i == 1 + inputs["Site"]["latitude"] = 37.746 + inputs["Site"]["longitude"] = -122.448 + # inputs["ElectricUtility"]["emissions_region"] = "California" + end + inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] + inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] + inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end + inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] + inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] + inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] + if with_outage[i] + outage_start_hour = 4032 + outage_duration = 2000 #hrs + inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 + inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration + inputs["Generator"]["max_kw"] = 20 + inputs["Generator"]["existing_kw"] = 2 + inputs["Generator"]["fuel_avail_gal"] = 1000 + end + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt([m1, m2], inputs) + + if !isnothing(ER_target[i]) + ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] + @test ER_target[i] ≈ ER_fraction_out atol=1e-3 + lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] + lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] + ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out + ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) + @test ER_fraction_diff ≈ 0.0 atol=1e-2 + end + + annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] + yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] + yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] + yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out + @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 + if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 + end - # if i == 1 - # @test results["PV"]["size_kw"] ≈ 60.12 atol=1e-1 - # @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 - # @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 - # @test results["Generator"]["size_kw"] ≈ 21.52 atol=1e-1 - # @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 - # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.147576 atol=1e-4 - # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.58694032 atol=1e-4 - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 355.8 atol=1 - # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.64 atol=1e-2 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.0605 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8315.69 atol=1 - # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 232.85 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 141.21 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 28.186 atol=1e-1 - # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 563.72 - # elseif i == 2 - # #commented out values are results using same levelization factor as API - # @test results["PV"]["size_kw"] ≈ 106.13 atol=1 - # @test results["ElectricStorage"]["size_kw"] ≈ 21.58 atol=1 # 20.29 - # @test results["ElectricStorage"]["size_kwh"] ≈ 166.29 atol=1 - # @test !haskey(results, "Generator") - # # Renewable energy - # @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 - # @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 - # @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13211.78 atol=10 # 13542.62 atol=10 - # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 - # # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven - # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 460.7 atol=1e-1 - # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 - # @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8401.1 atol=1 - # @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 - # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 + if i == 1 + @test results["PV"]["size_kw"] ≈ 60.12 atol=1e-1 + @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 + @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 + @test results["Generator"]["size_kw"] ≈ 21.52 atol=1e-1 + @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 + @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.147576 atol=1e-4 + @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.58694032 atol=1e-4 + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 355.8 atol=1 + @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.64 atol=1e-2 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.0605 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8315.69 atol=1 + @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 232.85 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 141.21 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 28.186 atol=1e-1 + @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 563.72 + elseif i == 2 + #commented out values are results using same levelization factor as API + @test results["PV"]["size_kw"] ≈ 106.13 atol=1 + @test results["ElectricStorage"]["size_kw"] ≈ 21.58 atol=1 # 20.29 + @test results["ElectricStorage"]["size_kwh"] ≈ 166.29 atol=1 + @test !haskey(results, "Generator") + # Renewable energy + @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 + @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 + @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13211.78 atol=10 # 13542.62 atol=10 + @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 + # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven + @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 460.7 atol=1e-1 + @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 + @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8401.1 atol=1 + @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 + @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 - # #also test CO2 breakeven cost - # inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - # inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - # inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] - # inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] - # inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] - # inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] - # inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] - # inputs["Settings"]["include_climate_in_objective"] = true - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt([m1, m2], inputs) - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 - # elseif i == 3 - # @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 - # @test !haskey(results, "Wind") - # @test !haskey(results, "ElectricStorage") - # @test !haskey(results, "Generator") - # @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 - # @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 - # @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 - # @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 - # yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] - # nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) - # TONNE_PER_LB = 1/2204.62 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 - # @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 - # @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 - # @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 - # @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 - # @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 - # KWH_PER_MMBTU = 293.07107 - # annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] - # annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU - # @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 - # end - # end - # end - - # @testset "Back pressure steam turbine" begin - # """ - # Validation to ensure that: - # 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application - # 2) SteamTurbine serves the heating load with the condensing steam - - # """ - # # Setup inputs, make heating load large to entice SteamTurbine - # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - # latitude = input_data["Site"]["latitude"] - # longitude = input_data["Site"]["longitude"] - # building = "Hospital" - # elec_load_multiplier = 5.0 - # heat_load_multiplier = 100.0 - # input_data["ElectricLoad"]["doe_reference_name"] = building - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = building - # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building - # elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) - # input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) - # space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - # dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], inputs) - - # # The expected values below were directly copied from the REopt_API V2 expected values - # @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 - # @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 - # @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 - # @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 - # @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 - # @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 - - # # BAU boiler loads - # load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency - # load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency - - # # ExistingBoiler and SteamTurbine production - # boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - # boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] - # boiler_total = boiler_to_load + boiler_to_st - # st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] - - # # Fuel/thermal **consumption** - # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - - # # Check that all thermal supply to load meets the BAU load - # thermal_to_load = sum(boiler_to_load) + sum(st_to_load) - # @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 - - # # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet - # steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] - # net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) - # @test net_electric_efficiency ≈ 0.052 atol=0.005 - - # # Check that the max production of the boiler is still less than peak heating load times thermal factor - # factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] - # boiler_capacity = maximum(load_boiler_thermal) * factor - # @test maximum(boiler_total) <= boiler_capacity - # end - - # @testset "All heating supply/demand/storage energy balance" begin - # """ - # Validation to ensure that: - # 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included - # 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption - # 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False + #also test CO2 breakeven cost + inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] + inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] + inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] + inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] + inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] + inputs["Settings"]["include_climate_in_objective"] = true + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt([m1, m2], inputs) + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 + elseif i == 3 + @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 + @test !haskey(results, "Wind") + @test !haskey(results, "ElectricStorage") + @test !haskey(results, "Generator") + @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 + @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 + @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 + @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 + yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] + nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) + TONNE_PER_LB = 1/2204.62 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 + @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 + @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 + @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 + @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 + @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 + KWH_PER_MMBTU = 293.07107 + annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] + annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU + @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 + end + end + end + + @testset "Back pressure steam turbine" begin + """ + Validation to ensure that: + 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application + 2) SteamTurbine serves the heating load with the condensing steam + + """ + # Setup inputs, make heating load large to entice SteamTurbine + input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + latitude = input_data["Site"]["latitude"] + longitude = input_data["Site"]["longitude"] + building = "Hospital" + elec_load_multiplier = 5.0 + heat_load_multiplier = 100.0 + input_data["ElectricLoad"]["doe_reference_name"] = building + input_data["SpaceHeatingLoad"]["doe_reference_name"] = building + input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building + elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) + input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) + space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], inputs) + + # The expected values below were directly copied from the REopt_API V2 expected values + @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 + @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 + @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 + @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 + @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 + @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 + + # BAU boiler loads + load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency + load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency + + # ExistingBoiler and SteamTurbine production + boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] + boiler_total = boiler_to_load + boiler_to_st + st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] + + # Fuel/thermal **consumption** + boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + + # Check that all thermal supply to load meets the BAU load + thermal_to_load = sum(boiler_to_load) + sum(st_to_load) + @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 + + # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet + steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] + net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) + @test net_electric_efficiency ≈ 0.052 atol=0.005 + + # Check that the max production of the boiler is still less than peak heating load times thermal factor + factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] + boiler_capacity = maximum(load_boiler_thermal) * factor + @test maximum(boiler_total) <= boiler_capacity + end + + @testset "All heating supply/demand/storage energy balance" begin + """ + Validation to ensure that: + 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included + 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption + 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False - # :return: - # """ + :return: + """ - # # Start with steam turbine inputs, but adding a bunch below - # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - # # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - # delete!(input_data, "DomesticHotWaterLoad") + # Start with steam turbine inputs, but adding a bunch below + input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + delete!(input_data, "DomesticHotWaterLoad") - # # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances - # input_data["SteamTurbine"]["min_kw"] = 30.0 - # input_data["SteamTurbine"]["max_kw"] = 30.0 + # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances + input_data["SteamTurbine"]["min_kw"] = 30.0 + input_data["SteamTurbine"]["max_kw"] = 30.0 - # # Add CHP - # input_data["CHP"] = Dict{Any, Any}([ - # ("prime_mover", "recip_engine"), - # ("size_class", 4), - # ("min_kw", 250.0), - # ("min_allowable_kw", 0.0), - # ("max_kw", 250.0), - # ("can_supply_steam_turbine", false), - # ("fuel_cost_per_mmbtu", 8.0), - # ("cooling_thermal_factor", 1.0) - # ]) + # Add CHP + input_data["CHP"] = Dict{Any, Any}([ + ("prime_mover", "recip_engine"), + ("size_class", 4), + ("min_kw", 250.0), + ("min_allowable_kw", 0.0), + ("max_kw", 250.0), + ("can_supply_steam_turbine", false), + ("fuel_cost_per_mmbtu", 8.0), + ("cooling_thermal_factor", 1.0) + ]) - # input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 + input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 - # # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) - # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") - # input_data["AbsorptionChiller"] = Dict{Any, Any}([ - # ("min_ton", 600.0), - # ("max_ton", 600.0), - # ("cop_thermal", 0.7), - # ("installed_cost_per_ton", 500.0), - # ("om_cost_per_ton", 0.5) - # ]) + # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) + input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") + input_data["AbsorptionChiller"] = Dict{Any, Any}([ + ("min_ton", 600.0), + ("max_ton", 600.0), + ("cop_thermal", 0.7), + ("installed_cost_per_ton", 500.0), + ("om_cost_per_ton", 0.5) + ]) - # # Add Hot TES - # input_data["HotThermalStorage"] = Dict{Any, Any}([ - # ("min_gal", 50000.0), - # ("max_gal", 50000.0) - # ]) + # Add Hot TES + input_data["HotThermalStorage"] = Dict{Any, Any}([ + ("min_gal", 50000.0), + ("max_gal", 50000.0) + ]) - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) + s = Scenario(input_data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) - # thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] - # thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech - # tech_to_thermal_load = Dict{Any, Any}() - # for tech in thermal_techs - # tech_to_thermal_load[tech] = Dict{Any, Any}() - # for load in thermal_loads - # if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") - # tech_to_thermal_load[tech][load] = [0.0] * 8760 - # else - # if load == "waste" - # tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] - # else - # tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] - # end - # end - # end - # end - # # Hot TES is the other thermal supply - # hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] + thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech + tech_to_thermal_load = Dict{Any, Any}() + for tech in thermal_techs + tech_to_thermal_load[tech] = Dict{Any, Any}() + for load in thermal_loads + if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") + tech_to_thermal_load[tech][load] = [0.0] * 8760 + else + if load == "waste" + tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] + else + tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] + end + end + end + end + # Hot TES is the other thermal supply + hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - # # BAU boiler loads - # load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU - # load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] + # BAU boiler loads + load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU + load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] - # # Fuel/thermal **consumption** - # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - # chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] - # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - # absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + # Fuel/thermal **consumption** + boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] + steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - # # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked - # alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) - # thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) - # @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 + # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked + alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) + thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) + @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 - # # Check that all thermal to steam turbine is equal to steam turbine thermal consumption - # alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) - # @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 + # Check that all thermal to steam turbine is equal to steam turbine thermal consumption + alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) + @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 - # # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False - # for tech in ["ExistingBoiler", "CHP"] - # if !(tech in inputs.techs.can_supply_steam_turbine) - # @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 - # end - # end - # end - - # @testset "Electric Heater" begin - # d = JSON.parsefile("./scenarios/electric_heater.json") - # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - - # #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased - # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 - # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 + # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False + for tech in ["ExistingBoiler", "CHP"] + if !(tech in inputs.techs.can_supply_steam_turbine) + @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 + end + end + end + + @testset "Electric Heater" begin + d = JSON.parsefile("./scenarios/electric_heater.json") + d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + + #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased + @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 + @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 - # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 - # d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 - # d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - - # annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour - # annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP - # annual_energy_supplied = 87600 + annual_electric_heater_consumption - - # #Second run: ElectricHeater produces the required heat with free electricity - # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 - # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 - # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 - - # end - - # @testset "Custom REopt logger" begin + d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 + d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 + d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + + annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour + annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP + annual_energy_supplied = 87600 + annual_electric_heater_consumption + + #Second run: ElectricHeater produces the required heat with free electricity + @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 + @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 + @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 + + end + + @testset "Custom REopt logger" begin - # # Throw a handled error - # d = JSON.parsefile("./scenarios/logger.json") - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - # @test r["Messages"]["has_stacktrace"] == false - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # # Type is dict when errors, otherwise type REoptInputs - # @test isa(REoptInputs(d), Dict) - - # # Using filepath - # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([n1,n2], "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(n, "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging - # d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" - # d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # # Type is dict when errors, otherwise type REoptInputs - # @test isa(REoptInputs(d), Dict) - - # # Using filepath - # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([n1,n2], "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(n, "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - # end - # end + # Throw a handled error + d = JSON.parsefile("./scenarios/logger.json") + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + @test r["Messages"]["has_stacktrace"] == false + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + # Type is dict when errors, otherwise type REoptInputs + @test isa(REoptInputs(d), Dict) + + # Using filepath + n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([n1,n2], "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(n, "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging + d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" + d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + # Type is dict when errors, otherwise type REoptInputs + @test isa(REoptInputs(d), Dict) + + # Using filepath + n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([n1,n2], "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(n, "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + end + end end From 51a7ff92864344a45d0ee5a52a7b9e86020ffdc5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 13:09:56 -0600 Subject: [PATCH 031/151] rm self discharge test now dupe --- test/runtests.jl | 26 -------------------------- 1 file changed, 26 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index fe61d48de..c5f464813 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -552,32 +552,6 @@ else # run HiGHS tests sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 end - @testset "Electric Storage Self-Discharge" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/storage_om.json") - - data["ElectricStorage"]["soc_init_fraction"] = 1.0 - data["ElectricStorage"]["soc_min_fraction"] = 0.0 - data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 - - s = Scenario(data) - inputs = REoptInputs(s) - results_self_discharge = run_reopt(model, inputs) - - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0 - - s = Scenario(data) - inputs = REoptInputs(s) - results_no_self_discharge = run_reopt(model, inputs) - - @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 - @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 - @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - - sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 - - end - @testset "Imported Xpress Test Suite" begin @testset "Heating loads and addressable load fraction" begin # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler From 1d3181f5b9f1f3b2c12d6d435e2565c64183b33f Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 30 May 2024 13:11:36 -0600 Subject: [PATCH 032/151] Update CHANGELOG.md --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 06e74e7f5..827fbd5e9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -28,7 +28,7 @@ Classify the change according to the following categories: - Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M - Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added new input **self_discharge_fraction_per_timestep** to `ElectricStorage` for modeling battery self-discharge -- Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` +- Added testset **Electric Storage O&M and Self-Discharge** in `test/runtests.jl` ## v0.45.0 ### Fixed From d6b9b6ebd711fdc4f0b2e7166b4f7de983fff736 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 12:40:16 -0600 Subject: [PATCH 033/151] comment out battery charge/discharge binary --- src/constraints/storage_constraints.jl | 16 ++++++++-------- src/core/reopt.jl | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 91a04a102..aed91d828 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -121,14 +121,14 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") end # Prevent charging and discharging of the battery at the same time - @constraint(m, [ts in p.time_steps], - m[Symbol("dvGridToStorage"*_n)][b, ts] + - sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) - @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid")][ts] + - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) + # @constraint(m, [ts in p.time_steps], + # m[Symbol("dvGridToStorage"*_n)][b, ts] + + # sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= + # p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) + # @constraint(m, [ts in p.time_steps], + # m[Symbol("dvStorageToGrid")][ts] + + # m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= + # p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 9cc24a610..1755ca295 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -217,7 +217,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) end else # Additional variables for exporting storage energy to the grid - @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) + # @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) From 2b076895b38672258a6928620fb72c38e55941a5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 12:40:47 -0600 Subject: [PATCH 034/151] comment out added battery power constraint --- src/constraints/storage_constraints.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index aed91d828..5e8cf5d12 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -83,9 +83,9 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + m[Symbol("dvStorageToGrid")][ts] ) - #Dispatch from electrical storage is no greater than power capacity - @constraint(m, [ts in p.time_steps_without_grid], - m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][ts]) + # #Dispatch from electrical storage is no greater than power capacity + # @constraint(m, [ts in p.time_steps_without_grid], + # m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][ts]) #Constraint (4l)-alt: Dispatch from electrical storage is no greater than power capacity (no grid connection) @constraint(m, [ts in p.time_steps_without_grid], From c6ff6860eb86da33b28145e540d5d98e1566eda2 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 12:43:11 -0600 Subject: [PATCH 035/151] comment out dvStorageToGrid --- src/constraints/electric_utility_constraints.jl | 2 +- src/constraints/storage_constraints.jl | 7 +++++-- src/results/electric_storage.jl | 2 +- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 6038710d5..fdd9653c3 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -120,7 +120,7 @@ function add_export_constraints(m, p; _n="") # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) sum(p.s.electric_tariff.export_rates[:WHL][ts] * ( - m[Symbol("dvStorageToGrid")][ts] + + # m[Symbol("dvStorageToGrid")][ts] + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) ) for ts in p.time_steps ) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 5e8cf5d12..4a35d9df3 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -43,7 +43,7 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") #Constraint (4j): Dispatch from storage is no greater than power capacity @constraint(m, [ts in p.time_steps], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] - + m[Symbol("dvStorageToGrid")][ts] # TODO: add "b" index to this decision variable + # + m[Symbol("dvStorageToGrid")][ts] # TODO: add "b" index to this decision variable ) end @@ -57,7 +57,10 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) + - ( + m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + # + m[Symbol("dvStorageToGrid")][ts] + ) / p.s.storage.attr[b].discharge_efficiency ) ) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 4dc8ee84c..f06d710aa 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -50,7 +50,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")]), digits = 3) + # r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")]), digits = 3) else r["soc_series_fraction"] = [] From 0b1618bf6be5face07821f7ce299ff208bf6a499 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 12:45:22 -0600 Subject: [PATCH 036/151] Revert "comment out dvStorageToGrid" This reverts commit c6ff6860eb86da33b28145e540d5d98e1566eda2. --- src/constraints/electric_utility_constraints.jl | 2 +- src/constraints/storage_constraints.jl | 7 ++----- src/results/electric_storage.jl | 2 +- 3 files changed, 4 insertions(+), 7 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index fdd9653c3..6038710d5 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -120,7 +120,7 @@ function add_export_constraints(m, p; _n="") # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) sum(p.s.electric_tariff.export_rates[:WHL][ts] * ( - # m[Symbol("dvStorageToGrid")][ts] + + m[Symbol("dvStorageToGrid")][ts] + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) ) for ts in p.time_steps ) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 4a35d9df3..5e8cf5d12 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -43,7 +43,7 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") #Constraint (4j): Dispatch from storage is no greater than power capacity @constraint(m, [ts in p.time_steps], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] - # + m[Symbol("dvStorageToGrid")][ts] # TODO: add "b" index to this decision variable + + m[Symbol("dvStorageToGrid")][ts] # TODO: add "b" index to this decision variable ) end @@ -57,10 +57,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - - ( - m[Symbol("dvDischargeFromStorage"*_n)][b,ts] - # + m[Symbol("dvStorageToGrid")][ts] - ) / p.s.storage.attr[b].discharge_efficiency + - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) ) ) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index f06d710aa..4dc8ee84c 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -50,7 +50,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - # r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")]), digits = 3) + r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")]), digits = 3) else r["soc_series_fraction"] = [] From eb6ed96d19b8df0cdb92f0b3ab4e6b279a050204 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 13:13:44 -0600 Subject: [PATCH 037/151] comment out dvStorageToGrid in WHL_benefit expression --- src/constraints/electric_utility_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 6038710d5..fdd9653c3 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -120,7 +120,7 @@ function add_export_constraints(m, p; _n="") # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) sum(p.s.electric_tariff.export_rates[:WHL][ts] * ( - m[Symbol("dvStorageToGrid")][ts] + + # m[Symbol("dvStorageToGrid")][ts] + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) ) for ts in p.time_steps ) From c2739720b5c6c7f5af7c74ce4e4932c581313364 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 13:13:53 -0600 Subject: [PATCH 038/151] Revert "comment out added battery power constraint" This reverts commit 2b076895b38672258a6928620fb72c38e55941a5. --- src/constraints/storage_constraints.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 5e8cf5d12..aed91d828 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -83,9 +83,9 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + m[Symbol("dvStorageToGrid")][ts] ) - # #Dispatch from electrical storage is no greater than power capacity - # @constraint(m, [ts in p.time_steps_without_grid], - # m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][ts]) + #Dispatch from electrical storage is no greater than power capacity + @constraint(m, [ts in p.time_steps_without_grid], + m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][ts]) #Constraint (4l)-alt: Dispatch from electrical storage is no greater than power capacity (no grid connection) @constraint(m, [ts in p.time_steps_without_grid], From 3a8834a89a5325480de97617133e7b80c25cca99 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 13:14:10 -0600 Subject: [PATCH 039/151] Revert "comment out battery charge/discharge binary" This reverts commit d6b9b6ebd711fdc4f0b2e7166b4f7de983fff736. --- src/constraints/storage_constraints.jl | 16 ++++++++-------- src/core/reopt.jl | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index aed91d828..91a04a102 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -121,14 +121,14 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") end # Prevent charging and discharging of the battery at the same time - # @constraint(m, [ts in p.time_steps], - # m[Symbol("dvGridToStorage"*_n)][b, ts] + - # sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - # p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) - # @constraint(m, [ts in p.time_steps], - # m[Symbol("dvStorageToGrid")][ts] + - # m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - # p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvGridToStorage"*_n)][b, ts] + + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= + p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvStorageToGrid")][ts] + + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= + p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 1755ca295..9cc24a610 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -217,7 +217,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) end else # Additional variables for exporting storage energy to the grid - # @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) + @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) From 134adb6f86cec65ec162efa484c9c9245fbb18b3 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 31 May 2024 18:05:08 -0600 Subject: [PATCH 040/151] add battery max_kw to bigM_hourly_load --- src/constraints/electric_utility_constraints.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index fdd9653c3..4aa64134f 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -120,7 +120,7 @@ function add_export_constraints(m, p; _n="") # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) sum(p.s.electric_tariff.export_rates[:WHL][ts] * ( - # m[Symbol("dvStorageToGrid")][ts] + + m[Symbol("dvStorageToGrid")][ts] + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) ) for ts in p.time_steps ) @@ -266,7 +266,7 @@ function add_simultaneous_export_import_constraint(m, p; _n="") } ) else - bigM_hourly_load = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal) + bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+p.s.storage.attr["ElectricStorage"].max_kw @constraint(m, NoGridPurchasesBinary[ts in p.time_steps], sum(m[Symbol("dvGridPurchase"*_n)][ts, tier] for tier in 1:p.s.electric_tariff.n_energy_tiers) + sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) <= bigM_hourly_load*(1-m[Symbol("binNoGridPurchases"*_n)][ts]) From 5db75ae63af3a9877be58c89e31676b01d5ccd6c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 11:40:16 -0600 Subject: [PATCH 041/151] change to bigM_hourly_load_plus_battery_power where used --- src/constraints/electric_utility_constraints.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 4aa64134f..a7b7c4bf8 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -269,10 +269,10 @@ function add_simultaneous_export_import_constraint(m, p; _n="") bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+p.s.storage.attr["ElectricStorage"].max_kw @constraint(m, NoGridPurchasesBinary[ts in p.time_steps], sum(m[Symbol("dvGridPurchase"*_n)][ts, tier] for tier in 1:p.s.electric_tariff.n_energy_tiers) + - sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) <= bigM_hourly_load*(1-m[Symbol("binNoGridPurchases"*_n)][ts]) + sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) <= bigM_hourly_load_plus_battery_power*(1-m[Symbol("binNoGridPurchases"*_n)][ts]) ) @constraint(m, ExportOnlyAfterSiteLoadMetCon[ts in p.time_steps], - sum(m[Symbol("dvProductionToGrid"*_n)][t,u,ts] for t in p.techs.elec, u in p.export_bins_by_tech[t]) <= bigM_hourly_load * m[Symbol("binNoGridPurchases"*_n)][ts] + sum(m[Symbol("dvProductionToGrid"*_n)][t,u,ts] for t in p.techs.elec, u in p.export_bins_by_tech[t]) <= bigM_hourly_load_plus_battery_power * m[Symbol("binNoGridPurchases"*_n)][ts] ) end end From 0f13d77cc1e4dc1fd44ae160668cd0315de65101 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 16:43:55 -0600 Subject: [PATCH 042/151] add b index to dvStorageToGrid --- src/constraints/electric_utility_constraints.jl | 2 +- src/constraints/storage_constraints.jl | 10 +++++----- src/core/reopt.jl | 4 ++-- src/results/electric_storage.jl | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index a7b7c4bf8..143376023 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -120,7 +120,7 @@ function add_export_constraints(m, p; _n="") # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) sum(p.s.electric_tariff.export_rates[:WHL][ts] * ( - m[Symbol("dvStorageToGrid")][ts] + + sum(m[Symbol("dvStorageToGrid")][b, ts] for b in p.s.storage.types.elec) + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) ) for ts in p.time_steps ) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 91a04a102..f4aef9d59 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -43,7 +43,7 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") #Constraint (4j): Dispatch from storage is no greater than power capacity @constraint(m, [ts in p.time_steps], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] - + m[Symbol("dvStorageToGrid")][ts] # TODO: add "b" index to this decision variable + + m[Symbol("dvStorageToGrid")][b, ts] # TODO: add "b" index to this decision variable ) end @@ -57,7 +57,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][ts]) / p.s.storage.attr[b].discharge_efficiency) + - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][b, ts]) / p.s.storage.attr[b].discharge_efficiency) ) ) @@ -80,12 +80,12 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, [ts in p.time_steps_with_grid], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + m[Symbol("dvGridToStorage"*_n)][b, ts] - + m[Symbol("dvStorageToGrid")][ts] + + m[Symbol("dvStorageToGrid")][b, ts] ) #Dispatch from electrical storage is no greater than power capacity @constraint(m, [ts in p.time_steps_without_grid], - m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][ts]) + m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][b, ts]) #Constraint (4l)-alt: Dispatch from electrical storage is no greater than power capacity (no grid connection) @constraint(m, [ts in p.time_steps_without_grid], @@ -126,7 +126,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid")][ts] + + m[Symbol("dvStorageToGrid")][b, ts] + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 9cc24a610..0770eeb9e 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -213,7 +213,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) @constraint(m, [ts in p.time_steps], m[:dvGridToStorage][b, ts] == 0) @constraint(m, [t in p.techs.elec, ts in p.time_steps_with_grid], m[:dvProductionToStorage][b, t, ts] == 0) - @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid")][ts] == 0) # if there isn't a battery, then the battery can't export power to the grid + @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid")][b, ts] == 0) # if there isn't a battery, then the battery can't export power to the grid end else # Additional variables for exporting storage energy to the grid @@ -571,7 +571,7 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) dvPeakDemandMonth[p.months, 1:p.s.electric_tariff.n_monthly_demand_tiers] >= 0 # Peak electrical power demand during month m [kW] MinChargeAdder >= 0 binGHP[p.ghp_options], Bin # Can be <= 1 if require_ghp_purchase=0, and is ==1 if require_ghp_purchase=1 - dvStorageToGrid[p.time_steps] >= 0 # TODO, add: "p.StorageSalesTiers" as well? export of energy from storage to the grid + dvStorageToGrid[p.s.storage.types.elec, p.time_steps] >= 0 # TODO, add: "p.StorageSalesTiers" as well? export of energy from storage to the grid end diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 4dc8ee84c..75ff6a79c 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -50,7 +50,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")]), digits = 3) + r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")][b, :]), digits = 3) else r["soc_series_fraction"] = [] From 7d2694edc6cb49549988999793fdb5671e68bc60 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 16:58:55 -0600 Subject: [PATCH 043/151] add dvStorageToGrid to mpc model --- src/mpc/model.jl | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/mpc/model.jl b/src/mpc/model.jl index dc8145033..7eabc8e82 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -99,6 +99,7 @@ function build_mpc!(m::JuMP.AbstractModel, p::MPCInputs) @constraint(m, [ts in p.time_steps], m[:dvDischargeFromStorage][b, ts] == 0) if b in p.s.storage.types.elec @constraint(m, [ts in p.time_steps], m[:dvGridToStorage][b, ts] == 0) + @constraint(m, [ts in p.time_steps], m[:dvStorageToGrid][b, ts] == 0) end else add_general_storage_dispatch_constraints(m, p, b) @@ -230,6 +231,7 @@ function add_variables!(m::JuMP.AbstractModel, p::MPCInputs) dvCurtail[p.techs.all, p.time_steps] >= 0 # [kW] dvProductionToStorage[p.s.storage.types.all, p.techs.all, p.time_steps] >= 0 # Power from technology t used to charge storage system b [kW] dvDischargeFromStorage[p.s.storage.types.all, p.time_steps] >= 0 # Power discharged from storage system b [kW] + dvStorageToGrid[p.s.storage.types.elec, p.time_steps] >= 0 # TODO, add: "p.StorageSalesTiers" as well? export of energy from storage to the grid dvGridToStorage[p.s.storage.types.elec, p.time_steps] >= 0 # Electrical power delivered to storage by the grid [kW] dvStoredEnergy[p.s.storage.types.all, 0:p.time_steps[end]] >= 0 # State of charge of storage system b dvStoragePower[p.s.storage.types.all] >= 0 # Power capacity of storage system b [kW] From 31e912be25873125a50fd070794514132d1c4505 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 17:21:55 -0600 Subject: [PATCH 044/151] is_ldes and duration -> fixed_duration --- src/constraints/storage_constraints.jl | 4 ++-- src/core/energy_storage/electric_storage.jl | 12 ++++-------- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index f4aef9d59..b684b5003 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -108,8 +108,8 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") ) end - if p.s.storage.attr[b].is_ldes - @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].duration) + if !isnothing(p.s.storage.attr[b].fixed_duration) + @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end if p.s.storage.attr[b].require_start_and_end_charge_to_be_equal diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index dbc4a5772..31fa001a9 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -168,8 +168,7 @@ end degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 self_discharge_fraction_per_timestep::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep - is_ldes::Bool = false - duration::Real = 100.0 + fixed_duration::Union{Real, Nothing} = nothing require_start_and_end_charge_to_be_equal::Bool = false ``` """ @@ -207,8 +206,7 @@ Base.@kwdef struct ElectricStorageDefaults degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 self_discharge_fraction_per_timestep::Float64 = 0.0 - is_ldes::Bool = false - duration::Real = 100.0 + fixed_duration::Union{Real, Nothing} = nothing require_start_and_end_charge_to_be_equal::Bool = false end @@ -254,8 +252,7 @@ struct ElectricStorage <: AbstractElectricStorage degradation::Degradation minimum_avg_soc_fraction::Float64 self_discharge_fraction_per_timestep::Float64 - is_ldes::Bool - duration::Real + fixed_duration::Union{Real, Nothing} require_start_and_end_charge_to_be_equal::Bool function ElectricStorage(d::Dict, f::Financial) @@ -348,8 +345,7 @@ struct ElectricStorage <: AbstractElectricStorage degr, s.minimum_avg_soc_fraction, s.self_discharge_fraction_per_timestep, - s.is_ldes, - s.duration, + s.fixed_duration, s.require_start_and_end_charge_to_be_equal ) end From 0d37b7c6f0009c7cb7eeca7957b0f685090b51a3 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 17:22:39 -0600 Subject: [PATCH 045/151] account for fixed_duration not in MPCElectricStorage --- src/constraints/storage_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index b684b5003..0ca632696 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -108,7 +108,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") ) end - if !isnothing(p.s.storage.attr[b].fixed_duration) + if p.s.storage.attr[b] isa ElectricStorage && !isnothing(p.s.storage.attr[b].fixed_duration) @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end From 84293bd99e2734369f51216339d0cd98aef1d869 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 17:23:27 -0600 Subject: [PATCH 046/151] account for require_start_and_end_charge_to_be_equal not in MPCElectricStorage --- src/constraints/storage_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 0ca632696..e3a0e925a 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -112,7 +112,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end - if p.s.storage.attr[b].require_start_and_end_charge_to_be_equal + if p.s.storage.attr[b] isa ElectricStorage && p.s.storage.attr[b].require_start_and_end_charge_to_be_equal # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) print("\nSetting initial SOC = final SOC\n") @constraint(m, From 3bd6e24eef641e845dde9c728bbbf9a20c4925b0 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:12:12 -0600 Subject: [PATCH 047/151] add allow_simultaneous_charge_discharge input to ElectricStorage --- src/core/energy_storage/electric_storage.jl | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 31fa001a9..cb4dedebb 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -170,6 +170,7 @@ end self_discharge_fraction_per_timestep::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep fixed_duration::Union{Real, Nothing} = nothing require_start_and_end_charge_to_be_equal::Bool = false + allow_simultaneous_charge_discharge::Bool = true # Simultaneous charge/discharge is typically suboptimal anyway and allowing this avoids additional binary variables (which can slow solve time) ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -208,6 +209,7 @@ Base.@kwdef struct ElectricStorageDefaults self_discharge_fraction_per_timestep::Float64 = 0.0 fixed_duration::Union{Real, Nothing} = nothing require_start_and_end_charge_to_be_equal::Bool = false + allow_simultaneous_charge_discharge::Bool = true end @@ -254,6 +256,7 @@ struct ElectricStorage <: AbstractElectricStorage self_discharge_fraction_per_timestep::Float64 fixed_duration::Union{Real, Nothing} require_start_and_end_charge_to_be_equal::Bool + allow_simultaneous_charge_discharge::Bool function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -347,6 +350,7 @@ struct ElectricStorage <: AbstractElectricStorage s.self_discharge_fraction_per_timestep, s.fixed_duration, s.require_start_and_end_charge_to_be_equal + s.allow_simultaneous_charge_discharge ) end end From d0b467111e3dc208d53622652f19abe70fdfeb3b Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:13:41 -0600 Subject: [PATCH 048/151] move def of binBattCharging to be with rest of variable defs --- src/core/reopt.jl | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 0770eeb9e..42c9f356e 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -216,9 +216,6 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid")][b, ts] == 0) # if there isn't a battery, then the battery can't export power to the grid end else - # Additional variables for exporting storage energy to the grid - @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) - add_storage_size_constraints(m, p, b) add_general_storage_dispatch_constraints(m, p, b) if b in p.s.storage.types.elec @@ -575,6 +572,12 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) end + for b in p.s.storage.types.elec + if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) + @warn "Adding binary variable to prevent simultaneous battery charge/discharge. Some solvers are very slow with integer variables." + @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) + end + end if !isempty(p.techs.gen) # Problem becomes a MILP @warn "Adding binary variable to model gas generator. Some solvers are very slow with integer variables." @@ -592,7 +595,7 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) end if !(p.s.electric_utility.allow_simultaneous_export_import) & !isempty(p.s.electric_tariff.export_bins) - @warn "Adding binary variable to prevent simultaneous grid import/export. Some solvers are very slow with integer variables" + @warn "Adding binary variable to prevent simultaneous grid import/export. Some solvers are very slow with integer variables." @variable(m, binNoGridPurchases[p.time_steps], Bin) end @@ -612,7 +615,7 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) end if !isempty(p.s.electric_utility.outage_durations) # add dvUnserved Load if there is at least one outage - @warn "Adding binary variable to model outages. Some solvers are very slow with integer variables" + @warn "Adding binary variable to model outages. Some solvers are very slow with integer variables." max_outage_duration = maximum(p.s.electric_utility.outage_durations) outage_time_steps = p.s.electric_utility.outage_time_steps tZeros = p.s.electric_utility.outage_start_time_steps From f34f44c460450d8f5d98df5aef25cd7faf15fd28 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:14:30 -0600 Subject: [PATCH 049/151] missing comma --- src/core/energy_storage/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index cb4dedebb..dc645f738 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -349,7 +349,7 @@ struct ElectricStorage <: AbstractElectricStorage s.minimum_avg_soc_fraction, s.self_discharge_fraction_per_timestep, s.fixed_duration, - s.require_start_and_end_charge_to_be_equal + s.require_start_and_end_charge_to_be_equal, s.allow_simultaneous_charge_discharge ) end From 0336a2dbe7ce2275140177a98a54b6bbb4936c52 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:18:57 -0600 Subject: [PATCH 050/151] only prevent simultaneous battery charge/discharge when that input is false --- src/constraints/storage_constraints.jl | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index e3a0e925a..68ac83b3a 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -121,15 +121,17 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") end # Prevent charging and discharging of the battery at the same time - @constraint(m, [ts in p.time_steps], - m[Symbol("dvGridToStorage"*_n)][b, ts] + - sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) - @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid")][b, ts] + - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) - + if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) + #TODO: implement indicator constraint version for solvers that support it + @constraint(m, [ts in p.time_steps], + m[Symbol("dvGridToStorage"*_n)][b, ts] + + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= + p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvStorageToGrid")][b, ts] + + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= + p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) + end end From 300c95e6374edd3c9dbcda2703579da2d4ac65ac Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:25:15 -0600 Subject: [PATCH 051/151] add allow_simultaneous_charge_discharge to MPCElectricStorage --- src/mpc/structs.jl | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index 0ec508176..c886251e4 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -227,6 +227,7 @@ Base.@kwdef struct MPCElectricStorage < AbstractElectricStorage soc_init_fraction::Float64 = 0.5 can_grid_charge::Bool = true grid_charge_efficiency::Float64 = 0.96 * 0.975^2 + allow_simultaneous_charge_discharge::Bool = true end ``` """ @@ -243,6 +244,7 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage max_kwh::Float64 = size_kwh minimum_avg_soc_fraction::Float64 = 0.0 self_discharge_fraction_per_timestep::Float64 = 0.0 + allow_simultaneous_charge_discharge::Bool = true end From 9f4df445615cd5ef269afb7df62c89f4ec9e04e1 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:37:25 -0600 Subject: [PATCH 052/151] rm dvStorageToGrid from general storage discharge power constraint it's in the elec version of this constraint --- src/constraints/storage_constraints.jl | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 68ac83b3a..d842b6e9c 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -43,8 +43,7 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") #Constraint (4j): Dispatch from storage is no greater than power capacity @constraint(m, [ts in p.time_steps], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] - + m[Symbol("dvStorageToGrid")][b, ts] # TODO: add "b" index to this decision variable - ) + ) end From 5f6428477fea10b31832c44d5533a1f130e7bb36 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 4 Jun 2024 19:04:22 -0600 Subject: [PATCH 053/151] make dvStorageToGrid multinode compat --- src/constraints/electric_utility_constraints.jl | 2 +- src/constraints/storage_constraints.jl | 8 ++++---- src/core/reopt.jl | 6 +++--- src/core/reopt_multinode.jl | 4 ++-- src/mpc/model_multinode.jl | 3 ++- src/results/electric_storage.jl | 2 +- 6 files changed, 13 insertions(+), 12 deletions(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 143376023..ea485bc21 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -120,7 +120,7 @@ function add_export_constraints(m, p; _n="") # for t in p.techs_by_exportbin[:WHL]) for ts in p.time_steps) sum(p.s.electric_tariff.export_rates[:WHL][ts] * ( - sum(m[Symbol("dvStorageToGrid")][b, ts] for b in p.s.storage.types.elec) + + sum(m[Symbol("dvStorageToGrid"*_n)][b, ts] for b in p.s.storage.types.elec) + sum(m[Symbol("dvProductionToGrid"*_n)][t, :WHL, ts] for t in p.techs_by_exportbin[:WHL]) ) for ts in p.time_steps ) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index d842b6e9c..2d37c85a7 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -56,7 +56,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid")][b, ts]) / p.s.storage.attr[b].discharge_efficiency) + - ((m[Symbol("dvDischargeFromStorage"*_n)][b,ts]+m[Symbol("dvStorageToGrid"*_n)][b, ts]) / p.s.storage.attr[b].discharge_efficiency) ) ) @@ -79,12 +79,12 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, [ts in p.time_steps_with_grid], m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b, ts] + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + m[Symbol("dvGridToStorage"*_n)][b, ts] - + m[Symbol("dvStorageToGrid")][b, ts] + + m[Symbol("dvStorageToGrid"*_n)][b, ts] ) #Dispatch from electrical storage is no greater than power capacity @constraint(m, [ts in p.time_steps_without_grid], - m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid")][b, ts]) + m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + m[Symbol("dvStorageToGrid"*_n)][b, ts]) #Constraint (4l)-alt: Dispatch from electrical storage is no greater than power capacity (no grid connection) @constraint(m, [ts in p.time_steps_without_grid], @@ -127,7 +127,7 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid")][b, ts] + + m[Symbol("dvStorageToGrid"*_n)][b, ts] + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) end diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 42c9f356e..ecfb85391 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -213,7 +213,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) @constraint(m, [ts in p.time_steps], m[:dvGridToStorage][b, ts] == 0) @constraint(m, [t in p.techs.elec, ts in p.time_steps_with_grid], m[:dvProductionToStorage][b, t, ts] == 0) - @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid")][b, ts] == 0) # if there isn't a battery, then the battery can't export power to the grid + @constraint(m, [ts in p.time_steps], m[Symbol("dvStorageToGrid"*_n)][b, ts] == 0) # if there isn't a battery, then the battery can't export power to the grid end else add_storage_size_constraints(m, p, b) @@ -561,6 +561,7 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) dvProductionToStorage[p.s.storage.types.all, p.techs.all, p.time_steps] >= 0 # Power from technology t used to charge storage system b [kW] dvDischargeFromStorage[p.s.storage.types.all, p.time_steps] >= 0 # Power discharged from storage system b [kW] dvGridToStorage[p.s.storage.types.elec, p.time_steps] >= 0 # Electrical power delivered to storage by the grid [kW] + dvStorageToGrid[p.s.storage.types.elec, p.time_steps] >= 0 # TODO, add: "p.StorageSalesTiers" as well? export of energy from storage to the grid dvStoredEnergy[p.s.storage.types.all, 0:p.time_steps[end]] >= 0 # State of charge of storage system b dvStoragePower[p.s.storage.types.all] >= 0 # Power capacity of storage system b [kW] dvStorageEnergy[p.s.storage.types.all] >= 0 # Energy capacity of storage system b [kWh] @@ -568,8 +569,7 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) dvPeakDemandMonth[p.months, 1:p.s.electric_tariff.n_monthly_demand_tiers] >= 0 # Peak electrical power demand during month m [kW] MinChargeAdder >= 0 binGHP[p.ghp_options], Bin # Can be <= 1 if require_ghp_purchase=0, and is ==1 if require_ghp_purchase=1 - dvStorageToGrid[p.s.storage.types.elec, p.time_steps] >= 0 # TODO, add: "p.StorageSalesTiers" as well? export of energy from storage to the grid - + end for b in p.s.storage.types.elec diff --git a/src/core/reopt_multinode.jl b/src/core/reopt_multinode.jl index 240956f6e..7693e79b8 100644 --- a/src/core/reopt_multinode.jl +++ b/src/core/reopt_multinode.jl @@ -2,7 +2,6 @@ function add_variables!(m::JuMP.AbstractModel, ps::AbstractVector{REoptInputs{T}}) where T <: AbstractScenario - dvs_idx_on_techs = String[ "dvSize", "dvPurchaseSize", @@ -16,7 +15,8 @@ function add_variables!(m::JuMP.AbstractModel, ps::AbstractVector{REoptInputs{T} "dvStorageEnergy", ] dvs_idx_on_storagetypes_time_steps = String[ - "dvDischargeFromStorage" + "dvDischargeFromStorage", + "dvStorageToGrid" ] for p in ps _n = string("_", p.s.site.node) diff --git a/src/mpc/model_multinode.jl b/src/mpc/model_multinode.jl index ab71d5765..03d1677d1 100644 --- a/src/mpc/model_multinode.jl +++ b/src/mpc/model_multinode.jl @@ -152,7 +152,8 @@ function add_variables!(m::JuMP.AbstractModel, ps::AbstractVector{MPCInputs}) "dvRatedProduction", ] dvs_idx_on_storagetypes_time_steps = String[ - "dvDischargeFromStorage" + "dvDischargeFromStorage", + "dvStorageToGrid" ] for p in ps _n = string("_", p.s.node) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 75ff6a79c..9a4e154a0 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -50,7 +50,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid")][b, :]), digits = 3) + r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid"*_n)][b, :]), digits = 3) else r["soc_series_fraction"] = [] From 62124060240aee6deefb9f816e4333463f73630c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 20 Jun 2024 10:05:28 -0600 Subject: [PATCH 054/151] add more export incentive and om tax/period combos to results --- src/results/electric_storage.jl | 2 ++ src/results/electric_tariff.jl | 2 ++ 2 files changed, 4 insertions(+) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 9a4e154a0..95a8ef783 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -38,7 +38,9 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: p.s.storage.attr[b].om_cost_per_kwh * m[Symbol("dvStorageEnergy"*_n)][b]) r["lifecycle_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) + r["lifecycle_om_cost_before_tax"] = round(value(StoragePerUnitOMCosts), digits=0) r["year_one_om_cost_before_tax"] = round(value(StoragePerUnitOMCosts) / (p.pwf_om * p.third_party_factor), digits=0) + r["year_one_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction) / (p.pwf_om * p.third_party_factor), digits=0) if p.s.storage.attr[b].model_degradation r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])["ElectricStorage"]; diff --git a/src/results/electric_tariff.jl b/src/results/electric_tariff.jl index 8bc2c05a9..331c07a8e 100644 --- a/src/results/electric_tariff.jl +++ b/src/results/electric_tariff.jl @@ -36,7 +36,9 @@ function add_electric_tariff_results(m::JuMP.AbstractModel, p::REoptInputs, d::D r["year_one_min_charge_adder_before_tax"] = round(value(m[Symbol("MinChargeAdder"*_n)]) / p.pwf_e, digits=2) r["lifecycle_export_benefit_after_tax"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]) * (1 - p.s.financial.offtaker_tax_rate_fraction), digits=2) + r["lifecycle_export_benefit_before_tax"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]), digits=2) r["year_one_export_benefit_before_tax"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]) / p.pwf_e, digits=0) + r["year_one_export_benefit_after_tax"] = -1 * round(value(m[Symbol("TotalExportBenefit"*_n)]) * (1 - p.s.financial.offtaker_tax_rate_fraction) / p.pwf_e, digits=0) r["lifecycle_coincident_peak_cost_after_tax"] = round(value(m[Symbol("TotalCPCharges"*_n)]) * (1 - p.s.financial.offtaker_tax_rate_fraction), digits=2) r["year_one_coincident_peak_cost_before_tax"] = round(value(m[Symbol("TotalCPCharges"*_n)]) / p.pwf_e, digits=2) From 5eea67e32f026b78707d713b441ac4416fa908fe Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Mon, 8 Jul 2024 22:01:50 -0600 Subject: [PATCH 055/151] Add capacity-based self-discharge, update tests --- CHANGELOG.md | 3 ++- src/constraints/storage_constraints.jl | 6 ++++-- src/core/energy_storage/electric_storage.jl | 12 +++++++---- src/mpc/structs.jl | 3 ++- test/runtests.jl | 22 +++++++++++++++------ 5 files changed, 32 insertions(+), 14 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6e37b28a8..e09487631 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,7 +26,8 @@ Classify the change according to the following categories: ## Develop 2024-07-6 ### Added - Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M -- Added new input **per_timestep_self_discharge_fraction** to `ElectricStorage` for modeling battery self-discharge +- Added new input **soc_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling SOC-based battery self-discharge +- Added new input **capacity_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling capacity-based battery self-discharge - Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 33270d5f2..1a38ab58e 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -57,7 +57,8 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] - m[Symbol("dvDischargeFromStorage"*_n)][b,ts] / p.s.storage.attr[b].discharge_efficiency ) - - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].soc_based_per_ts_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].capacity_based_per_ts_self_discharge_fraction * m[Symbol("dvStorageEnergy"*_n)][b]) ) # Constraint (4h): state-of-charge for electrical storage - no grid @@ -66,7 +67,8 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.elec) - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] / p.s.storage.attr[b].discharge_efficiency ) - - (p.s.storage.attr[b].per_timestep_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].soc_based_per_ts_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].capacity_based_per_ts_self_discharge_fraction * m[Symbol("dvStorageEnergy"*_n)][b]) ) # Constraint (4i)-1: Dispatch to electrical storage is no greater than power capacity diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index a21ba780d..a99f0e3af 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -167,7 +167,8 @@ end model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - per_timestep_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep + capacity_based_per_ts_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on the rated kWh capacity of the sized storage system. + soc_based_per_ts_self_discharge_fraction::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -203,7 +204,8 @@ Base.@kwdef struct ElectricStorageDefaults model_degradation::Bool = false degradation::Dict = Dict() minimum_avg_soc_fraction::Float64 = 0.0 - per_timestep_self_discharge_fraction::Float64 = 0.0 + capacity_based_per_ts_self_discharge_fraction::Float64 = 0.0 + soc_based_per_ts_self_discharge_fraction::Float64 = 0.0 end @@ -247,7 +249,8 @@ struct ElectricStorage <: AbstractElectricStorage model_degradation::Bool degradation::Degradation minimum_avg_soc_fraction::Float64 - per_timestep_self_discharge_fraction::Float64 + capacity_based_per_ts_self_discharge_fraction::Float64 + soc_based_per_ts_self_discharge_fraction::Float64 function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -338,7 +341,8 @@ struct ElectricStorage <: AbstractElectricStorage s.model_degradation, degr, s.minimum_avg_soc_fraction, - s.per_timestep_self_discharge_fraction + s.capacity_based_per_ts_self_discharge_fraction, + s.soc_based_per_ts_self_discharge_fraction ) end end diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index b8e16d244..0c725d324 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -242,7 +242,8 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage max_kw::Float64 = size_kw max_kwh::Float64 = size_kwh minimum_avg_soc_fraction::Float64 = 0.0 - per_timestep_self_discharge_fraction::Float64 = 0.0 + capacity_based_per_ts_self_discharge_fraction::Float64 = 0.0 + soc_based_per_ts_self_discharge_fraction::Float64 = 0.0 end diff --git a/test/runtests.jl b/test/runtests.jl index 6beec90c7..be81c5040 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -589,23 +589,33 @@ else # run HiGHS tests data["ElectricStorage"]["soc_init_fraction"] = 1.0 data["ElectricStorage"]["soc_min_fraction"] = 0.0 - data["ElectricStorage"]["per_timestep_self_discharge_fraction"] = 0.0025/24 + data["ElectricStorage"]["soc_based_per_ts_self_discharge_fraction"] = 0.0025/24 s = Scenario(data) inputs = REoptInputs(s) - results_self_discharge = run_reopt(model, inputs) + results_soc_based_self_discharge = run_reopt(model, inputs) model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data["ElectricStorage"]["per_timestep_self_discharge_fraction"] = 0.0 + data["ElectricStorage"]["soc_based_per_ts_self_discharge_fraction"] = 0.0 s = Scenario(data) inputs = REoptInputs(s) results_no_self_discharge = run_reopt(model, inputs) - @test results_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 - @test results_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data["ElectricStorage"]["capacity_based_per_ts_self_discharge_fraction"] = 0.0025/24 + + s = Scenario(data) + inputs = REoptInputs(s) + results_capacity_based_self_discharge = run_reopt(model, inputs) + + @test results_soc_based_self_discharge["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 0 atol=0.01 + @test results_soc_based_self_discharge["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 0 atol=0.01 @test sum(results_no_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) - - sum(results_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + sum(results_soc_based_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + + calculated_output = round((80 - (80 * 0.0025/24 * 8760)) * sqrt(0.975) * 0.96, digits = 3) + @test sum(results_capacity_based_self_discharge["ElectricStorage"]["storage_to_load_series_kw"]) ≈ calculated_output atol=0.01 end @testset "Disaggregated Heating Loads" begin From 8ad69fda19fafe82086f8c536fe354d022734c1f Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 6 Aug 2024 16:56:32 -0600 Subject: [PATCH 056/151] add can_export_to_grid ElectricStorage input --- src/core/energy_storage/electric_storage.jl | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 270442a70..1e0ea2a8c 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -147,6 +147,7 @@ end soc_min_applies_during_outages::Bool = false soc_init_fraction::Float64 = off_grid_flag ? 1.0 : 0.5 can_grid_charge::Bool = off_grid_flag ? false : true + can_export_to_grid::Bool = false installed_cost_per_kw::Real = 910.0 installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 @@ -186,6 +187,7 @@ Base.@kwdef struct ElectricStorageDefaults soc_min_applies_during_outages::Bool = false soc_init_fraction::Float64 = off_grid_flag ? 1.0 : 0.5 can_grid_charge::Bool = off_grid_flag ? false : true + can_export_to_grid::Bool = false installed_cost_per_kw::Real = 910.0 installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 @@ -231,6 +233,7 @@ struct ElectricStorage <: AbstractElectricStorage soc_min_applies_during_outages::Bool soc_init_fraction::Float64 can_grid_charge::Bool + can_export_to_grid::Bool installed_cost_per_kw::Real installed_cost_per_kwh::Real replace_cost_per_kw::Real @@ -325,6 +328,7 @@ struct ElectricStorage <: AbstractElectricStorage s.soc_min_applies_during_outages, s.soc_init_fraction, s.can_grid_charge, + s.can_export_to_grid, s.installed_cost_per_kw, s.installed_cost_per_kwh, replace_cost_per_kw, From 88cc28e9b1922b8201efb0c8a767628a303c2ccd Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 6 Aug 2024 16:56:44 -0600 Subject: [PATCH 057/151] Revert "add can_export_to_grid ElectricStorage input" This reverts commit 8ad69fda19fafe82086f8c536fe354d022734c1f. --- src/core/energy_storage/electric_storage.jl | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 1e0ea2a8c..270442a70 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -147,7 +147,6 @@ end soc_min_applies_during_outages::Bool = false soc_init_fraction::Float64 = off_grid_flag ? 1.0 : 0.5 can_grid_charge::Bool = off_grid_flag ? false : true - can_export_to_grid::Bool = false installed_cost_per_kw::Real = 910.0 installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 @@ -187,7 +186,6 @@ Base.@kwdef struct ElectricStorageDefaults soc_min_applies_during_outages::Bool = false soc_init_fraction::Float64 = off_grid_flag ? 1.0 : 0.5 can_grid_charge::Bool = off_grid_flag ? false : true - can_export_to_grid::Bool = false installed_cost_per_kw::Real = 910.0 installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 @@ -233,7 +231,6 @@ struct ElectricStorage <: AbstractElectricStorage soc_min_applies_during_outages::Bool soc_init_fraction::Float64 can_grid_charge::Bool - can_export_to_grid::Bool installed_cost_per_kw::Real installed_cost_per_kwh::Real replace_cost_per_kw::Real @@ -328,7 +325,6 @@ struct ElectricStorage <: AbstractElectricStorage s.soc_min_applies_during_outages, s.soc_init_fraction, s.can_grid_charge, - s.can_export_to_grid, s.installed_cost_per_kw, s.installed_cost_per_kwh, replace_cost_per_kw, From f04b60df35cb3fbf64be82078ce7a8cfea141c66 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 6 Aug 2024 17:05:09 -0600 Subject: [PATCH 058/151] add can_export_to_grid ElectricStorage input --- src/core/energy_storage/electric_storage.jl | 4 ++++ src/mpc/structs.jl | 2 ++ 2 files changed, 6 insertions(+) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 270442a70..1e0ea2a8c 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -147,6 +147,7 @@ end soc_min_applies_during_outages::Bool = false soc_init_fraction::Float64 = off_grid_flag ? 1.0 : 0.5 can_grid_charge::Bool = off_grid_flag ? false : true + can_export_to_grid::Bool = false installed_cost_per_kw::Real = 910.0 installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 @@ -186,6 +187,7 @@ Base.@kwdef struct ElectricStorageDefaults soc_min_applies_during_outages::Bool = false soc_init_fraction::Float64 = off_grid_flag ? 1.0 : 0.5 can_grid_charge::Bool = off_grid_flag ? false : true + can_export_to_grid::Bool = false installed_cost_per_kw::Real = 910.0 installed_cost_per_kwh::Real = 455.0 replace_cost_per_kw::Real = 715.0 @@ -231,6 +233,7 @@ struct ElectricStorage <: AbstractElectricStorage soc_min_applies_during_outages::Bool soc_init_fraction::Float64 can_grid_charge::Bool + can_export_to_grid::Bool installed_cost_per_kw::Real installed_cost_per_kwh::Real replace_cost_per_kw::Real @@ -325,6 +328,7 @@ struct ElectricStorage <: AbstractElectricStorage s.soc_min_applies_during_outages, s.soc_init_fraction, s.can_grid_charge, + s.can_export_to_grid, s.installed_cost_per_kw, s.installed_cost_per_kwh, replace_cost_per_kw, diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index c886251e4..ddc8cd665 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -226,6 +226,7 @@ Base.@kwdef struct MPCElectricStorage < AbstractElectricStorage soc_min_fraction::Float64 = 0.2 soc_init_fraction::Float64 = 0.5 can_grid_charge::Bool = true + can_export_to_grid::Bool = false grid_charge_efficiency::Float64 = 0.96 * 0.975^2 allow_simultaneous_charge_discharge::Bool = true end @@ -239,6 +240,7 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage soc_min_fraction::Float64 = 0.2 soc_init_fraction::Float64 = 0.5 can_grid_charge::Bool = true + can_export_to_grid::Bool = false grid_charge_efficiency::Float64 = 0.96 * 0.975^2 max_kw::Float64 = size_kw max_kwh::Float64 = size_kwh From c06912285f24d43717d2a9f1edba89959369b950 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 6 Aug 2024 17:05:41 -0600 Subject: [PATCH 059/151] fix storage export to zero if can_export_to_grid=false --- src/constraints/storage_constraints.jl | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 8ca73fbd9..e914f710e 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -91,14 +91,21 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") m[Symbol("dvStoragePower"*_n)][b] >= m[Symbol("dvDischargeFromStorage"*_n)][b,ts] + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) ) - - # Remove grid-to-storage as an option if option to grid charge is turned off + + #Constraint (4m)-1: Remove grid-to-storage as an option if option to grid charge is turned off if !(p.s.storage.attr[b].can_grid_charge) for ts in p.time_steps_with_grid fix(m[Symbol("dvGridToStorage"*_n)][b, ts], 0.0, force=true) end end + #Constraint (4m)-2: Force storage export to grid to zero if option to grid export is turned off + if !p.s.storage.attr[b].can_export_to_grid + for ts in p.time_steps + fix(m[Symbol("dvStorageToGrid"*_n)][b, ts], 0.0, force=true) + end + end + if p.s.storage.attr[b].minimum_avg_soc_fraction > 0 avg_soc = sum(m[Symbol("dvStoredEnergy"*_n)][b, ts] for ts in p.time_steps) / (8760. / p.hours_per_time_step) From 1c857169a5594a6e0d681f30a0f9d3862dceb490 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 6 Aug 2024 17:25:58 -0600 Subject: [PATCH 060/151] rename require_start_and_end_charge_to_be_equal to optimize_soc_init_fraction --- src/core/energy_storage/electric_storage.jl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 1e0ea2a8c..63cb028df 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -170,7 +170,7 @@ end minimum_avg_soc_fraction::Float64 = 0.0 self_discharge_fraction_per_timestep::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep fixed_duration::Union{Real, Nothing} = nothing - require_start_and_end_charge_to_be_equal::Bool = false + optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true # Simultaneous charge/discharge is typically suboptimal anyway and allowing this avoids additional binary variables (which can slow solve time) ``` """ @@ -210,7 +210,7 @@ Base.@kwdef struct ElectricStorageDefaults minimum_avg_soc_fraction::Float64 = 0.0 self_discharge_fraction_per_timestep::Float64 = 0.0 fixed_duration::Union{Real, Nothing} = nothing - require_start_and_end_charge_to_be_equal::Bool = false + optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true end @@ -258,7 +258,7 @@ struct ElectricStorage <: AbstractElectricStorage minimum_avg_soc_fraction::Float64 self_discharge_fraction_per_timestep::Float64 fixed_duration::Union{Real, Nothing} - require_start_and_end_charge_to_be_equal::Bool + optimize_soc_init_fraction::Bool allow_simultaneous_charge_discharge::Bool function ElectricStorage(d::Dict, f::Financial) @@ -353,7 +353,7 @@ struct ElectricStorage <: AbstractElectricStorage s.minimum_avg_soc_fraction, s.self_discharge_fraction_per_timestep, s.fixed_duration, - s.require_start_and_end_charge_to_be_equal, + s.optimize_soc_init_fraction, s.allow_simultaneous_charge_discharge ) end From 0f2f2468f7c2c5ffd7d0b7a4861fc7caa2581e5a Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 6 Aug 2024 17:29:12 -0600 Subject: [PATCH 061/151] still fix init and final soc for non elec storage and elec storage when optimize init soc option false --- src/constraints/storage_constraints.jl | 27 ++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index e914f710e..878f05104 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -25,10 +25,21 @@ end function add_general_storage_dispatch_constraints(m, p, b; _n="") - # Constraint (4a): initial state of charge - # @constraint(m, - # m[Symbol("dvStoredEnergy"*_n)][b, 0] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] - # ) + # Constraint (4a): initial and final state of charge + if hasproperty(p.s.storage.attr[b], :optimize_soc_init_fraction) && p.s.storage.attr[b].optimize_soc_init_fraction + # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + print("\nConstraining ElectricStorage initial SOC = final SOC\n") + @constraint(m, + m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] + ) + else + @constraint(m, + m[Symbol("dvStoredEnergy"*_n)][b, 0] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] + ) + @constraint(m, + m[Symbol("dvStoredEnergy"*_n)][b, maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] + ) + end #Constraint (4n): State of charge upper bound is storage system size @constraint(m, [ts in p.time_steps], @@ -118,14 +129,6 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end - if p.s.storage.attr[b] isa ElectricStorage && p.s.storage.attr[b].require_start_and_end_charge_to_be_equal - # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) - print("\nSetting initial SOC = final SOC\n") - @constraint(m, - m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] - ) - end - # Prevent charging and discharging of the battery at the same time if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) #TODO: implement indicator constraint version for solvers that support it From 5c512b5db8b21bb92bb01663a8b71383b053d9ac Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 7 Aug 2024 06:44:39 -0600 Subject: [PATCH 062/151] remove final soc constraint --- src/constraints/storage_constraints.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 878f05104..2dc8da568 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -36,9 +36,9 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, m[Symbol("dvStoredEnergy"*_n)][b, 0] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) - @constraint(m, - m[Symbol("dvStoredEnergy"*_n)][b, maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] - ) + # @constraint(m, + # m[Symbol("dvStoredEnergy"*_n)][b, maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] + # ) end #Constraint (4n): State of charge upper bound is storage system size From 9bde839188a79aca5e0f6ffd5ee8f6a3862ed3b5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 7 Aug 2024 10:36:58 -0600 Subject: [PATCH 063/151] add can_export_to_grid to test inputs --- test/scenarios/storage_om.json | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/scenarios/storage_om.json b/test/scenarios/storage_om.json index f14380219..c188b51b8 100644 --- a/test/scenarios/storage_om.json +++ b/test/scenarios/storage_om.json @@ -24,7 +24,8 @@ "macrs_option_years": 0, "macrs_bonus_fraction": 0, "soc_init_fraction": 1.0, - "soc_min_fraction": 0.0 + "soc_min_fraction": 0.0, + "can_export_to_grid": true }, "ElectricTariff": { "urdb_label": "5ed6c1a15457a3367add15ae" From bb904476bc7c95c3d6deb06e0956fb2135c840d1 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:02:30 -0700 Subject: [PATCH 064/151] improve log msg --- src/constraints/storage_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 2dc8da568..050953b0c 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -28,7 +28,7 @@ function add_general_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4a): initial and final state of charge if hasproperty(p.s.storage.attr[b], :optimize_soc_init_fraction) && p.s.storage.attr[b].optimize_soc_init_fraction # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) - print("\nConstraining ElectricStorage initial SOC = final SOC\n") + print("\nOptimizing "*b*" inital SOC and constraining initial SOC = final SOC\n") @constraint(m, m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] ) From 81927a2f390cca4b9d748122d63d5cc82b0240c5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:02:58 -0700 Subject: [PATCH 065/151] use _n where missing in wind results processing --- src/results/wind.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/results/wind.jl b/src/results/wind.jl index 492571fb3..879730190 100644 --- a/src/results/wind.jl +++ b/src/results/wind.jl @@ -26,7 +26,7 @@ function add_wind_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["production_factor_series"] = Vector(p.production_factor[t, :]) per_unit_size_om = @expression(m, p.third_party_factor * p.pwf_om * m[:dvSize][t] * p.om_cost_per_kw[t]) - r["size_kw"] = round(value(m[:dvSize][t]), digits=2) + r["size_kw"] = round(value(m[Symbol("dvSize"*_n)][t]), digits=2) r["lifecycle_om_cost_after_tax"] = round(value(per_unit_size_om) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) r["year_one_om_cost_before_tax"] = round(value(per_unit_size_om) / (p.pwf_om * p.third_party_factor), digits=0) @@ -66,7 +66,7 @@ function add_wind_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["annual_energy_produced_kwh"] = round(value(AvgWindProd), digits=0) r["lcoe_per_kwh"] = calculate_lcoe(p, r, p.s.wind) - r["initial_capital_cost"] = value(m[:dvSize][t]) * p.s.wind.installed_cost_per_kw + r["initial_capital_cost"] = value(m[Symbol("dvSize"*_n)][t]) * p.s.wind.installed_cost_per_kw d[t] = r nothing end From 7edb8a3a5874634e575e7345e366875393935c00 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:06:10 -0700 Subject: [PATCH 066/151] round initial_capital_cost in wind and pv results --- src/results/pv.jl | 2 +- src/results/wind.jl | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/results/pv.jl b/src/results/pv.jl index 0bebc1745..514ab0934 100644 --- a/src/results/pv.jl +++ b/src/results/pv.jl @@ -67,7 +67,7 @@ function add_pv_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["lifecycle_om_cost_after_tax"] = round(value(PVPerUnitSizeOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) r["lcoe_per_kwh"] = calculate_lcoe(p, r, get_pv_by_name(t, p.s.pvs)) r["year_one_om_cost_before_tax"] = round(value(PVPerUnitSizeOMCosts) / p.pwf_om, digits=0) - r["initial_capital_cost"] = value(m[Symbol("dvSize"*_n)][t]) * get_pv_by_name(t, p.s.pvs).installed_cost_per_kw + r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)][t]) * get_pv_by_name(t, p.s.pvs).installed_cost_per_kw, digits=3) d[t] = r end nothing diff --git a/src/results/wind.jl b/src/results/wind.jl index 879730190..39a713f52 100644 --- a/src/results/wind.jl +++ b/src/results/wind.jl @@ -66,7 +66,7 @@ function add_wind_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["annual_energy_produced_kwh"] = round(value(AvgWindProd), digits=0) r["lcoe_per_kwh"] = calculate_lcoe(p, r, p.s.wind) - r["initial_capital_cost"] = value(m[Symbol("dvSize"*_n)][t]) * p.s.wind.installed_cost_per_kw + r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)][t]) * p.s.wind.installed_cost_per_kw, digits=3) d[t] = r nothing end From a88583645bc4042ff2b1269a0d94eec928dd9ead Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:08:11 -0700 Subject: [PATCH 067/151] add initial_capital_cost output to steam turbine --- src/results/steam_turbine.jl | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/results/steam_turbine.jl b/src/results/steam_turbine.jl index e25aa37a2..a60878ea9 100644 --- a/src/results/steam_turbine.jl +++ b/src/results/steam_turbine.jl @@ -101,6 +101,8 @@ function add_steam_turbine_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dic r["thermal_to_process_heat_load_series_mmbtu_per_hour"] = round.(value.(SteamTurbineToProcessHeatKW ./ KWH_PER_MMBTU), digits=5) + r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)][t]) * p.s.steam_turbine.installed_cost_per_kw, digits=3) + d["SteamTurbine"] = r nothing end \ No newline at end of file From 80e7f6ce35210784283850016cf0f2ae5935403d Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:12:55 -0700 Subject: [PATCH 068/151] add initial_capital_cost output to generator --- src/results/generator.jl | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/results/generator.jl b/src/results/generator.jl index ec9c2efb7..dfd00f4a8 100644 --- a/src/results/generator.jl +++ b/src/results/generator.jl @@ -69,6 +69,8 @@ function add_generator_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _ ) r["annual_energy_produced_kwh"] = round(value(AverageGenProd), digits=0) + r["initial_capital_cost"] = round(value(m[:dvSize][t]) * p.s.generator.installed_cost_per_kw, digits=3) + d["Generator"] = r nothing end From 142868470620c8132c42fb8ecefb330cb9986a1c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:15:16 -0700 Subject: [PATCH 069/151] add initial_capital_cost output to electric heater --- src/results/electric_heater.jl | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/results/electric_heater.jl b/src/results/electric_heater.jl index af6e4bad2..d14e541f6 100644 --- a/src/results/electric_heater.jl +++ b/src/results/electric_heater.jl @@ -86,6 +86,8 @@ function add_electric_heater_results(m::JuMP.AbstractModel, p::REoptInputs, d::D end r["thermal_to_process_heat_load_series_mmbtu_per_hour"] = round.(value.(ElectricHeaterToProcessHeatKW ./ KWH_PER_MMBTU), digits=5) + r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)][t]) * p.s.electric_heater.installed_cost_per_kw, digits=3) + d["ElectricHeater"] = r nothing end \ No newline at end of file From e8032d15502cb0a66637f8df4de261e9dec30cb2 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:17:44 -0700 Subject: [PATCH 070/151] add initial_capital_cost output to chp --- src/results/chp.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/results/chp.jl b/src/results/chp.jl index ccd5aa5f0..ed69865aa 100644 --- a/src/results/chp.jl +++ b/src/results/chp.jl @@ -30,6 +30,7 @@ function add_chp_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") # Note: the node number is an empty string if evaluating a single `Site`. r = Dict{String, Any}() r["size_kw"] = value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.chp)) + r["initial_capital_cost"] = round(value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.chp)) * p.s.chp.installed_cost_per_kw, digits=3) r["size_supplemental_firing_kw"] = value(sum(m[Symbol("dvSupplementaryFiringSize"*_n)][t] for t in p.techs.chp)) @expression(m, CHPFuelUsedKWH, sum(m[Symbol("dvFuelUsage"*_n)][t, ts] for t in p.techs.chp, ts in p.time_steps)) r["annual_fuel_consumption_mmbtu"] = round(value(CHPFuelUsedKWH) / KWH_PER_MMBTU, digits=3) From d13ff0b8fc5ae36d4f89923c31e4528d9c919bf3 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:19:06 -0700 Subject: [PATCH 071/151] add initial_capital_cost output to boiler --- src/results/boiler.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/results/boiler.jl b/src/results/boiler.jl index cacdfb695..b4cf002b9 100644 --- a/src/results/boiler.jl +++ b/src/results/boiler.jl @@ -22,6 +22,7 @@ function add_boiler_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r = Dict{String, Any}() r["size_mmbtu_per_hour"] = round(value(m[Symbol("dvSize"*_n)]["Boiler"]) / KWH_PER_MMBTU, digits=3) + r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)]["Boiler"]) * p.s.boiler.installed_cost_per_kw, digits=3) r["fuel_consumption_series_mmbtu_per_hour"] = round.(value.(m[:dvFuelUsage]["Boiler", ts] for ts in p.time_steps) / KWH_PER_MMBTU, digits=3) r["annual_fuel_consumption_mmbtu"] = round(sum(r["fuel_consumption_series_mmbtu_per_hour"]), digits=3) From 586b4c5bb520d81d8ace15f6e4e3c7d2b3cb1e85 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:23:55 -0700 Subject: [PATCH 072/151] correct some initial_capital_cost calcs --- src/results/electric_heater.jl | 3 +-- src/results/generator.jl | 3 +-- src/results/steam_turbine.jl | 4 +--- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/src/results/electric_heater.jl b/src/results/electric_heater.jl index d14e541f6..4bac8aeb5 100644 --- a/src/results/electric_heater.jl +++ b/src/results/electric_heater.jl @@ -20,6 +20,7 @@ function add_electric_heater_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r = Dict{String, Any}() r["size_mmbtu_per_hour"] = round(value(m[Symbol("dvSize"*_n)]["ElectricHeater"]) / KWH_PER_MMBTU, digits=3) + r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)]["ElectricHeater"]) * p.s.electric_heater.installed_cost_per_kw, digits=3) @expression(m, ElectricHeaterElectricConsumptionSeries[ts in p.time_steps], p.hours_per_time_step * sum(m[:dvHeatingProduction][t,q,ts] / p.heating_cop[t] for q in p.heating_loads, t in p.techs.electric_heater)) @@ -86,8 +87,6 @@ function add_electric_heater_results(m::JuMP.AbstractModel, p::REoptInputs, d::D end r["thermal_to_process_heat_load_series_mmbtu_per_hour"] = round.(value.(ElectricHeaterToProcessHeatKW ./ KWH_PER_MMBTU), digits=5) - r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)][t]) * p.s.electric_heater.installed_cost_per_kw, digits=3) - d["ElectricHeater"] = r nothing end \ No newline at end of file diff --git a/src/results/generator.jl b/src/results/generator.jl index dfd00f4a8..22e6ab975 100644 --- a/src/results/generator.jl +++ b/src/results/generator.jl @@ -32,6 +32,7 @@ function add_generator_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _ for t in p.techs.gen, ts in p.time_steps) ) r["size_kw"] = round(value(sum(m[:dvSize][t] for t in p.techs.gen)), digits=2) + r["initial_capital_cost"] = round(value(sum(m[:dvSize][t] for t in p.techs.gen)) * p.s.generator.installed_cost_per_kw, digits=3) r["lifecycle_fixed_om_cost_after_tax"] = round(value(GenPerUnitSizeOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) r["lifecycle_variable_om_cost_after_tax"] = round(value(m[:TotalPerUnitProdOMCosts]) * (1 - p.s.financial.owner_tax_rate_fraction), digits=0) r["lifecycle_fuel_cost_after_tax"] = round(value(m[:TotalGenFuelCosts]) * (1 - p.s.financial.offtaker_tax_rate_fraction), digits=2) @@ -69,8 +70,6 @@ function add_generator_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _ ) r["annual_energy_produced_kwh"] = round(value(AverageGenProd), digits=0) - r["initial_capital_cost"] = round(value(m[:dvSize][t]) * p.s.generator.installed_cost_per_kw, digits=3) - d["Generator"] = r nothing end diff --git a/src/results/steam_turbine.jl b/src/results/steam_turbine.jl index a60878ea9..d0b6d283d 100644 --- a/src/results/steam_turbine.jl +++ b/src/results/steam_turbine.jl @@ -25,6 +25,7 @@ function add_steam_turbine_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dic r = Dict{String, Any}() r["size_kw"] = round(value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.steam_turbine)), digits=3) + r["initial_capital_cost"] = round(value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.steam_turbine)) * p.s.steam_turbine.installed_cost_per_kw, digits=3) @expression(m, Year1SteamTurbineThermalConsumptionKWH, p.hours_per_time_step * sum(m[Symbol("dvThermalToSteamTurbine"*_n)][tst,q,ts] for tst in p.techs.can_supply_steam_turbine, q in p.heating_loads, ts in p.time_steps)) r["annual_thermal_consumption_mmbtu"] = round(value(Year1SteamTurbineThermalConsumptionKWH) / KWH_PER_MMBTU, digits=5) @@ -99,10 +100,7 @@ function add_steam_turbine_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dic @expression(m, SteamTurbineToProcessHeatKW[ts in p.time_steps], 0.0) end r["thermal_to_process_heat_load_series_mmbtu_per_hour"] = round.(value.(SteamTurbineToProcessHeatKW ./ KWH_PER_MMBTU), digits=5) - - r["initial_capital_cost"] = round(value(m[Symbol("dvSize"*_n)][t]) * p.s.steam_turbine.installed_cost_per_kw, digits=3) - d["SteamTurbine"] = r nothing end \ No newline at end of file From 8f42464dbf0744f0acd8ef3e4df911c215f8036e Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:26:26 -0700 Subject: [PATCH 073/151] add initial_capital_cost output to thermal storage --- src/results/thermal_storage.jl | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/results/thermal_storage.jl b/src/results/thermal_storage.jl index 48e9f607c..602269cb4 100644 --- a/src/results/thermal_storage.jl +++ b/src/results/thermal_storage.jl @@ -20,6 +20,7 @@ function add_hot_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict, r = Dict{String, Any}() size_kwh = round(value(m[Symbol("dvStorageEnergy"*_n)][b]), digits=3) r["size_gal"] = round(size_kwh / kwh_per_gal, digits=0) + r["initial_capital_cost"] = round(value(m[Symbol("dvStorageEnergy"*_n)][b]) * p.s.storage.attr[b].installed_cost_per_kwh, digits=3) if size_kwh != 0 soc = (m[Symbol("dvStoredEnergy"*_n)][b, ts] for ts in p.time_steps) @@ -93,12 +94,13 @@ function add_cold_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict Note: the node number is an empty string if evaluating a single `Site`. =# - kwh_per_gal = get_kwh_per_gal(p.s.storage.attr["ColdThermalStorage"].hot_water_temp_degF, - p.s.storage.attr["ColdThermalStorage"].cool_water_temp_degF) + kwh_per_gal = get_kwh_per_gal(p.s.storage.attr[b].hot_water_temp_degF, + p.s.storage.attr[b].cool_water_temp_degF) r = Dict{String, Any}() size_kwh = round(value(m[Symbol("dvStorageEnergy"*_n)][b]), digits=3) r["size_gal"] = round(size_kwh / kwh_per_gal, digits=0) + r["initial_capital_cost"] = round(value(m[Symbol("dvStorageEnergy"*_n)][b]) * p.s.storage.attr[b].installed_cost_per_kwh, digits=3) if size_kwh != 0 soc = (m[Symbol("dvStoredEnergy"*_n)][b, ts] for ts in p.time_steps) From f6424629fdab9f1297b242ec644e3547d7a55cf9 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 8 Aug 2024 13:26:30 -0700 Subject: [PATCH 074/151] Update CHANGELOG.md --- CHANGELOG.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e69fe1555..05c80642d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -25,10 +25,18 @@ Classify the change according to the following categories: ## Develop 2024-04-19 ### Added -- Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M -- Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` +- Added inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M +- Added outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added new input **self_discharge_fraction_per_timestep** to `ElectricStorage` for modeling battery self-discharge -- Added testset **Electric Storage O&M and Self-Discharge** in `test/runtests.jl` +- Added input option **can_export_to_grid** (defaults to _false_) to `ElectricStorage` and decision variable **dvStorageToGrid** +- Added input **allow_simultaneous_charge_discharge** (defaults to _true_) to `ElectricStorage` to give option to disallow battery from simultaneously charging and discharging, which adds binary decision variables often unnecessarily +- Added input **fixed_duration** to `ElectricStorage`, which fixes the ratio between **size_kw** and **size_kwh** in the optimized solution if provided +- Added input option **optimize_soc_init_fraction** (defaults to _false_) to `ElectricStorage`, which makes the optimization choose the inital SOC (equal to final SOC) instead of using **soc_init_fraction**. The initial SOC is also constrained to equal the final SOC, which eliminates the "free energy" issue. We currently do not fix SOC when **soc_init_fraction** is used because this has caused infeasibility. +- Added output **initial_capital_cost** to all techs +### Fixed +- Added missing outputs **lifecycle_export_benefit_before_tax** and **year_one_export_benefit_after_tax** to `ElectricTariff` +- Add missing output **year_one_om_cost_before_tax** to `PV` + ## v0.47.1 ### Fixed From b0a24c59c40a0a1ab08969c2993bfbb68036f2ca Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Mon, 12 Aug 2024 11:23:05 -0700 Subject: [PATCH 075/151] add initial_capital_cost output to absorption chiller --- src/results/absorption_chiller.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/results/absorption_chiller.jl b/src/results/absorption_chiller.jl index 86f424b99..2e974261f 100644 --- a/src/results/absorption_chiller.jl +++ b/src/results/absorption_chiller.jl @@ -25,6 +25,7 @@ function add_absorption_chiller_results(m::JuMP.AbstractModel, p::REoptInputs, d r["size_kw"] = value(sum(m[:dvSize][t] for t in p.techs.absorption_chiller)) r["size_ton"] = r["size_kw"] / KWH_THERMAL_PER_TONHOUR + r["initial_capital_cost"] = round(value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.absorption_chiller)) * p.s.absorption_chiller.installed_cost_per_kw, digits=3) @expression(m, ABSORPCHLtoTESKW[ts in p.time_steps], sum(m[:dvProductionToStorage][b,t,ts] for b in p.s.storage.types.cold, t in p.techs.absorption_chiller)) r["thermal_to_storage_series_ton"] = round.(value.(ABSORPCHLtoTESKW) ./ KWH_THERMAL_PER_TONHOUR, digits=5) From 305075d613382bf1aab6625511f9cc675f91d6a5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Mon, 12 Aug 2024 11:25:39 -0700 Subject: [PATCH 076/151] remove initial_capital_cost from chp results for now --- src/results/chp.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/results/chp.jl b/src/results/chp.jl index ed69865aa..21be26d47 100644 --- a/src/results/chp.jl +++ b/src/results/chp.jl @@ -30,8 +30,8 @@ function add_chp_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") # Note: the node number is an empty string if evaluating a single `Site`. r = Dict{String, Any}() r["size_kw"] = value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.chp)) - r["initial_capital_cost"] = round(value(sum(m[Symbol("dvSize"*_n)][t] for t in p.techs.chp)) * p.s.chp.installed_cost_per_kw, digits=3) - r["size_supplemental_firing_kw"] = value(sum(m[Symbol("dvSupplementaryFiringSize"*_n)][t] for t in p.techs.chp)) + #TODO: add initial_capital_cost to results (need to handle installed_cost_per_kw being a vector when cost curve is used) + r["size_supplemental_firing_kw"] = value(sum(m[Symbol("dvSupplementaryFiringSize"*_n)][t] for t in p.techs.chp)) @expression(m, CHPFuelUsedKWH, sum(m[Symbol("dvFuelUsage"*_n)][t, ts] for t in p.techs.chp, ts in p.time_steps)) r["annual_fuel_consumption_mmbtu"] = round(value(CHPFuelUsedKWH) / KWH_PER_MMBTU, digits=3) @expression(m, Year1CHPElecProd, From dd13655cf69db2de38604a50a0170bb4af55659f Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:52:01 -0600 Subject: [PATCH 077/151] make b arg required in degradation functions --- src/constraints/battery_degradation.jl | 6 +++--- src/core/reopt.jl | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/constraints/battery_degradation.jl b/src/constraints/battery_degradation.jl index be7c78cc2..5f990fa0a 100644 --- a/src/constraints/battery_degradation.jl +++ b/src/constraints/battery_degradation.jl @@ -10,7 +10,7 @@ function add_degradation_variables(m, p) end -function constrain_degradation_variables(m, p; b="ElectricStorage") +function constrain_degradation_variables(m, p, b) days = 1:365*p.s.financial.analysis_years ts_per_day = 24 / p.hours_per_time_step ts_per_year = ts_per_day * 365 @@ -41,12 +41,12 @@ end """ - add_degradation(m, p; b="ElectricStorage") + add_degradation(m, p, b) NOTE the average SOC and EFC variables are in absolute units. For example, the SOH variable starts at the battery capacity in kWh. """ -function add_degradation(m, p; b="ElectricStorage") +function add_degradation(m, p, b) days = 1:365*p.s.financial.analysis_years strategy = p.s.storage.attr[b].degradation.maintenance_strategy diff --git a/src/core/reopt.jl b/src/core/reopt.jl index f3d081c07..2336c953f 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -518,7 +518,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) for b in p.s.storage.types.elec if p.s.storage.attr[b].model_degradation - add_degradation(m, p; b=b) + add_degradation(m, p, b) if p.s.settings.add_soc_incentive @warn "Settings.add_soc_incentive is set to true but no incentive will be added because it conflicts with the battery degradation model." end From 2960b234de916ef8d143ed26ec1ff13fbf10c371 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:52:26 -0600 Subject: [PATCH 078/151] add name field to ElectricStorage --- src/core/energy_storage/electric_storage.jl | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 63cb028df..1bb403ba4 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -172,6 +172,7 @@ end fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true # Simultaneous charge/discharge is typically suboptimal anyway and allowing this avoids additional binary variables (which can slow solve time) + name::String = "ElectricStorage" ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -212,6 +213,7 @@ Base.@kwdef struct ElectricStorageDefaults fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true + name::String = "ElectricStorage" end @@ -260,6 +262,8 @@ struct ElectricStorage <: AbstractElectricStorage fixed_duration::Union{Real, Nothing} optimize_soc_init_fraction::Bool allow_simultaneous_charge_discharge::Bool + name::String + function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -354,7 +358,8 @@ struct ElectricStorage <: AbstractElectricStorage s.self_discharge_fraction_per_timestep, s.fixed_duration, s.optimize_soc_init_fraction, - s.allow_simultaneous_charge_discharge + s.allow_simultaneous_charge_discharge, + s.name ) end end From c9115a59e31aa0008534ce4f81eedeaa36ffd59e Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:56:21 -0600 Subject: [PATCH 079/151] loop elec stor, don't hardcode "ElectricStorage" (bigM_hourly_load_plus_battery_power) --- src/constraints/electric_utility_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index 4bb060e95..d9de8e841 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -279,7 +279,7 @@ function add_simultaneous_export_import_constraint(m, p; _n="") } ) else - bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.process_heat_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+p.s.storage.attr["ElectricStorage"].max_kw + bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.process_heat_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+sum(p.s.storage.attr[b].max_kw for b in p.s.storage.types.elec) @constraint(m, NoGridPurchasesBinary[ts in p.time_steps], sum(m[Symbol("dvGridPurchase"*_n)][ts, tier] for tier in 1:p.s.electric_tariff.n_energy_tiers) + sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) <= bigM_hourly_load_plus_battery_power*(1-m[Symbol("binNoGridPurchases"*_n)][ts]) From 5f2ceb6dcf41bc0e5ff6b39fd2258f4474af6273 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:56:26 -0600 Subject: [PATCH 080/151] use b arg don't hardcode "ElectricStorage" --- src/constraints/storage_constraints.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 050953b0c..e6a8fbfbd 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -27,10 +27,10 @@ end function add_general_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4a): initial and final state of charge if hasproperty(p.s.storage.attr[b], :optimize_soc_init_fraction) && p.s.storage.attr[b].optimize_soc_init_fraction - # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + # @constraint(m, m[:dvStoredEnergy][b,maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) print("\nOptimizing "*b*" inital SOC and constraining initial SOC = final SOC\n") @constraint(m, - m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] + m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy][b, maximum(p.time_steps)] ) else @constraint(m, From 710f2f5d13d44d6ed1121f480bdf857cdf8f65e9 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 13:58:41 -0600 Subject: [PATCH 081/151] handle ElectricStorage being a list of dicts --- src/core/scenario.jl | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index cf8197df2..092f41282 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -53,7 +53,7 @@ A Scenario struct can contain the following keys: - [SteamTurbine](@ref) (optional) - [ElectricHeater](@ref) (optional) -All values of `d` are expected to be `Dicts` except for `PV` and `GHP`, which can be either a `Dict` or `Dict[]` (for multiple PV arrays or GHP options). +All values of `d` are expected to be `Dicts` except for `PV`, `ElectricStorage`, and `GHP`, which can be either a `Dict` or `Dict[]` (for multiple PV arrays or GHP options). !!! note Set `flex_hvac_from_json=true` if `FlexibleHVAC` values were loaded in from JSON (necessary to @@ -157,15 +157,29 @@ function Scenario(d::Dict; flex_hvac_from_json=false) emissions_factor_series_lb_PM25_per_kwh = 0 ) end - + storage_structs = Dict{String, AbstractStorage}() if haskey(d, "ElectricStorage") - storage_dict = dictkeys_tosymbols(d["ElectricStorage"]) - storage_dict[:off_grid_flag] = settings.off_grid_flag + if typeof(d["ElectricStorage"]) <: AbstractArray + for (i, elecstor) in enumerate(d["ElectricStorage"]) + if !(haskey(elecstor, "name")) + elecstor["name"] = string("ElectricStorage", i) + end + storage_dict = dictkeys_tosymbols(elecstor) + storage_dict[:off_grid_flag] = settings.off_grid_flag + storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) + end + elseif typeof(d["ElectricStorage"]) <: AbstractDict + storage_dict = dictkeys_tosymbols(d["ElectricStorage"]) + storage_dict[:off_grid_flag] = settings.off_grid_flag + storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) + else + throw(@error("PV input must be Dict or Dict[].")) + end else storage_dict = Dict(:max_kw => 0.0) + storage_structs["ElectricStorage"] = ElectricStorage(storage_dict, financial) end - storage_structs["ElectricStorage"] = ElectricStorage(storage_dict, financial) # TODO stop building ElectricStorage when it is not modeled by user # (requires significant changes to constraints, variables) if haskey(d, "HotThermalStorage") From 1d3bce0facfcf223f06812c5ca64b9d633e9f06b Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:04:57 -0600 Subject: [PATCH 082/151] loop elec stor, don't hardcode "ElectricStorage" (steam turbine results) --- src/results/steam_turbine.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/steam_turbine.jl b/src/results/steam_turbine.jl index d0b6d283d..48fe2b40b 100644 --- a/src/results/steam_turbine.jl +++ b/src/results/steam_turbine.jl @@ -51,7 +51,7 @@ function add_steam_turbine_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dic r["electric_to_grid_series_kw"] = round.(value.(SteamTurbinetoGrid), digits=3) if !isempty(p.s.storage.types.elec) @expression(m, SteamTurbinetoBatt[ts in p.time_steps], - sum(m[Symbol("dvProductionToStorage"*_n)]["ElectricStorage",t,ts] for t in p.techs.steam_turbine)) + sum(m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.steam_turbine, b in p.s.storage.types.elec)) else SteamTurbinetoBatt = zeros(length(p.time_steps)) end From b69edbf56a0f1ab541e4936b1a1a7f8cbc8478cb Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:11:28 -0600 Subject: [PATCH 083/151] (gen results) loop elec stor, don't hardcode "ElectricStorage" --- src/results/generator.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/generator.jl b/src/results/generator.jl index 22e6ab975..56d5d256e 100644 --- a/src/results/generator.jl +++ b/src/results/generator.jl @@ -90,7 +90,7 @@ function add_generator_results(m::JuMP.AbstractModel, p::MPCInputs, d::Dict; _n= r["variable_om_cost"] = round(value(m[:TotalPerUnitProdOMCosts]), digits=0) r["fuel_cost"] = round(value(m[:TotalGenFuelCosts]), digits=2) - if p.s.storage.attr["ElectricStorage"].size_kw > 0 + if any([p.s.storage.attr[b].size_kw for b in p.s.storage.types.elec] .> 0) generatorToBatt = @expression(m, [ts in p.time_steps], sum(m[:dvProductionToStorage][b, t, ts] for b in p.s.storage.types.elec, t in p.techs.gen)) r["to_battery_series_kw"] = round.(value.(generatorToBatt), digits=3).data From 949bcb8de546af6d5011f693493fbc6083eeca7a Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:12:36 -0600 Subject: [PATCH 084/151] (utility results) loop elec stor, don't hardcode "ElectricStorage" --- src/results/electric_utility.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_utility.jl b/src/results/electric_utility.jl index d5b21f467..fdc3cd4a7 100644 --- a/src/results/electric_utility.jl +++ b/src/results/electric_utility.jl @@ -94,7 +94,7 @@ function add_electric_utility_results(m::JuMP.AbstractModel, p::MPCInputs, d::Di tier in 1:p.s.electric_tariff.n_energy_tiers) r["energy_supplied_kwh"] = round(value(Year1UtilityEnergy), digits=2) - if p.s.storage.attr["ElectricStorage"].size_kwh > 0 + if any([p.s.storage.attr[b].size_kw for b in p.s.storage.types.elec] .> 0) GridToBatt = @expression(m, [ts in p.time_steps], sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) ) From 293fcd79b5754176673a1f35bc6321cbc2dd204c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:14:12 -0600 Subject: [PATCH 085/151] (chp results) loop elec stor, don't hardcode "ElectricStorage" --- src/results/chp.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/chp.jl b/src/results/chp.jl index 21be26d47..b154becbf 100644 --- a/src/results/chp.jl +++ b/src/results/chp.jl @@ -60,7 +60,7 @@ function add_chp_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["electric_to_grid_series_kw"] = round.(value.(CHPtoGrid), digits=3) if !isempty(p.s.storage.types.elec) @expression(m, CHPtoBatt[ts in p.time_steps], - sum(m[Symbol("dvProductionToStorage"*_n)]["ElectricStorage",t,ts] for t in p.techs.chp)) + sum(m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.chp, b in p.s.storage.types.elec)) else CHPtoBatt = zeros(length(p.time_steps)) end From 1de9ca60724263d72c9fbd318fa7acfcc28c16c2 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:18:28 -0600 Subject: [PATCH 086/151] (elec stor results) use b arg don't hardcode "ElectricStorage" --- src/results/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 95a8ef783..c00fdef5c 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -43,7 +43,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: r["year_one_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction) / (p.pwf_om * p.third_party_factor), digits=0) if p.s.storage.attr[b].model_degradation - r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])["ElectricStorage"]; + r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])[b]; r["maintenance_cost"] = value(m[:degr_cost]) if p.s.storage.attr[b].degradation.maintenance_strategy == "replacement" r["replacement_month"] = Int(value( From 793e8d76d03f319723822dc2d5d271adcff39313 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:23:11 -0600 Subject: [PATCH 087/151] (mpc/model.jl) don't hardcode "ElectricStorage" --- src/mpc/model.jl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/mpc/model.jl b/src/mpc/model.jl index 7eabc8e82..cd1e1809f 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -34,10 +34,10 @@ Returns a Dict of results with keys matching those in the `MPCScenario`. function run_mpc(m::JuMP.AbstractModel, p::MPCInputs) build_mpc!(m, p) - if !p.s.settings.add_soc_incentive || !("ElectricStorage" in p.s.storage.types.elec) + if !p.s.settings.add_soc_incentive || isempty(p.s.storage.types.elec) @objective(m, Min, m[:Costs]) else # Keep SOC high - @objective(m, Min, m[:Costs] - sum(m[:dvStoredEnergy]["ElectricStorage", ts] for ts in p.time_steps) / + @objective(m, Min, m[:Costs] - sum(m[:dvStoredEnergy][b, ts] for ts in p.time_steps, b in p.s.storage.types.elec) / (8760. / p.hours_per_time_step) ) end @@ -250,8 +250,8 @@ function add_variables!(m::JuMP.AbstractModel, p::MPCInputs) m[:dvSize] = p.existing_sizes for b in p.s.storage.types.all - fix(m[:dvStoragePower][b], p.s.storage.attr["ElectricStorage"].size_kw, force=true) - fix(m[:dvStorageEnergy][b], p.s.storage.attr["ElectricStorage"].size_kwh, force=true) + fix(m[:dvStoragePower][b], p.s.storage.attr[b].size_kw, force=true) + fix(m[:dvStorageEnergy][b], p.s.storage.attr[b].size_kwh, force=true) end # not modeling min charges since control does not affect them From e3a7656ecf7da145c757bb557ebb672275661589 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 15:46:14 -0600 Subject: [PATCH 088/151] correct error msg --- src/core/scenario.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index 092f41282..c76304d7d 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -174,7 +174,7 @@ function Scenario(d::Dict; flex_hvac_from_json=false) storage_dict[:off_grid_flag] = settings.off_grid_flag storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) else - throw(@error("PV input must be Dict or Dict[].")) + throw(@error("ElectricStorage input must be Dict or Dict[].")) end else storage_dict = Dict(:max_kw => 0.0) From 3ce3e32cd15470d71a6d66ae5d9604205a1e8f2e Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 15:47:14 -0600 Subject: [PATCH 089/151] error in ERP if multiple elec stor --- src/outagesim/backup_reliability.jl | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/outagesim/backup_reliability.jl b/src/outagesim/backup_reliability.jl index d089d207c..5e03df7b7 100644 --- a/src/outagesim/backup_reliability.jl +++ b/src/outagesim/backup_reliability.jl @@ -719,6 +719,11 @@ function backup_reliability_reopt_inputs(;d::Dict, p::REoptInputs, r::Dict = Dic !haskey(d, "Outages") || Bool(get(d["Outages"], "electric_storage_microgrid_upgraded", false)) ) + if typeof(d["ElectricStorage"]) <: AbstractArray && length(d["ElectricStorage"]) == 1 + d["ElectricStorage"] = d["ElectricStorage"][1] + elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) + throw(@error("Calculating resilience metrics for a REopt solution with multiple ElectricStorage is not yet supported.")) + end r2[:battery_charge_efficiency] = p.s.storage.attr["ElectricStorage"].charge_efficiency r2[:battery_discharge_efficiency] = p.s.storage.attr["ElectricStorage"].discharge_efficiency r2[:battery_size_kw] = get(d["ElectricStorage"], "size_kw", 0) From dcda12af26e2885fd418893b9d0b9c39a2eb3e33 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 16:00:21 -0600 Subject: [PATCH 090/151] in ERP use d["ElectricStorage"]["name"] not "ElectricStorage" hardcoded --- src/outagesim/backup_reliability.jl | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/outagesim/backup_reliability.jl b/src/outagesim/backup_reliability.jl index 5e03df7b7..b50037b24 100644 --- a/src/outagesim/backup_reliability.jl +++ b/src/outagesim/backup_reliability.jl @@ -724,8 +724,9 @@ function backup_reliability_reopt_inputs(;d::Dict, p::REoptInputs, r::Dict = Dic elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) throw(@error("Calculating resilience metrics for a REopt solution with multiple ElectricStorage is not yet supported.")) end - r2[:battery_charge_efficiency] = p.s.storage.attr["ElectricStorage"].charge_efficiency - r2[:battery_discharge_efficiency] = p.s.storage.attr["ElectricStorage"].discharge_efficiency + elec_stor_name = d["ElectricStorage"]["name"] + r2[:battery_charge_efficiency] = p.s.storage.attr[elec_stor_name].charge_efficiency + r2[:battery_discharge_efficiency] = p.s.storage.attr[elec_stor_name].discharge_efficiency r2[:battery_size_kw] = get(d["ElectricStorage"], "size_kw", 0) #ERP tool uses effective battery size so need to subtract minimum SOC From 970bd2b4b587455944b7687c69ff430a5b86de2d Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 29 Aug 2024 16:01:06 -0600 Subject: [PATCH 091/151] (outagesim) handle elec stor name and error if multiple --- src/outagesim/outage_simulator.jl | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/outagesim/outage_simulator.jl b/src/outagesim/outage_simulator.jl index d365347dc..0a60f6cc7 100644 --- a/src/outagesim/outage_simulator.jl +++ b/src/outagesim/outage_simulator.jl @@ -223,8 +223,6 @@ Returns a dict ``` """ function simulate_outages(d::Dict, p::REoptInputs; microgrid_only::Bool=false) - batt_roundtrip_efficiency = (p.s.storage.attr["ElectricStorage"].charge_efficiency * - p.s.storage.attr["ElectricStorage"].discharge_efficiency) # TODO handle generic PV names pv_kw_ac_hourly = zeros(length(p.time_steps)) @@ -250,11 +248,21 @@ function simulate_outages(d::Dict, p::REoptInputs; microgrid_only::Bool=false) batt_kwh = 0 batt_kw = 0 init_soc = zeros(length(p.time_steps)) + elec_stor_name = "ElectricStorage" if "ElectricStorage" in keys(d) + if typeof(d["ElectricStorage"]) <: AbstractArray && length(d["ElectricStorage"]) == 1 + d["ElectricStorage"] = d["ElectricStorage"][1] + elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) + throw(@error("simulate_outages for a REopt solution with multiple ElectricStorage is not yet supported")) + end + elec_stor_name = d["ElectricStorage"]["name"] batt_kwh = get(d["ElectricStorage"], "size_kwh", 0) batt_kw = get(d["ElectricStorage"], "size_kw", 0) init_soc = get(d["ElectricStorage"], "soc_series_fraction", zeros(length(p.time_steps))) end + batt_roundtrip_efficiency = (p.s.storage.attr[elec_stor_name].charge_efficiency * + p.s.storage.attr[elec_stor_name].discharge_efficiency) + if microgrid_only && !Bool(get(d["Outages"], "electric_storage_microgrid_upgraded", false)) batt_kwh = 0 batt_kw = 0 From febc3675baf939705d4d1cd5cb852839ed76eb46 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:25:44 -0600 Subject: [PATCH 092/151] Revert "(outagesim) handle elec stor name and error if multiple" This reverts commit 970bd2b4b587455944b7687c69ff430a5b86de2d. --- src/outagesim/outage_simulator.jl | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/src/outagesim/outage_simulator.jl b/src/outagesim/outage_simulator.jl index 0a60f6cc7..d365347dc 100644 --- a/src/outagesim/outage_simulator.jl +++ b/src/outagesim/outage_simulator.jl @@ -223,6 +223,8 @@ Returns a dict ``` """ function simulate_outages(d::Dict, p::REoptInputs; microgrid_only::Bool=false) + batt_roundtrip_efficiency = (p.s.storage.attr["ElectricStorage"].charge_efficiency * + p.s.storage.attr["ElectricStorage"].discharge_efficiency) # TODO handle generic PV names pv_kw_ac_hourly = zeros(length(p.time_steps)) @@ -248,21 +250,11 @@ function simulate_outages(d::Dict, p::REoptInputs; microgrid_only::Bool=false) batt_kwh = 0 batt_kw = 0 init_soc = zeros(length(p.time_steps)) - elec_stor_name = "ElectricStorage" if "ElectricStorage" in keys(d) - if typeof(d["ElectricStorage"]) <: AbstractArray && length(d["ElectricStorage"]) == 1 - d["ElectricStorage"] = d["ElectricStorage"][1] - elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) - throw(@error("simulate_outages for a REopt solution with multiple ElectricStorage is not yet supported")) - end - elec_stor_name = d["ElectricStorage"]["name"] batt_kwh = get(d["ElectricStorage"], "size_kwh", 0) batt_kw = get(d["ElectricStorage"], "size_kw", 0) init_soc = get(d["ElectricStorage"], "soc_series_fraction", zeros(length(p.time_steps))) end - batt_roundtrip_efficiency = (p.s.storage.attr[elec_stor_name].charge_efficiency * - p.s.storage.attr[elec_stor_name].discharge_efficiency) - if microgrid_only && !Bool(get(d["Outages"], "electric_storage_microgrid_upgraded", false)) batt_kwh = 0 batt_kw = 0 From 8fb61ee3c779cd17be013df72ac6925cfc50499a Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:25:47 -0600 Subject: [PATCH 093/151] Revert "in ERP use d["ElectricStorage"]["name"] not "ElectricStorage" hardcoded" This reverts commit dcda12af26e2885fd418893b9d0b9c39a2eb3e33. --- src/outagesim/backup_reliability.jl | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/outagesim/backup_reliability.jl b/src/outagesim/backup_reliability.jl index b50037b24..5e03df7b7 100644 --- a/src/outagesim/backup_reliability.jl +++ b/src/outagesim/backup_reliability.jl @@ -724,9 +724,8 @@ function backup_reliability_reopt_inputs(;d::Dict, p::REoptInputs, r::Dict = Dic elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) throw(@error("Calculating resilience metrics for a REopt solution with multiple ElectricStorage is not yet supported.")) end - elec_stor_name = d["ElectricStorage"]["name"] - r2[:battery_charge_efficiency] = p.s.storage.attr[elec_stor_name].charge_efficiency - r2[:battery_discharge_efficiency] = p.s.storage.attr[elec_stor_name].discharge_efficiency + r2[:battery_charge_efficiency] = p.s.storage.attr["ElectricStorage"].charge_efficiency + r2[:battery_discharge_efficiency] = p.s.storage.attr["ElectricStorage"].discharge_efficiency r2[:battery_size_kw] = get(d["ElectricStorage"], "size_kw", 0) #ERP tool uses effective battery size so need to subtract minimum SOC From b1b8dd77d044e343713c0e746913c47b24efdbfd Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:25:50 -0600 Subject: [PATCH 094/151] Revert "error in ERP if multiple elec stor" This reverts commit 3ce3e32cd15470d71a6d66ae5d9604205a1e8f2e. --- src/outagesim/backup_reliability.jl | 5 ----- 1 file changed, 5 deletions(-) diff --git a/src/outagesim/backup_reliability.jl b/src/outagesim/backup_reliability.jl index 5e03df7b7..d089d207c 100644 --- a/src/outagesim/backup_reliability.jl +++ b/src/outagesim/backup_reliability.jl @@ -719,11 +719,6 @@ function backup_reliability_reopt_inputs(;d::Dict, p::REoptInputs, r::Dict = Dic !haskey(d, "Outages") || Bool(get(d["Outages"], "electric_storage_microgrid_upgraded", false)) ) - if typeof(d["ElectricStorage"]) <: AbstractArray && length(d["ElectricStorage"]) == 1 - d["ElectricStorage"] = d["ElectricStorage"][1] - elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) - throw(@error("Calculating resilience metrics for a REopt solution with multiple ElectricStorage is not yet supported.")) - end r2[:battery_charge_efficiency] = p.s.storage.attr["ElectricStorage"].charge_efficiency r2[:battery_discharge_efficiency] = p.s.storage.attr["ElectricStorage"].discharge_efficiency r2[:battery_size_kw] = get(d["ElectricStorage"], "size_kw", 0) From 1e06aac657bd6ef55b4d70b313aa35e4e66eebb5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:25:53 -0600 Subject: [PATCH 095/151] Revert "correct error msg" This reverts commit e3a7656ecf7da145c757bb557ebb672275661589. --- src/core/scenario.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index c76304d7d..092f41282 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -174,7 +174,7 @@ function Scenario(d::Dict; flex_hvac_from_json=false) storage_dict[:off_grid_flag] = settings.off_grid_flag storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) else - throw(@error("ElectricStorage input must be Dict or Dict[].")) + throw(@error("PV input must be Dict or Dict[].")) end else storage_dict = Dict(:max_kw => 0.0) From 9d0c564464b35ea4365cb9a3f727f9ace1095a65 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:25:57 -0600 Subject: [PATCH 096/151] Revert "(mpc/model.jl) don't hardcode "ElectricStorage"" This reverts commit 793e8d76d03f319723822dc2d5d271adcff39313. --- src/mpc/model.jl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/mpc/model.jl b/src/mpc/model.jl index cd1e1809f..7eabc8e82 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -34,10 +34,10 @@ Returns a Dict of results with keys matching those in the `MPCScenario`. function run_mpc(m::JuMP.AbstractModel, p::MPCInputs) build_mpc!(m, p) - if !p.s.settings.add_soc_incentive || isempty(p.s.storage.types.elec) + if !p.s.settings.add_soc_incentive || !("ElectricStorage" in p.s.storage.types.elec) @objective(m, Min, m[:Costs]) else # Keep SOC high - @objective(m, Min, m[:Costs] - sum(m[:dvStoredEnergy][b, ts] for ts in p.time_steps, b in p.s.storage.types.elec) / + @objective(m, Min, m[:Costs] - sum(m[:dvStoredEnergy]["ElectricStorage", ts] for ts in p.time_steps) / (8760. / p.hours_per_time_step) ) end @@ -250,8 +250,8 @@ function add_variables!(m::JuMP.AbstractModel, p::MPCInputs) m[:dvSize] = p.existing_sizes for b in p.s.storage.types.all - fix(m[:dvStoragePower][b], p.s.storage.attr[b].size_kw, force=true) - fix(m[:dvStorageEnergy][b], p.s.storage.attr[b].size_kwh, force=true) + fix(m[:dvStoragePower][b], p.s.storage.attr["ElectricStorage"].size_kw, force=true) + fix(m[:dvStorageEnergy][b], p.s.storage.attr["ElectricStorage"].size_kwh, force=true) end # not modeling min charges since control does not affect them From 12889a5020a016c63a1a02924fa51d96e8a95811 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:00 -0600 Subject: [PATCH 097/151] Revert "(elec stor results) use b arg don't hardcode "ElectricStorage"" This reverts commit 1de9ca60724263d72c9fbd318fa7acfcc28c16c2. --- src/results/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index c00fdef5c..95a8ef783 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -43,7 +43,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: r["year_one_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction) / (p.pwf_om * p.third_party_factor), digits=0) if p.s.storage.attr[b].model_degradation - r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])[b]; + r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])["ElectricStorage"]; r["maintenance_cost"] = value(m[:degr_cost]) if p.s.storage.attr[b].degradation.maintenance_strategy == "replacement" r["replacement_month"] = Int(value( From 63ab4eb128eaefb8fb81548468f22e979ee1f1c8 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:02 -0600 Subject: [PATCH 098/151] Revert "(chp results) loop elec stor, don't hardcode "ElectricStorage"" This reverts commit 293fcd79b5754176673a1f35bc6321cbc2dd204c. --- src/results/chp.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/chp.jl b/src/results/chp.jl index b154becbf..21be26d47 100644 --- a/src/results/chp.jl +++ b/src/results/chp.jl @@ -60,7 +60,7 @@ function add_chp_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["electric_to_grid_series_kw"] = round.(value.(CHPtoGrid), digits=3) if !isempty(p.s.storage.types.elec) @expression(m, CHPtoBatt[ts in p.time_steps], - sum(m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.chp, b in p.s.storage.types.elec)) + sum(m[Symbol("dvProductionToStorage"*_n)]["ElectricStorage",t,ts] for t in p.techs.chp)) else CHPtoBatt = zeros(length(p.time_steps)) end From c4f138afb1ac3fd8742322bd95a6993120afbcd9 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:04 -0600 Subject: [PATCH 099/151] Revert "(utility results) loop elec stor, don't hardcode "ElectricStorage"" This reverts commit 949bcb8de546af6d5011f693493fbc6083eeca7a. --- src/results/electric_utility.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_utility.jl b/src/results/electric_utility.jl index fdc3cd4a7..d5b21f467 100644 --- a/src/results/electric_utility.jl +++ b/src/results/electric_utility.jl @@ -94,7 +94,7 @@ function add_electric_utility_results(m::JuMP.AbstractModel, p::MPCInputs, d::Di tier in 1:p.s.electric_tariff.n_energy_tiers) r["energy_supplied_kwh"] = round(value(Year1UtilityEnergy), digits=2) - if any([p.s.storage.attr[b].size_kw for b in p.s.storage.types.elec] .> 0) + if p.s.storage.attr["ElectricStorage"].size_kwh > 0 GridToBatt = @expression(m, [ts in p.time_steps], sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) ) From 10d3264a899b8a4798ae98b9e74209b91f768b6b Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:09 -0600 Subject: [PATCH 100/151] Revert "(gen results) loop elec stor, don't hardcode "ElectricStorage"" This reverts commit b69edbf56a0f1ab541e4936b1a1a7f8cbc8478cb. --- src/results/generator.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/generator.jl b/src/results/generator.jl index 56d5d256e..22e6ab975 100644 --- a/src/results/generator.jl +++ b/src/results/generator.jl @@ -90,7 +90,7 @@ function add_generator_results(m::JuMP.AbstractModel, p::MPCInputs, d::Dict; _n= r["variable_om_cost"] = round(value(m[:TotalPerUnitProdOMCosts]), digits=0) r["fuel_cost"] = round(value(m[:TotalGenFuelCosts]), digits=2) - if any([p.s.storage.attr[b].size_kw for b in p.s.storage.types.elec] .> 0) + if p.s.storage.attr["ElectricStorage"].size_kw > 0 generatorToBatt = @expression(m, [ts in p.time_steps], sum(m[:dvProductionToStorage][b, t, ts] for b in p.s.storage.types.elec, t in p.techs.gen)) r["to_battery_series_kw"] = round.(value.(generatorToBatt), digits=3).data From 34179acfc37cb642d53629ee59c4c89e11be939c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:12 -0600 Subject: [PATCH 101/151] Revert "loop elec stor, don't hardcode "ElectricStorage" (steam turbine results)" This reverts commit 1d3bce0facfcf223f06812c5ca64b9d633e9f06b. --- src/results/steam_turbine.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/steam_turbine.jl b/src/results/steam_turbine.jl index 48fe2b40b..d0b6d283d 100644 --- a/src/results/steam_turbine.jl +++ b/src/results/steam_turbine.jl @@ -51,7 +51,7 @@ function add_steam_turbine_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dic r["electric_to_grid_series_kw"] = round.(value.(SteamTurbinetoGrid), digits=3) if !isempty(p.s.storage.types.elec) @expression(m, SteamTurbinetoBatt[ts in p.time_steps], - sum(m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.steam_turbine, b in p.s.storage.types.elec)) + sum(m[Symbol("dvProductionToStorage"*_n)]["ElectricStorage",t,ts] for t in p.techs.steam_turbine)) else SteamTurbinetoBatt = zeros(length(p.time_steps)) end From fc44e06164891013b3cbaea72d3665cf6524e7ab Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:16 -0600 Subject: [PATCH 102/151] Revert "handle ElectricStorage being a list of dicts" This reverts commit 710f2f5d13d44d6ed1121f480bdf857cdf8f65e9. --- src/core/scenario.jl | 24 +++++------------------- 1 file changed, 5 insertions(+), 19 deletions(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index 092f41282..cf8197df2 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -53,7 +53,7 @@ A Scenario struct can contain the following keys: - [SteamTurbine](@ref) (optional) - [ElectricHeater](@ref) (optional) -All values of `d` are expected to be `Dicts` except for `PV`, `ElectricStorage`, and `GHP`, which can be either a `Dict` or `Dict[]` (for multiple PV arrays or GHP options). +All values of `d` are expected to be `Dicts` except for `PV` and `GHP`, which can be either a `Dict` or `Dict[]` (for multiple PV arrays or GHP options). !!! note Set `flex_hvac_from_json=true` if `FlexibleHVAC` values were loaded in from JSON (necessary to @@ -157,29 +157,15 @@ function Scenario(d::Dict; flex_hvac_from_json=false) emissions_factor_series_lb_PM25_per_kwh = 0 ) end - + storage_structs = Dict{String, AbstractStorage}() if haskey(d, "ElectricStorage") - if typeof(d["ElectricStorage"]) <: AbstractArray - for (i, elecstor) in enumerate(d["ElectricStorage"]) - if !(haskey(elecstor, "name")) - elecstor["name"] = string("ElectricStorage", i) - end - storage_dict = dictkeys_tosymbols(elecstor) - storage_dict[:off_grid_flag] = settings.off_grid_flag - storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) - end - elseif typeof(d["ElectricStorage"]) <: AbstractDict - storage_dict = dictkeys_tosymbols(d["ElectricStorage"]) - storage_dict[:off_grid_flag] = settings.off_grid_flag - storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) - else - throw(@error("PV input must be Dict or Dict[].")) - end + storage_dict = dictkeys_tosymbols(d["ElectricStorage"]) + storage_dict[:off_grid_flag] = settings.off_grid_flag else storage_dict = Dict(:max_kw => 0.0) - storage_structs["ElectricStorage"] = ElectricStorage(storage_dict, financial) end + storage_structs["ElectricStorage"] = ElectricStorage(storage_dict, financial) # TODO stop building ElectricStorage when it is not modeled by user # (requires significant changes to constraints, variables) if haskey(d, "HotThermalStorage") From 7161f6e6354322ae30b86620b6cc36ef17f7188d Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:22 -0600 Subject: [PATCH 103/151] Revert "use b arg don't hardcode "ElectricStorage"" This reverts commit 5f2ceb6dcf41bc0e5ff6b39fd2258f4474af6273. --- src/constraints/storage_constraints.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index e6a8fbfbd..050953b0c 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -27,10 +27,10 @@ end function add_general_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4a): initial and final state of charge if hasproperty(p.s.storage.attr[b], :optimize_soc_init_fraction) && p.s.storage.attr[b].optimize_soc_init_fraction - # @constraint(m, m[:dvStoredEnergy][b,maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) print("\nOptimizing "*b*" inital SOC and constraining initial SOC = final SOC\n") @constraint(m, - m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy][b, maximum(p.time_steps)] + m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] ) else @constraint(m, From 613942834bcdad8ab1cd47d3d059be5079921707 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:24 -0600 Subject: [PATCH 104/151] Revert "loop elec stor, don't hardcode "ElectricStorage" (bigM_hourly_load_plus_battery_power)" This reverts commit c9115a59e31aa0008534ce4f81eedeaa36ffd59e. --- src/constraints/electric_utility_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index d9de8e841..4bb060e95 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -279,7 +279,7 @@ function add_simultaneous_export_import_constraint(m, p; _n="") } ) else - bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.process_heat_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+sum(p.s.storage.attr[b].max_kw for b in p.s.storage.types.elec) + bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.process_heat_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+p.s.storage.attr["ElectricStorage"].max_kw @constraint(m, NoGridPurchasesBinary[ts in p.time_steps], sum(m[Symbol("dvGridPurchase"*_n)][ts, tier] for tier in 1:p.s.electric_tariff.n_energy_tiers) + sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) <= bigM_hourly_load_plus_battery_power*(1-m[Symbol("binNoGridPurchases"*_n)][ts]) From ac5a95485c520c013b4e1ec23b3cd0ef38e7cf66 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:27 -0600 Subject: [PATCH 105/151] Revert "add name field to ElectricStorage" This reverts commit 2960b234de916ef8d143ed26ec1ff13fbf10c371. --- src/core/energy_storage/electric_storage.jl | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 1bb403ba4..63cb028df 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -172,7 +172,6 @@ end fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true # Simultaneous charge/discharge is typically suboptimal anyway and allowing this avoids additional binary variables (which can slow solve time) - name::String = "ElectricStorage" ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -213,7 +212,6 @@ Base.@kwdef struct ElectricStorageDefaults fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true - name::String = "ElectricStorage" end @@ -262,8 +260,6 @@ struct ElectricStorage <: AbstractElectricStorage fixed_duration::Union{Real, Nothing} optimize_soc_init_fraction::Bool allow_simultaneous_charge_discharge::Bool - name::String - function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -358,8 +354,7 @@ struct ElectricStorage <: AbstractElectricStorage s.self_discharge_fraction_per_timestep, s.fixed_duration, s.optimize_soc_init_fraction, - s.allow_simultaneous_charge_discharge, - s.name + s.allow_simultaneous_charge_discharge ) end end From 29657c06769c7efe75775b0ed67a34b1ace803cc Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:26:33 -0600 Subject: [PATCH 106/151] Revert "make b arg required in degradation functions" This reverts commit dd13655cf69db2de38604a50a0170bb4af55659f. --- src/constraints/battery_degradation.jl | 6 +++--- src/core/reopt.jl | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/constraints/battery_degradation.jl b/src/constraints/battery_degradation.jl index 5f990fa0a..be7c78cc2 100644 --- a/src/constraints/battery_degradation.jl +++ b/src/constraints/battery_degradation.jl @@ -10,7 +10,7 @@ function add_degradation_variables(m, p) end -function constrain_degradation_variables(m, p, b) +function constrain_degradation_variables(m, p; b="ElectricStorage") days = 1:365*p.s.financial.analysis_years ts_per_day = 24 / p.hours_per_time_step ts_per_year = ts_per_day * 365 @@ -41,12 +41,12 @@ end """ - add_degradation(m, p, b) + add_degradation(m, p; b="ElectricStorage") NOTE the average SOC and EFC variables are in absolute units. For example, the SOH variable starts at the battery capacity in kWh. """ -function add_degradation(m, p, b) +function add_degradation(m, p; b="ElectricStorage") days = 1:365*p.s.financial.analysis_years strategy = p.s.storage.attr[b].degradation.maintenance_strategy diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 2336c953f..f3d081c07 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -518,7 +518,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) for b in p.s.storage.types.elec if p.s.storage.attr[b].model_degradation - add_degradation(m, p, b) + add_degradation(m, p; b=b) if p.s.settings.add_soc_incentive @warn "Settings.add_soc_incentive is set to true but no incentive will be added because it conflicts with the battery degradation model." end From aec28ca84828abf33fe638bdaa1ac48b648c8ac3 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:32:49 -0600 Subject: [PATCH 107/151] (proforma) handle elec stor name and error if multiple --- src/results/proforma.jl | 82 ++++++++++++++++++++++------------------- 1 file changed, 45 insertions(+), 37 deletions(-) diff --git a/src/results/proforma.jl b/src/results/proforma.jl index 9e403862e..8a4abd1e1 100644 --- a/src/results/proforma.jl +++ b/src/results/proforma.jl @@ -64,45 +64,53 @@ function proforma_results(p::REoptInputs, d::Dict) end # calculate Storage o+m costs, incentives, and depreciation - if "ElectricStorage" in keys(d) && d["ElectricStorage"]["size_kw"] > 0 - # TODO handle other types of storage - storage = p.s.storage.attr["ElectricStorage"] - total_kw = d["ElectricStorage"]["size_kw"] - total_kwh = d["ElectricStorage"]["size_kwh"] - capital_cost = total_kw * storage.installed_cost_per_kw + total_kwh * storage.installed_cost_per_kwh - battery_replacement_year = storage.battery_replacement_year - battery_replacement_cost = -1 * ((total_kw * storage.replace_cost_per_kw) + ( - total_kwh * storage.replace_cost_per_kwh)) - m.om_series += [yr != battery_replacement_year ? 0 : battery_replacement_cost for yr in 1:years] - - # storage only has cbi in the API - cbi = total_kw * storage.total_rebate_per_kw + total_kwh * storage.total_rebate_per_kwh - m.total_ibi_and_cbi += cbi - - # ITC - federal_itc_basis = capital_cost # bug in v1 subtracted cbi from capital_cost here - federal_itc_amount = storage.total_itc_fraction * federal_itc_basis - m.federal_itc += federal_itc_amount - - # Depreciation - if storage.macrs_option_years in [5, 7] - schedule = [] - if storage.macrs_option_years == 5 - schedule = p.s.financial.macrs_five_year - elseif storage.macrs_option_years == 7 - schedule = p.s.financial.macrs_seven_year - end - macrs_bonus_basis = federal_itc_basis * (1 - storage.total_itc_fraction * storage.macrs_itc_reduction) - macrs_basis = macrs_bonus_basis * (1 - storage.macrs_bonus_fraction) - - depreciation_schedule = zeros(years) - for (i, r) in enumerate(schedule) - if i < length(depreciation_schedule) - depreciation_schedule[i] = macrs_basis * r + if "ElectricStorage" in keys(d) + if typeof(d["ElectricStorage"]) <: AbstractArray && length(d["ElectricStorage"]) == 1 + d["ElectricStorage"] = d["ElectricStorage"][1] + elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) + throw(@error("Pro forma results for a REopt solution with multiple ElectricStorage is not yet supported")) + end + elec_stor_name = d["ElectricStorage"]["name"] + if d["ElectricStorage"]["size_kw"] > 0 + # TODO handle other types of storage + storage = p.s.storage.attr[elec_stor_name] + total_kw = d["ElectricStorage"]["size_kw"] + total_kwh = d["ElectricStorage"]["size_kwh"] + capital_cost = total_kw * storage.installed_cost_per_kw + total_kwh * storage.installed_cost_per_kwh + battery_replacement_year = storage.battery_replacement_year + battery_replacement_cost = -1 * ((total_kw * storage.replace_cost_per_kw) + ( + total_kwh * storage.replace_cost_per_kwh)) + m.om_series += [yr != battery_replacement_year ? 0 : battery_replacement_cost for yr in 1:years] + + # storage only has cbi in the API + cbi = total_kw * storage.total_rebate_per_kw + total_kwh * storage.total_rebate_per_kwh + m.total_ibi_and_cbi += cbi + + # ITC + federal_itc_basis = capital_cost # bug in v1 subtracted cbi from capital_cost here + federal_itc_amount = storage.total_itc_fraction * federal_itc_basis + m.federal_itc += federal_itc_amount + + # Depreciation + if storage.macrs_option_years in [5, 7] + schedule = [] + if storage.macrs_option_years == 5 + schedule = p.s.financial.macrs_five_year + elseif storage.macrs_option_years == 7 + schedule = p.s.financial.macrs_seven_year + end + macrs_bonus_basis = federal_itc_basis * (1 - storage.total_itc_fraction * storage.macrs_itc_reduction) + macrs_basis = macrs_bonus_basis * (1 - storage.macrs_bonus_fraction) + + depreciation_schedule = zeros(years) + for (i, r) in enumerate(schedule) + if i < length(depreciation_schedule) + depreciation_schedule[i] = macrs_basis * r + end end + depreciation_schedule[1] += storage.macrs_bonus_fraction * macrs_bonus_basis + m.total_depreciation += depreciation_schedule end - depreciation_schedule[1] += storage.macrs_bonus_fraction * macrs_bonus_basis - m.total_depreciation += depreciation_schedule end end From c978da36cd254686c4bf82b1ed13f1cdb1640818 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 13:54:27 -0600 Subject: [PATCH 108/151] make outage stor constraints take b arg --- src/constraints/outage_constraints.jl | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/src/constraints/outage_constraints.jl b/src/constraints/outage_constraints.jl index 8a34f6c2f..752770453 100644 --- a/src/constraints/outage_constraints.jl +++ b/src/constraints/outage_constraints.jl @@ -273,48 +273,48 @@ function add_binMGCHPIsOnInTS_constraints(m, p; _n="") # TODO? make binMGCHPIsOnInTS indexed on p.techs.chp end -function add_MG_storage_dispatch_constraints(m,p) +function add_MG_elec_storage_dispatch_constraints(m,p,b) # initial SOC at start of each outage equals the grid-optimal SOC @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps], - m[:dvMGStoredEnergy][s, tz, 0] <= m[:dvStoredEnergy]["ElectricStorage", tz] + m[:dvMGStoredEnergy][s, tz, 0] <= m[:dvStoredEnergy][b, tz] ) # state of charge @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], m[:dvMGStoredEnergy][s, tz, ts] == m[:dvMGStoredEnergy][s, tz, ts-1] + p.hours_per_time_step * ( - p.s.storage.attr["ElectricStorage"].charge_efficiency * sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) - - m[:dvMGDischargeFromStorage][s, tz, ts] / p.s.storage.attr["ElectricStorage"].discharge_efficiency + p.s.storage.attr[b].charge_efficiency * sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) + - m[:dvMGDischargeFromStorage][s, tz, ts] / p.s.storage.attr[b].discharge_efficiency ) ) - if p.s.storage.attr["ElectricStorage"].soc_min_applies_during_outages + if p.s.storage.attr[b].soc_min_applies_during_outages # Minimum state of charge @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - m[:dvMGStoredEnergy][s, tz, ts] >= p.s.storage.attr["ElectricStorage"].soc_min_fraction * m[:dvStorageEnergy]["ElectricStorage"] + m[:dvMGStoredEnergy][s, tz, ts] >= p.s.storage.attr[b].soc_min_fraction * m[:dvStorageEnergy][b] ) end # Dispatch to MG electrical storage is no greater than inverter capacity # and can't charge the battery unless binMGStorageUsed = 1 @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - m[:dvStoragePower]["ElectricStorage"] >= sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) + m[:dvStoragePower][b] >= sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) ) # Dispatch from MG storage is no greater than inverter capacity # and can't discharge from storage unless binMGStorageUsed = 1 @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - m[:dvStoragePower]["ElectricStorage"] >= m[:dvMGDischargeFromStorage][s, tz, ts] + m[:dvStoragePower][b] >= m[:dvMGDischargeFromStorage][s, tz, ts] ) # Dispatch to and from electrical storage is no greater than power capacity @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - m[:dvStoragePower]["ElectricStorage"] >= m[:dvMGDischargeFromStorage][s, tz, ts] + m[:dvStoragePower][b] >= m[:dvMGDischargeFromStorage][s, tz, ts] + sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) ) # State of charge upper bound is storage system size @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - m[:dvStorageEnergy]["ElectricStorage"] >= m[:dvMGStoredEnergy][s, tz, ts] + m[:dvStorageEnergy][b] >= m[:dvMGStoredEnergy][s, tz, ts] ) if solver_is_compatible_with_indicator_constraints(p.s.settings.solver_name) @@ -327,11 +327,11 @@ function add_MG_storage_dispatch_constraints(m,p) ) else @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) <= p.s.storage.attr["ElectricStorage"].max_kw * m[:binMGStorageUsed] + sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) <= p.s.storage.attr[b].max_kw * m[:binMGStorageUsed] ) @constraint(m, [s in p.s.electric_utility.scenarios, tz in p.s.electric_utility.outage_start_time_steps, ts in p.s.electric_utility.outage_time_steps], - m[:dvMGDischargeFromStorage][s, tz, ts] <= p.s.storage.attr["ElectricStorage"].max_kw * m[:binMGStorageUsed] + m[:dvMGDischargeFromStorage][s, tz, ts] <= p.s.storage.attr[b].max_kw * m[:binMGStorageUsed] ) end end From 38efed4b0d72ae484dc045d03452de66db3f91ea Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 14:24:21 -0600 Subject: [PATCH 109/151] pass b arg into MG elec stor constraints --- src/core/reopt.jl | 14 ++++++++++++-- src/mpc/model.jl | 15 ++++++++++++--- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 2336c953f..37a3383d6 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -405,11 +405,21 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) add_dv_UnservedLoad_constraints(m,p) add_outage_cost_constraints(m,p) add_MG_production_constraints(m,p) - if !isempty(p.s.storage.types.elec) - add_MG_storage_dispatch_constraints(m,p) + + if length(p.s.storage.types.elec) > 1 + throw(@error("REopt does not support considering multiple ElectricStorage when modelling outages.")) + end + if !isempty(p.s.storage.types.elec) #length=1 + b = p.s.storage.types.elec[1] + if p.s.storage.attr[b].max_kw == 0 || p.s.storage.attr[b].max_kwh == 0 + fix_MG_storage_variables(m,p) + else + add_MG_elec_storage_dispatch_constraints(m,p,b) + end else fix_MG_storage_variables(m,p) end + add_cannot_have_MG_with_only_PVwind_constraints(m,p) add_MG_size_constraints(m,p) diff --git a/src/mpc/model.jl b/src/mpc/model.jl index cd1e1809f..2132b80d0 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -171,16 +171,25 @@ function build_mpc!(m::JuMP.AbstractModel, p::MPCInputs) add_previous_monthly_peak_constraint(m, p) add_previous_tou_peak_constraint(m, p) - # TODO: random outages in MPC? if !isempty(p.s.electric_utility.outage_durations) add_dv_UnservedLoad_constraints(m,p) add_outage_cost_constraints(m,p) add_MG_production_constraints(m,p) - if !isempty(p.s.storage.types.elec) - add_MG_storage_dispatch_constraints(m,p) + + if length(p.s.storage.types.elec) > 1 + throw(@error("REopt does not support considering multiple ElectricStorage when modelling outages.")) + end + if !isempty(p.s.storage.types.elec) #length=1 + b = p.s.storage.types.elec[1] + if p.s.storage.attr[b].max_kw == 0 || p.s.storage.attr[b].max_kwh == 0 + fix_MG_storage_variables(m,p) + else + add_MG_elec_storage_dispatch_constraints(m,p,b) + end else fix_MG_storage_variables(m,p) end + add_cannot_have_MG_with_only_PVwind_constraints(m,p) add_MG_size_constraints(m,p) From a800d8216b2c901dd54ae745396381a5fa6fa495 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 30 Aug 2024 15:02:38 -0600 Subject: [PATCH 110/151] organize_multiple_elec_stor_results --- src/core/reopt.jl | 8 +++++++- src/results/electric_storage.jl | 20 ++++++++++++++++++++ 2 files changed, 27 insertions(+), 1 deletion(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 37a3383d6..ec33c606e 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -151,6 +151,9 @@ function run_reopt(ms::AbstractArray{T, 1}, p::REoptInputs) where T <: JuMP.Abst if !isempty(p.techs.pv) organize_multiple_pv_results(p, results_dict) end + if !isempty(p.s.storage.types.elec) + organize_multiple_elec_stor_results(p, results_dict) + end return results_dict else throw(@error("REopt scenarios solved either with errors or non-optimal solutions.")) @@ -405,7 +408,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) add_dv_UnservedLoad_constraints(m,p) add_outage_cost_constraints(m,p) add_MG_production_constraints(m,p) - + if length(p.s.storage.types.elec) > 1 throw(@error("REopt does not support considering multiple ElectricStorage when modelling outages.")) end @@ -570,6 +573,9 @@ function run_reopt(m::JuMP.AbstractModel, p::REoptInputs; organize_pvs=true) if organize_pvs && !isempty(p.techs.pv) # do not want to organize_pvs when running BAU case in parallel b/c then proform code fails organize_multiple_pv_results(p, results) end + if !isempty(p.s.storage.types.elec) + organize_multiple_elec_stor_results(p, results_dict) + end # add error messages (if any) and warnings to results dict results["Messages"] = logger_to_dict() diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index c00fdef5c..1332fca42 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -79,4 +79,24 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::MPCInputs, d::Di d[b] = r nothing +end + +""" + organize_multiple_elec_stor_results(p::REoptInputs, d::Dict) + +The last step in results processing: if more than one ElectricStorage was modeled then move their results from the top +level keys (that use each ElectricStorage.name) to an array of results with "ElectricStorage" as the top key in the results dict `d`. +""" +function organize_multiple_pv_results(p::REoptInputs, d::Dict) + if length(p.s.storage.types.elec) == 1 && p.s.storage.types.elec[1] == "ElectricStorage" + return nothing + end + stors = Dict[] + for storname in p.s.storage.types.elec + d[storname]["name"] = storname # add name to results dict to distinguish each ElectricStorage + push!(stors, d[storname]) + delete!(d, storname) + end + d["ElectricStorage"] = stors + nothing end \ No newline at end of file From 29dda76176ff3537e101f246ef52e5b7ce20da01 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 3 Sep 2024 09:17:21 -0600 Subject: [PATCH 111/151] function name typo correction --- src/results/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 1332fca42..7548052c4 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -87,7 +87,7 @@ end The last step in results processing: if more than one ElectricStorage was modeled then move their results from the top level keys (that use each ElectricStorage.name) to an array of results with "ElectricStorage" as the top key in the results dict `d`. """ -function organize_multiple_pv_results(p::REoptInputs, d::Dict) +function organize_multiple_elec_stor_results(p::REoptInputs, d::Dict) if length(p.s.storage.types.elec) == 1 && p.s.storage.types.elec[1] == "ElectricStorage" return nothing end From 3f9892cbb1d8c9ed9eb22249e4175d69a2dcc4a9 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 3 Sep 2024 09:37:40 -0600 Subject: [PATCH 112/151] create ElectricStorage struct before getting name so always there --- src/core/scenario.jl | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index c76304d7d..c2df410d2 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -172,7 +172,8 @@ function Scenario(d::Dict; flex_hvac_from_json=false) elseif typeof(d["ElectricStorage"]) <: AbstractDict storage_dict = dictkeys_tosymbols(d["ElectricStorage"]) storage_dict[:off_grid_flag] = settings.off_grid_flag - storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) + storage_struct = ElectricStorage(storage_dict, financial) + storage_structs[storage_struct.name] = storage_struct else throw(@error("ElectricStorage input must be Dict or Dict[].")) end From e6f7f6b4140e219d53c6cbaa33993510c622379f Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 3 Sep 2024 11:32:14 -0600 Subject: [PATCH 113/151] correct var name --- src/core/reopt.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index ec33c606e..77910b305 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -574,7 +574,7 @@ function run_reopt(m::JuMP.AbstractModel, p::REoptInputs; organize_pvs=true) organize_multiple_pv_results(p, results) end if !isempty(p.s.storage.types.elec) - organize_multiple_elec_stor_results(p, results_dict) + organize_multiple_elec_stor_results(p, results) end # add error messages (if any) and warnings to results dict From 7dde72baac8c661576e6f79197c355782e3ca35b Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 3 Sep 2024 13:43:29 -0600 Subject: [PATCH 114/151] safe get ElectricStorage name because only included if multiple --- src/outagesim/backup_reliability.jl | 2 +- src/outagesim/outage_simulator.jl | 2 +- src/results/proforma.jl | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/outagesim/backup_reliability.jl b/src/outagesim/backup_reliability.jl index b50037b24..1cd70e762 100644 --- a/src/outagesim/backup_reliability.jl +++ b/src/outagesim/backup_reliability.jl @@ -724,7 +724,7 @@ function backup_reliability_reopt_inputs(;d::Dict, p::REoptInputs, r::Dict = Dic elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) throw(@error("Calculating resilience metrics for a REopt solution with multiple ElectricStorage is not yet supported.")) end - elec_stor_name = d["ElectricStorage"]["name"] + elec_stor_name = get(d["ElectricStorage"],"name","ElectricStorage") r2[:battery_charge_efficiency] = p.s.storage.attr[elec_stor_name].charge_efficiency r2[:battery_discharge_efficiency] = p.s.storage.attr[elec_stor_name].discharge_efficiency r2[:battery_size_kw] = get(d["ElectricStorage"], "size_kw", 0) diff --git a/src/outagesim/outage_simulator.jl b/src/outagesim/outage_simulator.jl index 0a60f6cc7..acff7a451 100644 --- a/src/outagesim/outage_simulator.jl +++ b/src/outagesim/outage_simulator.jl @@ -255,7 +255,7 @@ function simulate_outages(d::Dict, p::REoptInputs; microgrid_only::Bool=false) elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) throw(@error("simulate_outages for a REopt solution with multiple ElectricStorage is not yet supported")) end - elec_stor_name = d["ElectricStorage"]["name"] + elec_stor_name = get(d["ElectricStorage"],"name","ElectricStorage") batt_kwh = get(d["ElectricStorage"], "size_kwh", 0) batt_kw = get(d["ElectricStorage"], "size_kw", 0) init_soc = get(d["ElectricStorage"], "soc_series_fraction", zeros(length(p.time_steps))) diff --git a/src/results/proforma.jl b/src/results/proforma.jl index 8a4abd1e1..ed442058d 100644 --- a/src/results/proforma.jl +++ b/src/results/proforma.jl @@ -70,7 +70,7 @@ function proforma_results(p::REoptInputs, d::Dict) elseif !(typeof(d["ElectricStorage"]) <: AbstractDict) throw(@error("Pro forma results for a REopt solution with multiple ElectricStorage is not yet supported")) end - elec_stor_name = d["ElectricStorage"]["name"] + elec_stor_name = get(d["ElectricStorage"],"name","ElectricStorage") if d["ElectricStorage"]["size_kw"] > 0 # TODO handle other types of storage storage = p.s.storage.attr[elec_stor_name] From fd206abf2f88f5903d13e8786975894cc06b5cac Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 3 Sep 2024 17:11:15 -0600 Subject: [PATCH 115/151] add multiple elec stor test --- test/debug_reopt_results.json | 105271 +++++++++++++++++++ test/runtests.jl | 20 + test/scenarios/multiple_elec_storage.json | 69 + 3 files changed, 105360 insertions(+) create mode 100644 test/debug_reopt_results.json create mode 100644 test/scenarios/multiple_elec_storage.json diff --git a/test/debug_reopt_results.json b/test/debug_reopt_results.json new file mode 100644 index 000000000..f2f1f8ec9 --- /dev/null +++ b/test/debug_reopt_results.json @@ -0,0 +1,105271 @@ +{ + "ElectricStorage": [ + { + "storage_to_grid_series_kw": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 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1.21894277735e7, + "lifecycle_storage_capital_costs": 66248.8454, + "lifecycle_om_costs_before_tax": 41554.032, + "lifecycle_emissions_cost_climate": 2.71682868e6, + "lifecycle_fuel_costs_after_tax": 0.0, + "lifecycle_capital_costs": 177425.972, + "lifecycle_production_incentive_after_tax": 0.0, + "lifecycle_offgrid_other_capital_costs": 0.0, + "om_and_replacement_present_cost_after_tax": 36422.0615, + "replacements_future_cost_after_tax": 41726.523, + "developer_om_and_replacement_present_cost_after_tax": 36422.0615, + "year_one_om_costs_after_tax": 2080.0, + "lifecycle_chp_standby_cost_after_tax": 0.0 + }, + "solver_seconds": 20.639 +} diff --git a/test/runtests.jl b/test/runtests.jl index c61cc6a08..cdf16a707 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -20,7 +20,27 @@ elseif "CPLEX" in ARGS @testset "test_with_cplex" begin include("test_with_cplex.jl") end +elseif "Debug" in ARGS + @testset "Multiple Electric Storage" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(model, "./scenarios/multiple_elec_storage.json") + open("debug_reopt_results.json","w") do f + JSON.print(f, r, 4) + end + + @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 + for stor in r["ElectricStorage"] + if stor["name"] == "CheapElecStor" + @test stor["size_kw"] ≈ 49.0 atol=0.1 + @test stor["size_kwh"] ≈ 83.3 atol=0.1 + else + @test stor["size_kw"] ≈ 0 atol=0.1 + @test stor["size_kwh"] ≈ 0 atol=0.1 + end + end + end else # run HiGHS tests @testset "Inputs" begin diff --git a/test/scenarios/multiple_elec_storage.json b/test/scenarios/multiple_elec_storage.json new file mode 100644 index 000000000..cbf135f20 --- /dev/null +++ b/test/scenarios/multiple_elec_storage.json @@ -0,0 +1,69 @@ +{ + "Site": { + "longitude": -118.1164613, + "latitude": 34.5794343, + "roof_squarefeet": 5000.0, + "land_acres": 1.0, + "node": 3 + }, + "ElectricUtility" : { + "co2_from_avert" : true + }, + "PV": { + "macrs_bonus_fraction": 0.4, + "installed_cost_per_kw": 2000.0, + "tilt": 34.579, + "degradation_fraction": 0.005, + "macrs_option_years": 5, + "federal_itc_fraction": 0.3, + "module_type": 0, + "array_type": 1, + "om_cost_per_kw": 16.0, + "macrs_itc_reduction": 0.5, + "azimuth": 180.0, + "federal_rebate_per_kw": 350.0 + }, + "ElectricLoad": { + "doe_reference_name": "RetailStore", + "annual_kwh": 10000000.0, + "year": 2017 + }, + "ElectricStorage": [ + { + "name": "CheapElecStor", + "total_rebate_per_kw": 100.0, + "macrs_option_years": 5, + "can_grid_charge": true, + "macrs_bonus_fraction": 0.4, + "replace_cost_per_kw": 460.0, + "replace_cost_per_kwh": 230.0, + "installed_cost_per_kw": 1000.0, + "installed_cost_per_kwh": 500.0, + "total_itc_fraction": 0.0 + }, + { + "name": "ExpensiveElecStor", + "total_rebate_per_kw": 100.0, + "macrs_option_years": 5, + "can_grid_charge": true, + "macrs_bonus_fraction": 0.4, + "replace_cost_per_kw": 460.0, + "replace_cost_per_kwh": 230.0, + "installed_cost_per_kw": 1500.0, + "installed_cost_per_kwh": 500.0, + "total_itc_fraction": 0.0 + } + ], + "ElectricTariff": { + "urdb_label": "5ed6c1a15457a3367add15ae" + }, + "Financial": { + "elec_cost_escalation_rate_fraction": 0.026, + "offtaker_discount_rate_fraction": 0.081, + "owner_discount_rate_fraction": 0.081, + "analysis_years": 20, + "offtaker_tax_rate_fraction": 0.4, + "owner_tax_rate_fraction": 0.4, + "om_cost_escalation_rate_fraction": 0.025 + } +} From 52922e439c19800c120f2653e1a221ccd97ecaec Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 4 Sep 2024 16:52:29 -0600 Subject: [PATCH 116/151] fix edit to constrain_degradation_variables --- src/constraints/battery_degradation.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/battery_degradation.jl b/src/constraints/battery_degradation.jl index 5f990fa0a..91fdd147d 100644 --- a/src/constraints/battery_degradation.jl +++ b/src/constraints/battery_degradation.jl @@ -70,7 +70,7 @@ function add_degradation(m, p, b) @variable(m, SOH[days]) add_degradation_variables(m, p) - constrain_degradation_variables(m, p, b=b) + constrain_degradation_variables(m, p, b) @constraint(m, [d in 2:days[end]], SOH[d] == SOH[d-1] - p.hours_per_time_step * ( From 457acd19a771b83749825f94368a7770c7de4861 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 4 Sep 2024 16:57:15 -0600 Subject: [PATCH 117/151] debugging --- ..._reopt_results.json => deg_results_1.json} | 65354 +-- test/deg_results_2.json | 112569 ++++++ test/deg_results_3.json | 63 + test/deg_results_4.json | 63 + test/import_export_results.json | 78977 ++++ test/runtests.jl | 206 +- test/unserved_results_1.json | 307960 +++++++++++++++ test/unserved_results_2.json | 307960 +++++++++++++++ test/unserved_results_3.json | 87247 ++++ test/unserved_results_4.json | 35239 ++ test/unserved_results_5.json | 133630 +++++++ test/unserved_results_6.json | 28 + test/wind_results_1.json | 15 + test/wind_results_2.json | 22 + test/wind_results_3.json | 22 + 15 files changed, 1101121 insertions(+), 28234 deletions(-) rename test/{debug_reopt_results.json => deg_results_1.json} (70%) create mode 100644 test/deg_results_2.json create mode 100644 test/deg_results_3.json create mode 100644 test/deg_results_4.json create mode 100644 test/import_export_results.json create mode 100644 test/unserved_results_1.json create mode 100644 test/unserved_results_2.json create mode 100644 test/unserved_results_3.json create mode 100644 test/unserved_results_4.json create mode 100644 test/unserved_results_5.json create mode 100644 test/unserved_results_6.json create mode 100644 test/wind_results_1.json create mode 100644 test/wind_results_2.json create mode 100644 test/wind_results_3.json diff --git a/test/debug_reopt_results.json b/test/deg_results_1.json similarity index 70% rename from test/debug_reopt_results.json rename to test/deg_results_1.json index f2f1f8ec9..decd18738 100644 --- a/test/debug_reopt_results.json +++ b/test/deg_results_1.json @@ -1,26324 +1,26311 @@ { - "ElectricStorage": [ - { - "storage_to_grid_series_kw": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 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@@ -27417,10 +27404,10 @@ 1828.191, 1862.067, 1908.849, - 2064.28, + 2015.232, 1684.891, - 1670.75, - 1458.706, + 1684.891, + 1507.754, 609.084, 331.071, 331.071, @@ -27431,8 +27418,8 @@ 331.071, 331.071, 641.606, - 735.739, - 1212.457, + 778.477, + 1198.317, 1831.166, 1806.515, 1794.792, @@ -27441,10 +27428,10 @@ 1827.965, 1862.522, 1912.948, - 2068.184, + 2019.137, 1689.466, - 1676.589, - 1465.159, + 1690.729, + 1514.207, 609.084, 331.071, 331.071, @@ -27455,7 +27442,7 @@ 318.763, 318.763, 640.595, - 567.013, + 608.8, 861.899, 1347.4, 1804.832, @@ -27465,10 +27452,10 @@ 1828.288, 1879.743, 1951.054, - 2064.28, - 1507.754, - 1139.34, - 1104.524, + 2015.232, + 1493.613, + 1153.481, + 1153.572, 801.212, 318.763, 318.763, @@ -27527,8 +27514,8 @@ 337.228, 339.055, 620.984, - 743.035, - 1212.709, + 786.503, + 1198.568, 1831.324, 1808.409, 1795.662, @@ -27538,9 +27525,9 @@ 1859.414, 1906.473, 2062.153, + 1635.843, 1684.891, - 1670.75, - 1458.706, + 1507.754, 609.084, 331.071, 331.071, @@ -27551,8 +27538,8 @@ 334.071, 334.663, 616.644, - 739.381, - 1212.155, + 780.945, + 1163.107, 1836.973, 1814.958, 1800.172, @@ -27564,7 +27551,7 @@ 2061.687, 1684.891, 1670.75, - 1458.706, + 1507.754, 609.084, 331.071, 331.071, @@ -27575,8 +27562,8 @@ 331.071, 331.071, 610.692, - 738.073, - 1275.168, + 779.462, + 1226.12, 1933.683, 1942.431, 1856.483, @@ -27586,9 +27573,9 @@ 1892.104, 1928.344, 2063.859, - 1684.891, 1670.75, - 1458.706, + 1684.891, + 1507.754, 609.084, 331.071, 331.071, @@ -27600,7 +27587,7 @@ 331.071, 610.984, 728.426, - 1199.319, + 1185.179, 1816.79, 1790.81, 1787.785, @@ -27610,9 +27597,9 @@ 1847.27, 1899.326, 2062.637, + 1635.843, 1684.891, - 1670.75, - 1458.706, + 1507.754, 609.084, 331.071, 331.071, @@ -27624,7 +27611,7 @@ 318.763, 598.518, 550.943, - 845.115, + 830.975, 1339.962, 1799.268, 1791.206, @@ -27634,9 +27621,9 @@ 1857.011, 1902.718, 2028.299, - 1507.754, - 1139.34, - 1104.433, + 1458.706, + 1153.481, + 1153.481, 799.207, 318.763, 318.763, @@ -27696,7 +27683,7 @@ 333.424, 580.287, 778.884, - 1199.781, + 1150.733, 1818.912, 1827.013, 1823.507, @@ -27708,7 +27695,7 @@ 2029.524, 1684.891, 1670.75, - 1458.706, + 1507.754, 609.084, 331.071, 331.071, @@ -27719,7 +27706,7 @@ 331.071, 332.522, 580.422, - 729.7, + 778.747, 1198.95, 1816.644, 1791.483, @@ -27730,9 +27717,9 @@ 1848.06, 1960.041, 2026.746, + 1635.843, 1684.891, - 1670.75, - 1458.706, + 1493.613, 609.084, 331.071, 331.071, @@ -27743,9 +27730,9 @@ 331.071, 331.071, 580.257, - 774.819, - 1242.17, - 1911.78, + 780.531, + 1193.122, + 1897.64, 1892.148, 1857.512, 1946.329, @@ -27755,8 +27742,8 @@ 1935.209, 2030.017, 1684.891, - 1670.75, - 1458.706, + 1684.891, + 1507.754, 609.084, 331.071, 331.071, @@ -27768,7 +27755,7 @@ 331.071, 569.501, 761.861, - 1246.005, + 1196.958, 1888.379, 1868.597, 1841.943, @@ -27777,10 +27764,10 @@ 1895.994, 1917.721, 1942.633, - 2030.017, + 2015.876, 1684.891, - 1670.75, - 1458.706, + 1684.891, + 1507.754, 609.084, 331.071, 331.071, @@ -27791,8 +27778,8 @@ 318.763, 318.763, 560.263, - 576.024, - 889.447, + 598.35, + 875.306, 1386.542, 1830.55, 1832.365, @@ -27802,9 +27789,9 @@ 2053.092, 2042.845, 2058.967, - 1512.855, - 1139.34, - 1104.433, + 1463.808, + 1153.481, + 1153.481, 799.207, 318.763, 318.763, @@ -27863,7 +27850,7 @@ 331.071, 331.071, 577.292, - 759.818, + 783.719, 1252.781, 1888.137, 1808.628, @@ -27874,9 +27861,9 @@ 1861.555, 1906.954, 2023.998, - 1684.891, + 1635.843, 1670.993, - 1459.977, + 1509.025, 609.084, 331.071, 331.071, @@ -27888,7 +27875,7 @@ 331.071, 535.981, 725.776, - 1203.221, + 1189.08, 1822.698, 1828.333, 1820.988, @@ -27898,9 +27885,9 @@ 1879.059, 1903.235, 2028.402, + 1635.843, 1684.891, - 1670.75, - 1458.706, + 1507.754, 609.084, 331.071, 331.071, @@ -27910,11 +27897,11 @@ 331.071, 331.071, 331.071, - 532.352, + 536.83, 717.721, - 1194.861, + 1145.814, 1817.724, - 1794.908, + 1780.768, 1787.785, 1787.785, 1798.78, @@ -27923,8 +27910,8 @@ 1914.899, 1990.542, 1684.891, - 1670.75, - 1458.706, + 1684.891, + 1507.754, 609.084, 331.071, 331.071, @@ -27935,8 +27922,8 @@ 331.071, 331.071, 530.524, - 733.275, - 1201.02, + 774.365, + 1186.88, 1825.044, 1803.461, 1795.546, @@ -27946,9 +27933,9 @@ 1884.191, 1917.934, 1988.752, + 1635.843, 1684.891, - 1670.75, - 1458.706, + 1507.754, 609.084, 331.071, 331.071, @@ -27961,7 +27948,7 @@ 519.67, 544.927, 873.385, - 1343.647, + 1329.506, 1802.871, 1791.952, 1792.717, @@ -27970,9 +27957,9 @@ 1859.526, 1905.459, 1987.983, - 1507.754, - 1139.34, - 1104.433, + 1458.706, + 1153.481, + 1153.481, 799.207, 318.763, 318.763, @@ -28031,8 +28018,8 @@ 331.071, 711.137, 807.576, - 1251.982, - 1858.648, + 1301.03, + 1844.507, 1822.802, 1800.198, 1788.979, @@ -28042,9 +28029,9 @@ 1857.157, 1903.655, 1504.435, - 1684.891, - 1493.613, - 560.036, + 1635.843, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28056,7 +28043,7 @@ 710.563, 805.89, 1248.874, - 1857.43, + 1843.289, 1825.098, 1805.593, 1795.811, @@ -28067,8 +28054,8 @@ 1904.281, 1504.395, 1684.891, - 1493.613, - 560.036, + 1458.706, + 609.084, 331.071, 331.071, 331.071, @@ -28080,7 +28067,7 @@ 717.656, 776.832, 1252.766, - 1855.929, + 1841.788, 1820.832, 1797.46, 1787.785, @@ -28090,9 +28077,9 @@ 1859.033, 1904.697, 1504.513, - 1684.891, - 1493.613, - 560.036, + 1635.843, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28104,7 +28091,7 @@ 717.498, 776.536, 1254.349, - 1861.138, + 1812.09, 1831.669, 1812.313, 1805.08, @@ -28114,9 +28101,9 @@ 1859.308, 1903.784, 1504.028, - 1684.891, - 1493.613, - 560.036, + 1670.75, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28126,9 +28113,9 @@ 318.763, 318.763, 699.624, - 594.642, + 604.105, 892.436, - 1370.703, + 1356.562, 1818.357, 1794.795, 1787.785, @@ -28138,9 +28125,9 @@ 1865.096, 1918.746, 1328.857, + 1104.433, 1153.481, - 1139.34, - 750.16, + 799.207, 318.763, 318.763, 318.763, @@ -28198,11 +28185,11 @@ 331.071, 331.071, 717.159, - 779.714, - 1275.781, - 1899.04, + 784.269, + 1310.179, + 1849.992, 1835.016, - 1821.171, + 1807.03, 1827.45, 1828.892, 1835.711, @@ -28211,8 +28198,8 @@ 1905.068, 1469.493, 1684.891, - 1493.613, - 560.036, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28222,9 +28209,9 @@ 331.071, 331.071, 713.88, - 771.62, - 1253.143, - 1862.309, + 782.121, + 1302.191, + 1848.168, 1831.057, 1812.298, 1801.915, @@ -28234,9 +28221,9 @@ 1865.131, 1912.429, 1475.085, - 1692.429, - 1502.618, - 560.036, + 1643.381, + 1516.759, + 609.084, 331.071, 331.071, 331.071, @@ -28246,7 +28233,7 @@ 341.79, 343.747, 690.017, - 783.094, + 793.558, 1260.675, 1866.222, 1831.667, @@ -28258,9 +28245,9 @@ 1861.072, 1905.987, 1469.607, - 1684.891, - 1493.613, - 560.036, + 1670.75, + 1458.706, + 609.084, 331.071, 331.071, 331.071, @@ -28271,8 +28258,8 @@ 331.071, 682.164, 772.688, - 1251.448, - 1885.192, + 1300.496, + 1836.145, 1861.445, 1816.925, 1824.756, @@ -28284,7 +28271,7 @@ 1472.032, 1684.891, 1493.947, - 560.036, + 609.084, 331.071, 331.071, 331.071, @@ -28296,7 +28283,7 @@ 668.44, 595.095, 899.637, - 1405.352, + 1356.305, 1845.249, 1806.663, 1798.066, @@ -28306,9 +28293,9 @@ 1876.672, 1920.608, 1292.9, - 1153.481, 1139.34, - 750.506, + 1153.481, + 799.553, 318.763, 318.763, 318.763, @@ -28366,8 +28353,8 @@ 331.071, 331.071, 675.43, - 765.739, - 1245.32, + 778.682, + 1294.368, 1854.068, 1821.085, 1801.256, @@ -28378,9 +28365,9 @@ 1859.535, 1937.33, 1468.987, - 1684.891, - 1493.613, - 560.036, + 1635.843, + 1507.754, + 594.943, 331.071, 331.071, 331.071, @@ -28392,9 +28379,9 @@ 675.439, 765.62, 1246.165, - 1859.362, + 1810.314, 1829.475, - 1831.503, + 1817.363, 1959.114, 1987.145, 1995.289, @@ -28403,8 +28390,8 @@ 1940.619, 1480.679, 1685.839, - 1493.613, - 560.036, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28414,8 +28401,8 @@ 331.071, 331.071, 674.919, - 764.544, - 1269.527, + 777.859, + 1303.767, 1889.014, 1831.152, 1873.553, @@ -28426,9 +28413,9 @@ 2040.324, 2055.266, 1513.599, - 1700.151, + 1651.103, 1502.017, - 560.036, + 609.084, 331.071, 331.071, 331.071, @@ -28439,9 +28426,9 @@ 331.071, 634.775, 758.464, - 1234.672, - 1846.998, - 1819.456, + 1283.72, + 1832.857, + 1770.408, 1913.582, 2066.296, 2144.91, @@ -28451,8 +28438,8 @@ 2055.049, 1516.708, 1695.951, - 1497.255, - 560.036, + 1511.395, + 609.084, 331.071, 331.071, 331.071, @@ -28534,8 +28521,8 @@ 331.071, 331.071, 644.375, - 764.273, - 1244.731, + 780.518, + 1293.779, 1875.261, 1854.848, 1817.697, @@ -28546,9 +28533,9 @@ 1863.495, 1907.099, 1468.591, - 1650.627, - 1493.613, - 560.036, + 1636.487, + 1458.706, + 609.084, 331.071, 331.071, 331.071, @@ -28558,8 +28545,8 @@ 331.071, 331.071, 643.563, - 762.976, - 1242.389, + 779.843, + 1291.437, 1890.205, 1846.09, 1804.528, @@ -28570,9 +28557,9 @@ 1859.064, 1905.194, 1468.091, - 1650.627, + 1601.58, 1493.613, - 560.036, + 609.084, 331.071, 331.071, 331.071, @@ -28583,8 +28570,8 @@ 331.071, 643.033, 761.775, - 1239.609, - 1848.043, + 1260.883, + 1833.902, 1813.9, 1793.676, 1787.785, @@ -28594,9 +28581,9 @@ 1898.016, 1904.811, 1467.704, - 1650.627, - 1493.613, - 560.036, + 1601.58, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28606,10 +28593,10 @@ 331.071, 331.071, 606.104, - 759.264, - 1236.709, + 777.926, + 1285.756, 1846.988, - 1813.592, + 1799.452, 1790.96, 1787.785, 1787.785, @@ -28618,9 +28605,9 @@ 1858.038, 1904.988, 1467.658, - 1650.627, - 1493.613, - 560.036, + 1601.58, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28631,10 +28618,10 @@ 318.763, 591.812, 580.567, - 881.52, - 1366.633, + 930.567, + 1317.586, 1817.352, - 1799.179, + 1785.039, 1792.32, 1796.177, 1808.607, @@ -28643,8 +28630,8 @@ 1908.951, 1293.203, 1119.935, - 1139.34, - 750.16, + 1153.481, + 799.207, 318.763, 318.763, 318.763, @@ -28702,11 +28689,11 @@ 331.071, 331.071, 603.085, - 756.113, + 775.493, 1235.557, - 1851.056, + 1836.915, 1820.465, - 1812.61, + 1763.563, 1846.236, 1822.857, 1836.003, @@ -28715,8 +28702,8 @@ 1936.027, 1488.537, 1659.403, - 1494.819, - 560.036, + 1508.959, + 609.084, 331.071, 331.071, 331.071, @@ -28728,7 +28715,7 @@ 601.72, 774.852, 1236.031, - 1897.406, + 1883.265, 1817.872, 1932.497, 1872.655, @@ -28740,7 +28727,7 @@ 1956.084, 1980.18, 1719.328, - 594.943, + 609.084, 331.071, 331.071, 331.071, @@ -28754,7 +28741,7 @@ 1234.511, 1929.383, 2006.543, - 2093.254, + 2079.114, 2156.526, 2235.152, 2200.145, @@ -28762,9 +28749,9 @@ 2278.246, 2222.321, 1676.115, - 1748.651, - 1508.065, - 560.036, + 1699.604, + 1522.206, + 609.084, 331.071, 331.071, 331.071, @@ -28775,8 +28762,8 @@ 331.071, 599.689, 772.926, - 1235.963, - 1854.314, + 1285.01, + 1805.266, 2072.432, 2250.221, 2362.586, @@ -28785,10 +28772,10 @@ 2250.866, 2177.524, 2156.48, - 1604.366, + 1590.225, 1672.77, - 1501.599, - 560.036, + 1515.74, + 609.084, 331.071, 331.071, 331.071, @@ -28798,10 +28785,10 @@ 318.763, 318.763, 555.324, - 582.22, - 876.297, - 1362.317, - 1812.271, + 597.429, + 925.345, + 1348.177, + 1763.224, 1795.257, 1795.292, 1795.244, @@ -28811,8 +28798,8 @@ 1912.653, 1297.376, 1119.728, - 1139.34, - 750.16, + 1153.481, + 799.207, 318.763, 318.763, 318.763, @@ -28873,7 +28860,7 @@ 754.156, 1236.46, 1857.283, - 1831.891, + 1817.751, 1817.222, 1809.569, 1807.118, @@ -28882,9 +28869,9 @@ 1865.965, 1908.873, 1469.222, - 1616.364, - 1493.613, - 560.036, + 1567.317, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28894,9 +28881,9 @@ 331.071, 331.071, 567.696, - 758.288, - 1237.77, - 1855.887, + 775.584, + 1286.817, + 1806.839, 1849.435, 1833.509, 1802.2, @@ -28906,9 +28893,9 @@ 1867.126, 1919.698, 1469.236, - 1616.387, - 1493.613, - 560.036, + 1602.247, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28919,11 +28906,11 @@ 331.071, 567.932, 754.739, - 1235.915, - 1854.529, + 1284.963, + 1805.481, 1827.861, 1813.857, - 1824.108, + 1809.968, 1847.835, 1861.287, 1880.329, @@ -28931,8 +28918,8 @@ 1945.464, 1483.115, 1623.605, - 1493.613, - 560.036, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28942,9 +28929,9 @@ 331.071, 331.071, 565.632, - 753.899, - 1240.603, - 1859.034, + 774.319, + 1289.651, + 1844.894, 1830.735, 1810.848, 1805.39, @@ -28954,9 +28941,9 @@ 1873.513, 1912.134, 1469.719, - 1616.364, - 1493.613, - 560.036, + 1567.317, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -28966,8 +28953,8 @@ 318.763, 318.763, 556.714, - 578.66, - 885.305, + 599.174, + 934.352, 1376.126, 1832.372, 1817.118, @@ -28978,9 +28965,9 @@ 1870.88, 1911.243, 1292.3, - 1084.954, - 1139.781, - 752.641, + 1070.814, + 1104.874, + 801.688, 318.763, 318.763, 318.763, @@ -29039,9 +29026,9 @@ 331.071, 547.486, 770.327, - 1244.395, - 1862.365, - 1826.934, + 1265.669, + 1813.318, + 1812.793, 1830.415, 1884.238, 1869.826, @@ -29051,8 +29038,8 @@ 1941.859, 1474.173, 1616.364, - 1493.613, - 560.036, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -29062,12 +29049,12 @@ 331.071, 331.071, 539.6, - 761.606, + 778.501, 1256.586, - 1896.811, + 1847.763, 1884.313, 1924.995, - 1863.651, + 1849.51, 1835.913, 1850.508, 1897.372, @@ -29075,8 +29062,8 @@ 1922.087, 1469.17, 1616.364, - 1493.613, - 560.036, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -29086,10 +29073,10 @@ 331.071, 331.071, 534.105, - 754.946, + 776.58, 1237.771, 1853.934, - 1823.835, + 1809.695, 1804.451, 1795.313, 1796.736, @@ -29098,9 +29085,9 @@ 1921.843, 1938.972, 1475.436, - 1616.364, - 1493.613, - 560.036, + 1567.317, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -29110,8 +29097,8 @@ 331.071, 331.071, 534.244, - 753.112, - 1234.478, + 774.386, + 1283.525, 1851.302, 1823.167, 1804.903, @@ -29122,9 +29109,9 @@ 1868.463, 1910.917, 1468.217, - 1616.364, + 1567.317, 1493.613, - 560.036, + 609.084, 331.071, 331.071, 331.071, @@ -29134,8 +29121,8 @@ 318.763, 318.763, 519.88, - 575.132, - 882.48, + 596.406, + 931.528, 1371.537, 1824.6, 1805.93, @@ -29146,9 +29133,9 @@ 1872.268, 1911.765, 1294.645, - 1089.107, + 1040.059, 1143.571, - 755.736, + 804.784, 318.763, 318.763, 318.763, @@ -29208,10 +29195,10 @@ 503.38, 756.228, 1237.407, - 1854.437, + 1805.389, 1827.476, 1811.008, - 1806.986, 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1153.553, + 801.385, 318.763, 318.763, 318.763, @@ -29711,9 +29698,9 @@ 296.808, 498.689, 753.103, - 1238.297, - 1862.355, - 1838.636, + 1287.345, + 1813.307, + 1824.496, 1824.33, 1846.82, 1903.762, @@ -29723,8 +29710,8 @@ 2029.068, 1574.876, 1568.627, - 1493.968, - 560.036, + 1508.108, + 609.084, 331.071, 331.071, 331.071, @@ -29735,20 +29722,20 @@ 296.808, 492.501, 750.248, - 1239.557, - 1864.155, + 1260.831, + 1815.108, 1839.07, 1836.171, 1909.758, - 1951.052, + 1936.912, 1962.717, 1983.785, 2011.564, 2049.03, 1584.694, 1563.344, - 1495.175, - 560.036, + 1509.316, + 609.084, 331.071, 331.071, 331.071, @@ -29759,9 +29746,9 @@ 296.808, 493.06, 750.118, - 1250.986, - 1858.726, - 1831.298, + 1300.034, + 1809.678, + 1817.158, 1842.362, 1805.604, 1808.085, @@ -29771,8 +29758,8 @@ 1968.711, 1499.078, 1547.832, - 1493.613, - 560.036, + 1507.754, + 609.084, 331.071, 331.071, 331.071, @@ -29781,11 +29768,11 @@ 331.071, 331.071, 296.808, - 492.457, + 497.504, 749.11, - 1234.477, - 1855.466, - 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331.071, 331.071, 331.071, @@ -29925,11 +29912,11 @@ 331.071, 331.071, 262.545, - 486.798, - 748.966, - 1253.635, - 1908.187, - 1881.533, + 492.752, + 764.794, + 1302.175, + 1859.139, + 1867.392, 1961.819, 2054.609, 2153.685, @@ -29939,8 +29926,8 @@ 2170.572, 1667.511, 1605.058, - 1504.481, - 560.036, + 1518.622, + 609.084, 331.071, 331.071, 331.071, @@ -30647,13 +30634,13 @@ 250.237, 476.844, 574.974, - 888.652, + 913.863, 1495.705, 2111.689, 2214.882, 2218.778, 2313.415, - 2348.919, + 2337.484, 2356.565, 2356.565, 2356.565, @@ -30887,12 +30874,12 @@ 296.808, 488.65, 755.684, - 1303.984, + 1325.258, 2219.321, 2290.736, 2390.086, 2390.086, - 2511.774, + 2462.727, 2494.796, 2558.274, 2558.274, @@ -31582,7 +31569,7 @@ 331.071, 331.071, 525.884, - 753.917, + 770.765, 1366.933, 2342.59, 2515.333, @@ -32717,10 +32704,10 @@ 2747.246, 2747.246, 2747.246, - 2747.246, + 2742.82, 2738.003, - 2733.783, - 2607.985, + 2684.735, + 2600.382, 2054.348, 2080.687, 1856.412, @@ -32903,7 +32890,7 @@ 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318.763, 318.763, 318.763, @@ -33406,10 +33393,10 @@ 331.071, 331.071, 700.098, - 759.459, - 1234.235, + 771.706, + 1283.283, 1850.822, - 1823.773, + 1809.632, 1807.607, 1812.557, 1849.034, @@ -33418,9 +33405,9 @@ 2012.552, 2055.689, 1722.199, - 1702.747, - 1505.936, - 560.036, + 1653.7, + 1520.077, + 609.084, 331.071, 331.071, 331.071, @@ -33430,9 +33417,9 @@ 331.071, 331.071, 700.073, - 762.412, - 1241.136, - 1858.704, + 771.538, + 1290.184, + 1809.657, 1829.556, 1824.439, 1876.323, @@ -33441,10 +33428,10 @@ 2016.168, 2030.671, 2054.232, - 1715.178, + 1701.037, 1698.379, - 1504.395, - 560.036, + 1518.536, + 609.084, 331.071, 331.071, 331.071, @@ -33454,9 +33441,9 @@ 331.071, 331.071, 699.709, - 796.982, - 1241.723, - 1858.388, + 805.531, + 1290.77, + 1844.247, 1827.313, 1809.364, 1802.923, @@ -33465,10 +33452,10 @@ 1870.989, 1911.282, 1959.602, - 1699.14, + 1650.093, 1698.059, - 1502.018, - 560.036, + 1516.159, + 609.084, 331.071, 331.071, 331.071, @@ -33489,10 +33476,10 @@ 1912.813, 1952.796, 2001.278, - 1714.971, - 1705.306, - 1504.678, - 560.036, + 1700.831, + 1656.258, + 1518.819, + 609.084, 331.071, 331.071, 331.071, @@ -33502,8 +33489,8 @@ 318.763, 318.763, 691.273, - 620.819, - 876.447, + 632.014, + 925.495, 1361.637, 1814.244, 1797.161, @@ -33513,10 +33500,10 @@ 1877.44, 1914.8, 1968.01, - 1527.066, - 1157.039, - 1139.34, - 750.16, + 1478.018, + 1142.899, + 1153.481, + 799.207, 318.763, 318.763, 318.763, @@ -33574,10 +33561,10 @@ 331.071, 331.071, 698.891, - 795.739, + 805.164, 1233.933, 1850.094, - 1886.831, + 1872.69, 2058.599, 2187.887, 2291.91, @@ -33587,8 +33574,8 @@ 2139.962, 1814.86, 1739.033, - 1516.103, - 560.036, + 1481.196, + 609.084, 331.071, 331.071, 331.071, @@ -33598,9 +33585,9 @@ 331.071, 331.071, 698.991, - 796.021, - 1235.316, - 1854.067, + 805.261, + 1284.363, + 1839.927, 1991.469, 2062.749, 2051.844, @@ -33609,10 +33596,10 @@ 2089.704, 2126.113, 2188.6, - 1767.045, + 1717.998, 1713.777, - 1514.671, - 560.036, + 1528.812, + 609.084, 331.071, 331.071, 331.071, @@ -33624,8 +33611,8 @@ 699.287, 797.491, 1236.512, - 1851.088, - 1818.607, + 1802.041, + 1804.466, 1821.762, 1884.709, 1929.616, @@ -33635,8 +33622,8 @@ 2012.092, 1704.82, 1693.892, - 1494.621, - 560.036, + 1508.762, + 609.084, 331.071, 331.071, 331.071, @@ -33647,7 +33634,7 @@ 331.071, 705.881, 804.938, - 1239.537, + 1288.584, 1852.898, 1822.193, 1806.345, @@ -33657,10 +33644,10 @@ 1883.093, 1920.733, 2010.156, - 1712.218, - 1699.222, - 1499.431, - 560.036, + 1698.078, + 1650.174, + 1513.572, + 609.084, 331.071, 331.071, 331.071, @@ -33671,8 +33658,8 @@ 318.763, 691.024, 624.049, - 877.618, - 1362.48, + 926.666, + 1348.339, 1813.266, 1794.796, 1788.936, @@ -33681,10 +33668,10 @@ 1842.504, 1883.908, 1977.618, - 1507.754, + 1458.706, 1153.481, - 1139.34, - 750.387, + 1153.481, + 799.435, 318.763, 318.763, 318.763, @@ -33742,8 +33729,8 @@ 331.071, 331.071, 331.071, - 564.285, - 710.271, + 568.674, + 759.319, 1194.497, 1825.378, 1809.234, @@ -33753,10 +33740,10 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@@ 318.763, 318.763, 215.974, - 95.221, + 113.184, 528.082, 505.774, 975.905, @@ -34137,10 +34124,10 @@ 1369.646, 1434.604, 1546.424, - 1330.617, - 976.344, - 304.622, - 269.715, + 1316.477, + 927.296, + 318.763, + 318.763, 318.763, 318.763, 318.763, @@ -34151,8 +34138,8 @@ 331.071, 331.071, 604.72, - 720.114, - 1196.809, + 769.161, + 1182.669, 1820.63, 1800.023, 1792.881, @@ -34161,10 +34148,10 @@ 1857.63, 1899.193, 2036.799, - 2167.502, + 2118.455, 1684.891, - 1670.75, - 1458.706, + 1684.891, + 1507.754, 609.084, 331.071, 331.071, @@ -34176,7 +34163,7 @@ 318.763, 589.609, 579.341, - 845.416, + 831.275, 1342.047, 1805.624, 1797.987, @@ -34185,10 +34172,10 @@ 1845.488, 1888.667, 2021.417, - 2167.069, + 2118.022, 1507.754, - 1139.34, - 1104.433, + 1153.481, + 1153.481, 799.207, 318.763, 318.763, @@ -34247,8 +34234,8 @@ 331.071, 331.071, 638.589, - 759.441, - 1245.107, + 774.484, + 1196.06, 1871.937, 1839.172, 1850.883, @@ -34257,10 +34244,10 @@ 1871.77, 1909.959, 2036.151, - 2175.695, + 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"lifecycle_elecbill_after_tax": 1.2199775049e7, + "lifecycle_storage_capital_costs": 57662.3676, + "lifecycle_om_costs_before_tax": 41554.032, + "lifecycle_emissions_cost_climate": 2.71648062e6, + "lifecycle_fuel_costs_after_tax": 0.0, + "lifecycle_capital_costs": 168839.4943, + "lifecycle_production_incentive_after_tax": 0.0, + "lifecycle_offgrid_other_capital_costs": 0.0, + "om_and_replacement_present_cost_after_tax": 24932.4192, + "replacements_future_cost_after_tax": 0.0, + "developer_om_and_replacement_present_cost_after_tax": 24932.4192, + "year_one_om_costs_after_tax": 2080.0, + "lifecycle_chp_standby_cost_after_tax": 0.0 + }, + "solver_seconds": 6.209 +} diff --git a/test/deg_results_3.json b/test/deg_results_3.json new file mode 100644 index 000000000..c18e4b15e --- /dev/null +++ b/test/deg_results_3.json @@ -0,0 +1,63 @@ +{ + "Messages": { + "errors": [ + [ + "ErrorException(\"Constraints of type MathOptInterface.VectorAffineFunction{Float64}-in-MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}} are not supported by the solver.\\n\\nIf you expected the solver to support your problem, you may have an error in your formulation. Otherwise, consider using a different solver.\\n\\nThe list of available solvers, along with the problem types they support, is available at https://jump.dev/JuMP.jl/stable/installation/#Supported-solvers.\")", + [ + "error(s::String) at error.jl:35", + "_moi_add_constraint(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{HiGHS.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, f::MathOptInterface.VectorAffineFunction{Float64}, s::MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}) at constraints.jl:679", + "add_constraint(model::Model, con::VectorConstraint{AffExpr, MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}, VectorShape}, name::String) at constraints.jl:706", + "macro expansion at macros.jl:1345 [inlined]", + "(::REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model})(d::Int64) at macro.jl:301", + "#87 at container.jl:124 [inlined]", + "iterate at generator.jl:47 [inlined]", + "collect(itr::Base.Generator{JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, JuMP.Containers.var\"#87#89\"{REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model}}}) at array.jl:834", + "map(f::Function, A::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}) at abstractarray.jl:3313", + "container(f::Function, indices::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, ::Type{JuMP.Containers.DenseAxisArray}, names::Vector{Any}) at container.jl:123", + "container at container.jl:75 [inlined]", + "macro expansion at macros.jl:1213 [inlined]", + "add_degradation(m::Model, p::REoptInputs{Scenario}, b::String) at battery_degradation.jl:111", + "build_reopt!(m::Model, p::REoptInputs{Scenario}) at reopt.jl:534", + "run_reopt(m::Model, p::REoptInputs{Scenario}; organize_pvs::Bool) at reopt.jl:548", + "run_reopt at reopt.jl:545 [inlined]", + "run_reopt(m::Model, d::Dict{String, Any}) at reopt.jl:52", + "macro expansion at runtests.jl:64 [inlined]", + "macro expansion at Test.jl:1577 [inlined]", + "macro expansion at runtests.jl:26 [inlined]", + "macro expansion at Test.jl:1577 [inlined]", + "top-level scope at runtests.jl:25", + "eval at boot.jl:385 [inlined]", + "include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) at loading.jl:2076", + "_include(mapexpr::Function, mod::Module, _path::String) at loading.jl:2136", + "include(fname::String) at client.jl:489", + "top-level scope at none:6", + "eval at boot.jl:385 [inlined]", + "exec_options(opts::Base.JLOptions) at client.jl:291", + "_start() at client.jl:552" + ] + ] + ], + "has_stacktrace": true, + "warnings": [ + [ + "constraints_battery_degradation.jl_106", + [ + "Adding binary and indicator constraints for \n ElectricStorage.degradation.maintenance_strategy = \"replacement\". \n Not all solvers support indicators and some are slow with integers." + ] + ], + [ + "energy_storage_electric_storage.jl_317", + [ + "Setting ElectricStorage replacement costs to zero. Using degradation.maintenance_cost_per_kwh instead." + ] + ], + [ + "constraints_emissions_constraints.jl_18", + [ + "No emissions reduction constraints added, as BAU emissions have not been calculated." + ] + ] + ] + }, + "status": "error" +} diff --git a/test/deg_results_4.json b/test/deg_results_4.json new file mode 100644 index 000000000..5a249d889 --- /dev/null +++ b/test/deg_results_4.json @@ -0,0 +1,63 @@ +{ + "Messages": { + "errors": [ + [ + "ErrorException(\"Constraints of type MathOptInterface.VectorAffineFunction{Float64}-in-MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}} are not supported by the solver.\\n\\nIf you expected the solver to support your problem, you may have an error in your formulation. Otherwise, consider using a different solver.\\n\\nThe list of available solvers, along with the problem types they support, is available at https://jump.dev/JuMP.jl/stable/installation/#Supported-solvers.\")", + [ + "error(s::String) at error.jl:35", + "_moi_add_constraint(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{HiGHS.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, f::MathOptInterface.VectorAffineFunction{Float64}, s::MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}) at constraints.jl:679", + "add_constraint(model::Model, con::VectorConstraint{AffExpr, MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}, VectorShape}, name::String) at constraints.jl:706", + "macro expansion at macros.jl:1345 [inlined]", + "(::REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model})(d::Int64) at macro.jl:301", + "#87 at container.jl:124 [inlined]", + "iterate at generator.jl:47 [inlined]", + "collect(itr::Base.Generator{JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, JuMP.Containers.var\"#87#89\"{REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model}}}) at array.jl:834", + "map(f::Function, A::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}) at abstractarray.jl:3313", + "container(f::Function, indices::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, ::Type{JuMP.Containers.DenseAxisArray}, names::Vector{Any}) at container.jl:123", + "container at container.jl:75 [inlined]", + "macro expansion at macros.jl:1213 [inlined]", + "add_degradation(m::Model, p::REoptInputs{Scenario}, b::String) at battery_degradation.jl:111", + "build_reopt!(m::Model, p::REoptInputs{Scenario}) at reopt.jl:534", + "run_reopt(m::Model, p::REoptInputs{Scenario}; organize_pvs::Bool) at reopt.jl:548", + "run_reopt at reopt.jl:545 [inlined]", + "run_reopt(m::Model, d::Dict{String, Any}) at reopt.jl:52", + "macro expansion at runtests.jl:83 [inlined]", + "macro expansion at Test.jl:1577 [inlined]", + "macro expansion at runtests.jl:26 [inlined]", + "macro expansion at Test.jl:1577 [inlined]", + "top-level scope at runtests.jl:25", + "eval at boot.jl:385 [inlined]", + "include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) at loading.jl:2076", + "_include(mapexpr::Function, mod::Module, _path::String) at loading.jl:2136", + "include(fname::String) at client.jl:489", + "top-level scope at none:6", + "eval at boot.jl:385 [inlined]", + "exec_options(opts::Base.JLOptions) at client.jl:291", + "_start() at client.jl:552" + ] + ] + ], + "has_stacktrace": true, + "warnings": [ + [ + "constraints_battery_degradation.jl_106", + [ + "Adding binary and indicator constraints for \n ElectricStorage.degradation.maintenance_strategy = \"replacement\". \n Not all solvers support indicators and some are slow with integers." + ] + ], + [ + "energy_storage_electric_storage.jl_317", + [ + "Setting ElectricStorage replacement costs to zero. Using degradation.maintenance_cost_per_kwh instead." + ] + ], + [ + "constraints_emissions_constraints.jl_18", + [ + "No emissions reduction constraints added, as BAU emissions have not been calculated." + ] + ] + ] + }, + "status": "error" +} diff --git a/test/import_export_results.json b/test/import_export_results.json new file mode 100644 index 000000000..09631d5ca --- /dev/null +++ b/test/import_export_results.json @@ -0,0 +1,78977 @@ +{ + "Messages": { + "errors": [], + "warnings": [ + [ + "results_electric_utility.jl_38", + [ + "Wholesale benefit is at the maximum allowable by the model; the problem is likely unbounded without this \nlimit in place. Check the inputs to ensure that there are practical limits for max system sizes and that \nthe wholesale and retail electricity rates are accurate." + ] + ], + [ + "constraints_emissions_constraints.jl_18", + [ + "No emissions reduction constraints added, as BAU emissions have not been calculated." + ] + ], + [ + "core_reopt.jl_643", + [ + "Adding binary variable to prevent simultaneous grid import/export. Some solvers are very slow with integer variables." + ] + ] + ] + }, + "ElectricUtility": { + "lifecycle_emissions_tonnes_PM25": 0.14, + "lifecycle_emissions_tonnes_SO2": 1.68, + "electric_to_load_series_kw": [ + 22.089, + 21.149, + 20.801, + 20.541, + 22.248, + 26.367, + 31.038, + 31.404, + 25.419, + 20.685, + 15.358, + 10.151, + 20.413, + 14.381, + 16.348, + 16.492, + 26.872, + 37.077, + 42.082, + 41.413, + 41.403, + 36.724, + 30.764, + 25.74, + 22.687, + 22.355, + 22.421, + 22.644, + 24.563, + 28.675, + 33.59, + 34.178, + 29.995, + 26.272, + 18.382, + 17.995, + 17.441, + 13.022, + 19.256, + 19.078, + 26.923, + 34.876, + 39.963, + 39.496, + 39.411, + 34.616, + 28.55, + 23.36, + 20.114, + 19.77, + 19.645, + 19.571, + 21.488, + 25.463, + 29.934, + 30.597, + 25.932, + 21.4, + 16.133, + 15.682, + 16.29, + 15.351, + 10.338, + 17.452, + 26.398, + 33.826, + 38.809, + 38.105, + 37.93, + 33.328, + 27.132, + 21.927, + 18.77, + 18.286, + 18.013, + 17.765, + 19.629, + 23.906, + 28.827, + 29.181, 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"lifecycle_production_incentive_after_tax": 0.0, + "lifecycle_offgrid_other_capital_costs": 0.0, + "om_and_replacement_present_cost_after_tax": 9669.4845, + "replacements_future_cost_after_tax": 0.0, + "developer_om_and_replacement_present_cost_after_tax": 9669.4845, + "year_one_om_costs_after_tax": 806.6818, + "lifecycle_chp_standby_cost_after_tax": 0.0 + }, + "solver_seconds": 4.276 +} diff --git a/test/runtests.jl b/test/runtests.jl index cdf16a707..19d32d576 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -21,14 +21,210 @@ elseif "CPLEX" in ARGS include("test_with_cplex.jl") end elseif "Debug" in ARGS + @testset "debugging" begin + @testset "Solar and ElectricStorage w/BAU and degradation" begin + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + d = JSON.parsefile("scenarios/pv_storage.json"); + d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) + results = run_reopt([m1,m2], d) + + open("deg_results_1.json","w") do f + JSON.print(f, results, 4) + end + + @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 + @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 + @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 + @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 + @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - + results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) + @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 + + # compare avg soc with and without degradation, + # using default augmentation battery maintenance strategy + avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 + d["ElectricStorage"]["model_degradation"] = true + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r_degr = run_reopt(m, d) + + open("deg_results_2.json","w") do f + JSON.print(f, r_degr, 4) + end + + avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 + @test avg_soc_no_degr > avg_soc_degr + + # test the replacement strategy + d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + set_optimizer_attribute(m, "mip_rel_gap", 0.01) + r = run_reopt(m, d) + + open("deg_results_3.json","w") do f + JSON.print(f, r, 4) + end + + @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # #optimal SOH at end of horizon is 80\% to prevent any replacement + # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 + # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 + # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match + # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 + # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 + # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 + + # test minimum_avg_soc_fraction + d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + set_optimizer_attribute(m, "mip_rel_gap", 0.01) + r = run_reopt(m, d) + + open("deg_results_4.json","w") do f + JSON.print(f, r, 4) + end + + @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 + end + @testset "Minimize Unserved Load" begin + d = JSON.parsefile("./scenarios/outage.json") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt(m, d) + + open("unserved_results_1.json","w") do f + JSON.print(f, results, 4) + end + + @test results["Outages"]["expected_outage_cost"] ≈ 0 atol=0.1 + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 atol=0.1 + @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 + @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + @test value(m[:binMGStorageUsed]) ≈ 1 + + # Increase cost of microgrid upgrade and PV Size, PV not used and some load not met + d["Financial"]["microgrid_upgrade_cost_fraction"] = 0.3 + d["PV"]["min_kw"] = 200.0 + d["PV"]["max_kw"] = 200.0 + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt(m, d) + open("unserved_results_2.json","w") do f + JSON.print(f, results, 4) + end + @test value(m[:binMGTechUsed]["PV"]) ≈ 0 + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 24.16 atol=0.1 + + #= + Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 + - should meet 168 kWh in each outage such that the total unserved load is 12 kWh + =# + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") + open("unserved_results_3.json","w") do f + JSON.print(f, results, 4) + end + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 + + # testing dvUnserved load, which would output 100 kWh for this scenario before output fix + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") + open("unserved_results_4.json","w") do f + JSON.print(f, results, 4) + end + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 + @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) + @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 + + # Scenario with generator, PV, electric storage + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") + open("unserved_results_5.json","w") do f + JSON.print(f, results, 4) + end + @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 + @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 + + # Scenario with generator, PV, wind, electric storage + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") + open("unserved_results_6.json","w") do f + JSON.print(f, results, 4) + end + @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 + @test results["Outages"]["expected_outage_cost"] ≈ 1.296319791276051e6 atol=1.0 + @test results["Financial"]["lcc"] ≈ 4.8046446434e6 rtol=0.001 + + end + @testset "Outages with Wind and supply-to-load no greater than critical load" begin + input_data = JSON.parsefile("./scenarios/wind_outages.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt([m1,m2], inputs) + open("out_wind_results.json","w") do f + JSON.print(f, results, 4) + end + + # Check that supply-to-load is equal to critical load during outages, including wind + supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] + supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] + critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] + check = .≈(supply_to_load, critical_load, atol=0.001) + @test !(0 in check) + + # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw + @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) + end + @testset "Wind" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d = JSON.parsefile("./scenarios/wind.json") + results = run_reopt(m, d) + open("wind_results_1.json","w") do f + JSON.print(f, results, 4) + end + @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 + @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 + #= + 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 + - both have zero curtailment + - same energy to grid: 5,839,317 vs 5,839,322 + - same energy to load: 4,160,683 vs 4,160,677 + - same city: Boulder + - same total wind prod factor + + REopt.jl has: + - bigger turbine: 3752 vs 3735 + - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 + + TODO: will these discrepancies be addressed once NMIL binaries are added? + =# + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) + results = run_reopt(m, d) + open("wind_results_2.json","w") do f + JSON.print(f, results, 4) + end + @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error + results = run_reopt(m, d) + @test "errors" ∈ keys(results["Messages"]) + @test length(results["Messages"]["errors"]) > 0 + end + end +else # run HiGHS tests @testset "Multiple Electric Storage" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) r = run_reopt(model, "./scenarios/multiple_elec_storage.json") - open("debug_reopt_results.json","w") do f - JSON.print(f, r, 4) - end - @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 for stor in r["ElectricStorage"] @@ -41,8 +237,6 @@ elseif "Debug" in ARGS end end end -else # run HiGHS tests - @testset "Inputs" begin @testset "hybrid profile" begin electric_load = REopt.ElectricLoad(; diff --git a/test/unserved_results_1.json b/test/unserved_results_1.json new file mode 100644 index 000000000..c561c0b5d --- /dev/null +++ b/test/unserved_results_1.json @@ -0,0 +1,307960 @@ +{ + "Site": { + "annual_emissions_from_fuelburn_tonnes_PM25": 0.0, + "lifecycle_emissions_tonnes_PM25": 0.45, + "lifecycle_emissions_tonnes_SO2": 8.49, + "lifecycle_emissions_from_fuelburn_tonnes_PM25": 0.0, + "annual_emissions_from_fuelburn_tonnes_NOx": 0.0, + "annual_emissions_tonnes_NOx": 0.512, + "annual_emissions_tonnes_SO2": 0.3396, + "annual_emissions_tonnes_PM25": 0.018000000000000002, + "lifecycle_emissions_tonnes_NOx": 12.8, + "annual_emissions_from_fuelburn_tonnes_CO2": 0.0, + "total_renewable_energy_fraction": 0.08223, + "annual_emissions_from_fuelburn_tonnes_SO2": 0.0, + "lifecycle_emissions_from_fuelburn_tonnes_SO2": 0.0, + "renewable_electricity_fraction": 0.08223, + "lifecycle_emissions_from_fuelburn_tonnes_CO2": 0.0, + "lifecycle_emissions_from_fuelburn_tonnes_NOx": 0.0, + "annual_renewable_electricity_kwh": 144412.87, + "lifecycle_emissions_tonnes_CO2": 26141.61, + "annual_emissions_tonnes_CO2": 1045.6644000000001 + 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"lifecycle_MG_upgrade_and_fuel_cost": 9.1848263748e6, + "lifecycle_elecbill_after_tax": 4.18991821852e7, + "lifecycle_storage_capital_costs": 2.28864728427e7, + "lifecycle_om_costs_before_tax": 1.5859040854e6, + "lifecycle_emissions_cost_climate": 3.50878129e7, + "lifecycle_fuel_costs_after_tax": 0.0, + "lifecycle_capital_costs": 3.06112479158e7, + "lifecycle_production_incentive_after_tax": 0.0, + "lifecycle_offgrid_other_capital_costs": 0.0, + "om_and_replacement_present_cost_after_tax": 2.9071818573e6, + "replacements_future_cost_after_tax": 6.0764124556e6, + "developer_om_and_replacement_present_cost_after_tax": 1.1214265336e6, + "year_one_om_costs_after_tax": 39962.1327, + "lifecycle_chp_standby_cost_after_tax": 0.0 + } +} diff --git a/test/unserved_results_6.json b/test/unserved_results_6.json new file mode 100644 index 000000000..585e8c7e7 --- /dev/null +++ b/test/unserved_results_6.json @@ -0,0 +1,28 @@ +{ + "Messages": { + "errors": [ + [ + "core_production_factor.jl_87", + [ + "Error occurred when calling Wind Toolkit: HTTP.Exceptions.ConnectError(\"https://gds-hsds-wtk-files.nrelcloud.org/data/wind/854279_39.75_-105.17_2012_052ad8cb28a9d97e6421f4ad70b33db0.csv\", CapturedException(OpenSSL.OpenSSLError(\"self-signed certificate in certificate chain\"), Any[(macro expansion at ssl.jl:512 [inlined], 1), (macro expansion at lock.jl:267 [inlined], 1), (connect(ssl::OpenSSL.SSLStream; require_ssl_verification::Bool) at ssl.jl:505, 1), (connect at ssl.jl:489 [inlined], 1), (#sslconnection#19 at Connections.jl:594 [inlined], 1), (sslconnection at Connections.jl:584 [inlined], 1), (getconnection(::Type{OpenSSL.SSLStream}, host::SubString{String}, port::SubString{String}; kw::Base.Pairs{Symbol, Union{Nothing, Integer}, NTuple{6, Symbol}, @NamedTuple{require_ssl_verification::Bool, keepalive::Bool, readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}) at Connections.jl:581, 1), (getconnection at Connections.jl:573 [inlined], 1), (#10 at Connections.jl:462 [inlined], 1), ((::ConcurrentUtilities.var\"#2#4\"{Any, Timer, ConcurrentUtilities.TimedOut{Any}, Channel{Any}, HTTP.Connections.var\"#10#13\"{OpenSSL.SSLStream, Bool, Bool, Base.Pairs{Symbol, Union{Nothing, Int64}, NTuple{4, Symbol}, @NamedTuple{readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}, SubString{String}, SubString{String}}})() at try_with_timeout.jl:82, 1)]))" + ] + ] + ], + "has_stacktrace": false, + "warnings": [ + [ + "core_electric_utility.jl_208", + [ + "You set co2_from_avert = true and provided values for emissions_factor_series_lb_CO2_per_kwh. REopt will use the provided values for emissions_factor_series_lb_CO2_per_kwh." + ] + ], + [ + "core_electric_utility.jl_321", + [ + "When using stochastic outage modeling (i.e. outage_start_time_steps, outage_durations, outage_probabilities), \n emissions and renewable energy percentage calculations and constraints do not consider outages." + ] + ] + ] + }, + "status": "error" +} diff --git a/test/wind_results_1.json b/test/wind_results_1.json new file mode 100644 index 000000000..dce5f0811 --- /dev/null +++ b/test/wind_results_1.json @@ -0,0 +1,15 @@ +{ + "Messages": { + "errors": [ + [ + "core_production_factor.jl_87", + [ + "Error occurred when calling Wind Toolkit: HTTP.Exceptions.ConnectError(\"https://gds-hsds-wtk-files.nrelcloud.org/data/wind/852132_39.91_-105.24_2012_e6bd2c0f0a6064af19205776fc3d1da8.csv\", CapturedException(OpenSSL.OpenSSLError(\"self-signed certificate in certificate chain\"), Any[(macro expansion at ssl.jl:512 [inlined], 1), (macro expansion at lock.jl:267 [inlined], 1), (connect(ssl::OpenSSL.SSLStream; require_ssl_verification::Bool) at ssl.jl:505, 1), (connect at ssl.jl:489 [inlined], 1), (#sslconnection#19 at Connections.jl:594 [inlined], 1), (sslconnection at Connections.jl:584 [inlined], 1), (getconnection(::Type{OpenSSL.SSLStream}, host::SubString{String}, port::SubString{String}; kw::Base.Pairs{Symbol, Union{Nothing, Integer}, NTuple{6, Symbol}, @NamedTuple{require_ssl_verification::Bool, keepalive::Bool, readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}) at Connections.jl:581, 1), (getconnection at Connections.jl:573 [inlined], 1), (#10 at Connections.jl:462 [inlined], 1), ((::ConcurrentUtilities.var\"#2#4\"{Any, Timer, ConcurrentUtilities.TimedOut{Any}, Channel{Any}, HTTP.Connections.var\"#10#13\"{OpenSSL.SSLStream, Bool, Bool, Base.Pairs{Symbol, Union{Nothing, Int64}, NTuple{4, Symbol}, @NamedTuple{readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}, SubString{String}, SubString{String}}})() at try_with_timeout.jl:82, 1)]))" + ] + ] + ], + "has_stacktrace": false, + "warnings": [] + }, + "status": "error" +} diff --git a/test/wind_results_2.json b/test/wind_results_2.json new file mode 100644 index 000000000..6a8d9b5e7 --- /dev/null +++ b/test/wind_results_2.json @@ -0,0 +1,22 @@ +{ + "Messages": { + "errors": [ + [ + "core_production_factor.jl_87", + [ + "Error occurred when calling Wind Toolkit: HTTP.Exceptions.ConnectError(\"https://gds-hsds-wtk-files.nrelcloud.org/data/wind/852132_39.91_-105.24_2012_e6bd2c0f0a6064af19205776fc3d1da8.csv\", CapturedException(OpenSSL.OpenSSLError(\"self-signed certificate in certificate chain\"), Any[(macro expansion at ssl.jl:512 [inlined], 1), (macro expansion at lock.jl:267 [inlined], 1), (connect(ssl::OpenSSL.SSLStream; require_ssl_verification::Bool) at ssl.jl:505, 1), (connect at ssl.jl:489 [inlined], 1), (#sslconnection#19 at Connections.jl:594 [inlined], 1), (sslconnection at Connections.jl:584 [inlined], 1), (getconnection(::Type{OpenSSL.SSLStream}, host::SubString{String}, port::SubString{String}; kw::Base.Pairs{Symbol, Union{Nothing, Integer}, NTuple{6, Symbol}, @NamedTuple{require_ssl_verification::Bool, keepalive::Bool, readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}) at Connections.jl:581, 1), (getconnection at Connections.jl:573 [inlined], 1), (#10 at Connections.jl:462 [inlined], 1), ((::ConcurrentUtilities.var\"#2#4\"{Any, Timer, ConcurrentUtilities.TimedOut{Any}, Channel{Any}, HTTP.Connections.var\"#10#13\"{OpenSSL.SSLStream, Bool, Bool, Base.Pairs{Symbol, Union{Nothing, Int64}, NTuple{4, Symbol}, @NamedTuple{readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}, SubString{String}, SubString{String}}})() at try_with_timeout.jl:82, 1)]))" + ] + ] + ], + "has_stacktrace": false, + "warnings": [ + [ + "core_reopt_inputs.jl_619", + [ + "User-provided maximum wind kW is greater than the calculated land-constrained kW (site.land_acres/wind.acres_per_kw). Wind max kW has been updated to land-constrained max of 2000.0 kW." + ] + ] + ] + }, + "status": "error" +} diff --git a/test/wind_results_3.json b/test/wind_results_3.json new file mode 100644 index 000000000..2f47d6866 --- /dev/null +++ b/test/wind_results_3.json @@ -0,0 +1,22 @@ +{ + "Messages": { + "errors": [ + [ + "core_reopt_inputs.jl_623", + [ + "User-provided minimum wind kW is greater than either wind.max_kw or calculated land-constrained kW (site.land_acres/wind.acres_per_kw). Update wind.min_kw or site.land_acres" + ] + ] + ], + "has_stacktrace": false, + "warnings": [ + [ + "core_reopt_inputs.jl_619", + [ + "User-provided maximum wind kW is greater than the calculated land-constrained kW (site.land_acres/wind.acres_per_kw). Wind max kW has been updated to land-constrained max of 2000.0 kW." + ] + ] + ] + }, + "status": "error" +} From 9d5e3cd7a0d231f010ab79c31270325a530a58e8 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 4 Sep 2024 16:58:17 -0600 Subject: [PATCH 118/151] cast as Real[] so sum of empty [] doesn't error --- src/constraints/electric_utility_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/electric_utility_constraints.jl b/src/constraints/electric_utility_constraints.jl index d9de8e841..bb970908f 100644 --- a/src/constraints/electric_utility_constraints.jl +++ b/src/constraints/electric_utility_constraints.jl @@ -279,7 +279,7 @@ function add_simultaneous_export_import_constraint(m, p; _n="") } ) else - bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.process_heat_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+sum(p.s.storage.attr[b].max_kw for b in p.s.storage.types.elec) + bigM_hourly_load_plus_battery_power = maximum(p.s.electric_load.loads_kw)+maximum(p.s.space_heating_load.loads_kw)+maximum(p.s.process_heat_load.loads_kw)+maximum(p.s.dhw_load.loads_kw)+maximum(p.s.cooling_load.loads_kw_thermal)+sum(Real[p.s.storage.attr[b].max_kw for b in p.s.storage.types.elec]) @constraint(m, NoGridPurchasesBinary[ts in p.time_steps], sum(m[Symbol("dvGridPurchase"*_n)][ts, tier] for tier in 1:p.s.electric_tariff.n_energy_tiers) + sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) <= bigM_hourly_load_plus_battery_power*(1-m[Symbol("binNoGridPurchases"*_n)][ts]) From d3d90e0d7a5e1006c6aa3212b3aecf1ec5679956 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 4 Sep 2024 16:59:55 -0600 Subject: [PATCH 119/151] rm debugging --- test/deg_results_1.json | 114169 ----------- test/deg_results_2.json | 112569 ----------- test/deg_results_3.json | 63 - test/deg_results_4.json | 63 - test/import_export_results.json | 78977 -------- test/runtests.jl | 200 - test/unserved_results_1.json | 307960 ----------------------------- test/unserved_results_2.json | 307960 ----------------------------- test/unserved_results_3.json | 87247 -------- test/unserved_results_4.json | 35239 ---- test/unserved_results_5.json | 133630 ------------- test/unserved_results_6.json | 28 - test/wind_results_1.json | 15 - test/wind_results_2.json | 22 - test/wind_results_3.json | 22 - 15 files changed, 1178164 deletions(-) delete mode 100644 test/deg_results_1.json delete mode 100644 test/deg_results_2.json delete mode 100644 test/deg_results_3.json delete mode 100644 test/deg_results_4.json delete mode 100644 test/import_export_results.json delete mode 100644 test/unserved_results_1.json delete mode 100644 test/unserved_results_2.json delete mode 100644 test/unserved_results_3.json delete mode 100644 test/unserved_results_4.json delete mode 100644 test/unserved_results_5.json delete mode 100644 test/unserved_results_6.json delete mode 100644 test/wind_results_1.json delete mode 100644 test/wind_results_2.json delete mode 100644 test/wind_results_3.json diff --git a/test/deg_results_1.json b/test/deg_results_1.json deleted file mode 100644 index decd18738..000000000 --- a/test/deg_results_1.json +++ /dev/null @@ -1,114169 +0,0 @@ -{ - "ElectricStorage": { - "size_kw": 49.05, - "size_kwh": 83.32, - "initial_capital_cost": 90710.0, - "year_one_om_cost_before_tax": 0.0, - "lifecycle_om_cost_before_tax": 0.0, - "storage_to_grid_series_kw": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 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"replacements_present_cost_after_tax": 0.0, - "lifecycle_emissions_cost_health": 428589.22, - "lifecycle_outage_cost": 0.0, - "initial_capital_costs": 512459.8662, - "initial_capital_costs_after_incentives": 168839.4943, - "lifecycle_MG_upgrade_and_fuel_cost": 0.0, - "lifecycle_elecbill_after_tax": 1.2199775049e7, - "lifecycle_storage_capital_costs": 57662.3676, - "lifecycle_om_costs_before_tax": 41554.032, - "lifecycle_emissions_cost_climate": 2.71648062e6, - "lifecycle_fuel_costs_after_tax": 0.0, - "lifecycle_capital_costs": 168839.4943, - "lifecycle_production_incentive_after_tax": 0.0, - "lifecycle_offgrid_other_capital_costs": 0.0, - "om_and_replacement_present_cost_after_tax": 24932.4192, - "replacements_future_cost_after_tax": 0.0, - "developer_om_and_replacement_present_cost_after_tax": 24932.4192, - "year_one_om_costs_after_tax": 2080.0, - "lifecycle_chp_standby_cost_after_tax": 0.0 - }, - "solver_seconds": 6.209 -} diff --git a/test/deg_results_3.json b/test/deg_results_3.json deleted file mode 100644 index c18e4b15e..000000000 --- a/test/deg_results_3.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "Messages": { - "errors": [ - [ - "ErrorException(\"Constraints of type MathOptInterface.VectorAffineFunction{Float64}-in-MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}} are not supported by the solver.\\n\\nIf you expected the solver to support your problem, you may have an error in your formulation. Otherwise, consider using a different solver.\\n\\nThe list of available solvers, along with the problem types they support, is available at https://jump.dev/JuMP.jl/stable/installation/#Supported-solvers.\")", - [ - "error(s::String) at error.jl:35", - "_moi_add_constraint(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{HiGHS.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, f::MathOptInterface.VectorAffineFunction{Float64}, s::MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}) at constraints.jl:679", - "add_constraint(model::Model, con::VectorConstraint{AffExpr, MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}, VectorShape}, name::String) at constraints.jl:706", - "macro expansion at macros.jl:1345 [inlined]", - "(::REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model})(d::Int64) at macro.jl:301", - "#87 at container.jl:124 [inlined]", - "iterate at generator.jl:47 [inlined]", - "collect(itr::Base.Generator{JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, JuMP.Containers.var\"#87#89\"{REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model}}}) at array.jl:834", - "map(f::Function, A::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}) at abstractarray.jl:3313", - "container(f::Function, indices::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, ::Type{JuMP.Containers.DenseAxisArray}, names::Vector{Any}) at container.jl:123", - "container at container.jl:75 [inlined]", - "macro expansion at macros.jl:1213 [inlined]", - "add_degradation(m::Model, p::REoptInputs{Scenario}, b::String) at battery_degradation.jl:111", - "build_reopt!(m::Model, p::REoptInputs{Scenario}) at reopt.jl:534", - "run_reopt(m::Model, p::REoptInputs{Scenario}; organize_pvs::Bool) at reopt.jl:548", - "run_reopt at reopt.jl:545 [inlined]", - "run_reopt(m::Model, d::Dict{String, Any}) at reopt.jl:52", - "macro expansion at runtests.jl:64 [inlined]", - "macro expansion at Test.jl:1577 [inlined]", - "macro expansion at runtests.jl:26 [inlined]", - "macro expansion at Test.jl:1577 [inlined]", - "top-level scope at runtests.jl:25", - "eval at boot.jl:385 [inlined]", - "include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) at loading.jl:2076", - "_include(mapexpr::Function, mod::Module, _path::String) at loading.jl:2136", - "include(fname::String) at client.jl:489", - "top-level scope at none:6", - "eval at boot.jl:385 [inlined]", - "exec_options(opts::Base.JLOptions) at client.jl:291", - "_start() at client.jl:552" - ] - ] - ], - "has_stacktrace": true, - "warnings": [ - [ - "constraints_battery_degradation.jl_106", - [ - "Adding binary and indicator constraints for \n ElectricStorage.degradation.maintenance_strategy = \"replacement\". \n Not all solvers support indicators and some are slow with integers." - ] - ], - [ - "energy_storage_electric_storage.jl_317", - [ - "Setting ElectricStorage replacement costs to zero. Using degradation.maintenance_cost_per_kwh instead." - ] - ], - [ - "constraints_emissions_constraints.jl_18", - [ - "No emissions reduction constraints added, as BAU emissions have not been calculated." - ] - ] - ] - }, - "status": "error" -} diff --git a/test/deg_results_4.json b/test/deg_results_4.json deleted file mode 100644 index 5a249d889..000000000 --- a/test/deg_results_4.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "Messages": { - "errors": [ - [ - "ErrorException(\"Constraints of type MathOptInterface.VectorAffineFunction{Float64}-in-MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}} are not supported by the solver.\\n\\nIf you expected the solver to support your problem, you may have an error in your formulation. Otherwise, consider using a different solver.\\n\\nThe list of available solvers, along with the problem types they support, is available at https://jump.dev/JuMP.jl/stable/installation/#Supported-solvers.\")", - [ - "error(s::String) at error.jl:35", - "_moi_add_constraint(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{HiGHS.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}}, f::MathOptInterface.VectorAffineFunction{Float64}, s::MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}) at constraints.jl:679", - "add_constraint(model::Model, con::VectorConstraint{AffExpr, MathOptInterface.Indicator{MathOptInterface.ACTIVATE_ON_ONE, MathOptInterface.GreaterThan{Float64}}, VectorShape}, name::String) at constraints.jl:706", - "macro expansion at macros.jl:1345 [inlined]", - "(::REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model})(d::Int64) at macro.jl:301", - "#87 at container.jl:124 [inlined]", - "iterate at generator.jl:47 [inlined]", - "collect(itr::Base.Generator{JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, JuMP.Containers.var\"#87#89\"{REopt.var\"#691#702\"{String, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, JuMP.Containers.DenseAxisArray{VariableRef, 1, Tuple{UnitRange{Int64}}, Tuple{JuMP.Containers._AxisLookup{Tuple{Int64, Int64}}}}, Model}}}) at array.jl:834", - "map(f::Function, A::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}) at abstractarray.jl:3313", - "container(f::Function, indices::JuMP.Containers.VectorizedProductIterator{Tuple{UnitRange{Int64}}}, ::Type{JuMP.Containers.DenseAxisArray}, names::Vector{Any}) at container.jl:123", - "container at container.jl:75 [inlined]", - "macro expansion at macros.jl:1213 [inlined]", - "add_degradation(m::Model, p::REoptInputs{Scenario}, b::String) at battery_degradation.jl:111", - "build_reopt!(m::Model, p::REoptInputs{Scenario}) at reopt.jl:534", - "run_reopt(m::Model, p::REoptInputs{Scenario}; organize_pvs::Bool) at reopt.jl:548", - "run_reopt at reopt.jl:545 [inlined]", - "run_reopt(m::Model, d::Dict{String, Any}) at reopt.jl:52", - "macro expansion at runtests.jl:83 [inlined]", - "macro expansion at Test.jl:1577 [inlined]", - "macro expansion at runtests.jl:26 [inlined]", - "macro expansion at Test.jl:1577 [inlined]", - "top-level scope at runtests.jl:25", - "eval at boot.jl:385 [inlined]", - "include_string(mapexpr::typeof(identity), mod::Module, code::String, filename::String) at loading.jl:2076", - "_include(mapexpr::Function, mod::Module, _path::String) at loading.jl:2136", - "include(fname::String) at client.jl:489", - "top-level scope at none:6", - "eval at boot.jl:385 [inlined]", - "exec_options(opts::Base.JLOptions) at client.jl:291", - "_start() at client.jl:552" - ] - ] - ], - "has_stacktrace": true, - "warnings": [ - [ - "constraints_battery_degradation.jl_106", - [ - "Adding binary and indicator constraints for \n ElectricStorage.degradation.maintenance_strategy = \"replacement\". \n Not all solvers support indicators and some are slow with integers." - ] - ], - [ - "energy_storage_electric_storage.jl_317", - [ - "Setting ElectricStorage replacement costs to zero. Using degradation.maintenance_cost_per_kwh instead." - ] - ], - [ - "constraints_emissions_constraints.jl_18", - [ - "No emissions reduction constraints added, as BAU emissions have not been calculated." - ] - ] - ] - }, - "status": "error" -} diff --git a/test/import_export_results.json b/test/import_export_results.json deleted file mode 100644 index 09631d5ca..000000000 --- a/test/import_export_results.json +++ /dev/null @@ -1,78977 +0,0 @@ -{ - "Messages": { - "errors": [], - "warnings": [ - [ - "results_electric_utility.jl_38", - [ - "Wholesale benefit is at the maximum allowable by the model; the problem is likely unbounded without this \nlimit in place. Check the inputs to ensure that there are practical limits for max system sizes and that \nthe wholesale and retail electricity rates are accurate." - ] - ], - [ - "constraints_emissions_constraints.jl_18", - [ - "No emissions reduction constraints added, as BAU emissions have not been calculated." - ] - ], - [ - "core_reopt.jl_643", - [ - "Adding binary variable to prevent simultaneous grid import/export. Some solvers are very slow with integer variables." - ] - ] - ] - }, - "ElectricUtility": { - "lifecycle_emissions_tonnes_PM25": 0.14, - "lifecycle_emissions_tonnes_SO2": 1.68, - "electric_to_load_series_kw": [ - 22.089, - 21.149, - 20.801, - 20.541, - 22.248, - 26.367, - 31.038, - 31.404, - 25.419, - 20.685, - 15.358, - 10.151, - 20.413, - 14.381, - 16.348, - 16.492, - 26.872, - 37.077, - 42.082, - 41.413, - 41.403, - 36.724, - 30.764, - 25.74, - 22.687, - 22.355, - 22.421, - 22.644, - 24.563, - 28.675, - 33.59, - 34.178, - 29.995, - 26.272, - 18.382, - 17.995, - 17.441, - 13.022, - 19.256, - 19.078, - 26.923, - 34.876, - 39.963, - 39.496, - 39.411, - 34.616, - 28.55, - 23.36, - 20.114, - 19.77, - 19.645, - 19.571, - 21.488, - 25.463, - 29.934, - 30.597, - 25.932, - 21.4, - 16.133, - 15.682, - 16.29, - 15.351, - 10.338, - 17.452, - 26.398, - 33.826, - 38.809, - 38.105, - 37.93, - 33.328, - 27.132, - 21.927, - 18.77, - 18.286, - 18.013, - 17.765, - 19.629, - 23.906, - 28.827, - 29.181, - 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"lifecycle_capital_costs": 74074.7991, - "lifecycle_production_incentive_after_tax": 0.0, - "lifecycle_offgrid_other_capital_costs": 0.0, - "om_and_replacement_present_cost_after_tax": 9669.4845, - "replacements_future_cost_after_tax": 0.0, - "developer_om_and_replacement_present_cost_after_tax": 9669.4845, - "year_one_om_costs_after_tax": 806.6818, - "lifecycle_chp_standby_cost_after_tax": 0.0 - }, - "solver_seconds": 4.276 -} diff --git a/test/runtests.jl b/test/runtests.jl index 19d32d576..ea1ffba58 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -20,206 +20,6 @@ elseif "CPLEX" in ARGS @testset "test_with_cplex" begin include("test_with_cplex.jl") end -elseif "Debug" in ARGS - @testset "debugging" begin - @testset "Solar and ElectricStorage w/BAU and degradation" begin - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - d = JSON.parsefile("scenarios/pv_storage.json"); - d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) - results = run_reopt([m1,m2], d) - - open("deg_results_1.json","w") do f - JSON.print(f, results, 4) - end - - @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 - @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 - @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 - @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 - @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - - results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) - @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 - - # compare avg soc with and without degradation, - # using default augmentation battery maintenance strategy - avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 - d["ElectricStorage"]["model_degradation"] = true - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r_degr = run_reopt(m, d) - - open("deg_results_2.json","w") do f - JSON.print(f, r_degr, 4) - end - - avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 - @test avg_soc_no_degr > avg_soc_degr - - # test the replacement strategy - d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - set_optimizer_attribute(m, "mip_rel_gap", 0.01) - r = run_reopt(m, d) - - open("deg_results_3.json","w") do f - JSON.print(f, r, 4) - end - - @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # #optimal SOH at end of horizon is 80\% to prevent any replacement - # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 - # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 - # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match - # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 - # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 - # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 - - # test minimum_avg_soc_fraction - d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - set_optimizer_attribute(m, "mip_rel_gap", 0.01) - r = run_reopt(m, d) - - open("deg_results_4.json","w") do f - JSON.print(f, r, 4) - end - - @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 - end - @testset "Minimize Unserved Load" begin - d = JSON.parsefile("./scenarios/outage.json") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt(m, d) - - open("unserved_results_1.json","w") do f - JSON.print(f, results, 4) - end - - @test results["Outages"]["expected_outage_cost"] ≈ 0 atol=0.1 - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 atol=0.1 - @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 - @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - @test value(m[:binMGStorageUsed]) ≈ 1 - - # Increase cost of microgrid upgrade and PV Size, PV not used and some load not met - d["Financial"]["microgrid_upgrade_cost_fraction"] = 0.3 - d["PV"]["min_kw"] = 200.0 - d["PV"]["max_kw"] = 200.0 - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt(m, d) - open("unserved_results_2.json","w") do f - JSON.print(f, results, 4) - end - @test value(m[:binMGTechUsed]["PV"]) ≈ 0 - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 24.16 atol=0.1 - - #= - Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 - - should meet 168 kWh in each outage such that the total unserved load is 12 kWh - =# - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") - open("unserved_results_3.json","w") do f - JSON.print(f, results, 4) - end - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 - - # testing dvUnserved load, which would output 100 kWh for this scenario before output fix - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") - open("unserved_results_4.json","w") do f - JSON.print(f, results, 4) - end - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 - @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) - @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 - - # Scenario with generator, PV, electric storage - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") - open("unserved_results_5.json","w") do f - JSON.print(f, results, 4) - end - @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 - @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 - - # Scenario with generator, PV, wind, electric storage - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") - open("unserved_results_6.json","w") do f - JSON.print(f, results, 4) - end - @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 - @test results["Outages"]["expected_outage_cost"] ≈ 1.296319791276051e6 atol=1.0 - @test results["Financial"]["lcc"] ≈ 4.8046446434e6 rtol=0.001 - - end - @testset "Outages with Wind and supply-to-load no greater than critical load" begin - input_data = JSON.parsefile("./scenarios/wind_outages.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt([m1,m2], inputs) - open("out_wind_results.json","w") do f - JSON.print(f, results, 4) - end - - # Check that supply-to-load is equal to critical load during outages, including wind - supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] - supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] - critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] - check = .≈(supply_to_load, critical_load, atol=0.001) - @test !(0 in check) - - # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw - @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) - end - @testset "Wind" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d = JSON.parsefile("./scenarios/wind.json") - results = run_reopt(m, d) - open("wind_results_1.json","w") do f - JSON.print(f, results, 4) - end - @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 - @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 - #= - 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 - - both have zero curtailment - - same energy to grid: 5,839,317 vs 5,839,322 - - same energy to load: 4,160,683 vs 4,160,677 - - same city: Boulder - - same total wind prod factor - - REopt.jl has: - - bigger turbine: 3752 vs 3735 - - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 - - TODO: will these discrepancies be addressed once NMIL binaries are added? - =# - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) - results = run_reopt(m, d) - open("wind_results_2.json","w") do f - JSON.print(f, results, 4) - end - @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error - results = run_reopt(m, d) - @test "errors" ∈ keys(results["Messages"]) - @test length(results["Messages"]["errors"]) > 0 - end - end else # run HiGHS tests @testset "Multiple Electric Storage" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) diff --git a/test/unserved_results_1.json b/test/unserved_results_1.json deleted file mode 100644 index c561c0b5d..000000000 --- a/test/unserved_results_1.json +++ /dev/null @@ -1,307960 +0,0 @@ -{ - "Site": { - "annual_emissions_from_fuelburn_tonnes_PM25": 0.0, - "lifecycle_emissions_tonnes_PM25": 0.45, - "lifecycle_emissions_tonnes_SO2": 8.49, - "lifecycle_emissions_from_fuelburn_tonnes_PM25": 0.0, - "annual_emissions_from_fuelburn_tonnes_NOx": 0.0, - "annual_emissions_tonnes_NOx": 0.512, - "annual_emissions_tonnes_SO2": 0.3396, - "annual_emissions_tonnes_PM25": 0.018000000000000002, - "lifecycle_emissions_tonnes_NOx": 12.8, - "annual_emissions_from_fuelburn_tonnes_CO2": 0.0, - "total_renewable_energy_fraction": 0.08223, - "annual_emissions_from_fuelburn_tonnes_SO2": 0.0, - "lifecycle_emissions_from_fuelburn_tonnes_SO2": 0.0, - "renewable_electricity_fraction": 0.08223, - "lifecycle_emissions_from_fuelburn_tonnes_CO2": 0.0, - "lifecycle_emissions_from_fuelburn_tonnes_NOx": 0.0, - "annual_renewable_electricity_kwh": 144412.87, - "lifecycle_emissions_tonnes_CO2": 26141.61, - "annual_emissions_tonnes_CO2": 1045.6644000000001 - }, - "Generator": { - "electric_to_grid_series_kw": [ - 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"lifecycle_emissions_cost_health": 664.31, - "lifecycle_outage_cost": 3.5447692281e6, - "initial_capital_costs": 1.78253229088e7, - "initial_capital_costs_after_incentives": 1.00744773804e7, - "lifecycle_MG_upgrade_and_fuel_cost": 9.1848263748e6, - "lifecycle_elecbill_after_tax": 4.18991821852e7, - "lifecycle_storage_capital_costs": 2.28864728427e7, - "lifecycle_om_costs_before_tax": 1.5859040854e6, - "lifecycle_emissions_cost_climate": 3.50878129e7, - "lifecycle_fuel_costs_after_tax": 0.0, - "lifecycle_capital_costs": 3.06112479158e7, - "lifecycle_production_incentive_after_tax": 0.0, - "lifecycle_offgrid_other_capital_costs": 0.0, - "om_and_replacement_present_cost_after_tax": 2.9071818573e6, - "replacements_future_cost_after_tax": 6.0764124556e6, - "developer_om_and_replacement_present_cost_after_tax": 1.1214265336e6, - "year_one_om_costs_after_tax": 39962.1327, - "lifecycle_chp_standby_cost_after_tax": 0.0 - } -} diff --git a/test/unserved_results_6.json b/test/unserved_results_6.json deleted file mode 100644 index 585e8c7e7..000000000 --- a/test/unserved_results_6.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "Messages": { - "errors": [ - [ - "core_production_factor.jl_87", - [ - "Error occurred when calling Wind Toolkit: HTTP.Exceptions.ConnectError(\"https://gds-hsds-wtk-files.nrelcloud.org/data/wind/854279_39.75_-105.17_2012_052ad8cb28a9d97e6421f4ad70b33db0.csv\", CapturedException(OpenSSL.OpenSSLError(\"self-signed certificate in certificate chain\"), Any[(macro expansion at ssl.jl:512 [inlined], 1), (macro expansion at lock.jl:267 [inlined], 1), (connect(ssl::OpenSSL.SSLStream; require_ssl_verification::Bool) at ssl.jl:505, 1), (connect at ssl.jl:489 [inlined], 1), (#sslconnection#19 at Connections.jl:594 [inlined], 1), (sslconnection at Connections.jl:584 [inlined], 1), (getconnection(::Type{OpenSSL.SSLStream}, host::SubString{String}, port::SubString{String}; kw::Base.Pairs{Symbol, Union{Nothing, Integer}, NTuple{6, Symbol}, @NamedTuple{require_ssl_verification::Bool, keepalive::Bool, readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}) at Connections.jl:581, 1), (getconnection at Connections.jl:573 [inlined], 1), (#10 at Connections.jl:462 [inlined], 1), ((::ConcurrentUtilities.var\"#2#4\"{Any, Timer, ConcurrentUtilities.TimedOut{Any}, Channel{Any}, HTTP.Connections.var\"#10#13\"{OpenSSL.SSLStream, Bool, Bool, Base.Pairs{Symbol, Union{Nothing, Int64}, NTuple{4, Symbol}, @NamedTuple{readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}, SubString{String}, SubString{String}}})() at try_with_timeout.jl:82, 1)]))" - ] - ] - ], - "has_stacktrace": false, - "warnings": [ - [ - "core_electric_utility.jl_208", - [ - "You set co2_from_avert = true and provided values for emissions_factor_series_lb_CO2_per_kwh. REopt will use the provided values for emissions_factor_series_lb_CO2_per_kwh." - ] - ], - [ - "core_electric_utility.jl_321", - [ - "When using stochastic outage modeling (i.e. outage_start_time_steps, outage_durations, outage_probabilities), \n emissions and renewable energy percentage calculations and constraints do not consider outages." - ] - ] - ] - }, - "status": "error" -} diff --git a/test/wind_results_1.json b/test/wind_results_1.json deleted file mode 100644 index dce5f0811..000000000 --- a/test/wind_results_1.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "Messages": { - "errors": [ - [ - "core_production_factor.jl_87", - [ - "Error occurred when calling Wind Toolkit: HTTP.Exceptions.ConnectError(\"https://gds-hsds-wtk-files.nrelcloud.org/data/wind/852132_39.91_-105.24_2012_e6bd2c0f0a6064af19205776fc3d1da8.csv\", CapturedException(OpenSSL.OpenSSLError(\"self-signed certificate in certificate chain\"), Any[(macro expansion at ssl.jl:512 [inlined], 1), (macro expansion at lock.jl:267 [inlined], 1), (connect(ssl::OpenSSL.SSLStream; require_ssl_verification::Bool) at ssl.jl:505, 1), (connect at ssl.jl:489 [inlined], 1), (#sslconnection#19 at Connections.jl:594 [inlined], 1), (sslconnection at Connections.jl:584 [inlined], 1), (getconnection(::Type{OpenSSL.SSLStream}, host::SubString{String}, port::SubString{String}; kw::Base.Pairs{Symbol, Union{Nothing, Integer}, NTuple{6, Symbol}, @NamedTuple{require_ssl_verification::Bool, keepalive::Bool, readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}) at Connections.jl:581, 1), (getconnection at Connections.jl:573 [inlined], 1), (#10 at Connections.jl:462 [inlined], 1), ((::ConcurrentUtilities.var\"#2#4\"{Any, Timer, ConcurrentUtilities.TimedOut{Any}, Channel{Any}, HTTP.Connections.var\"#10#13\"{OpenSSL.SSLStream, Bool, Bool, Base.Pairs{Symbol, Union{Nothing, Int64}, NTuple{4, Symbol}, @NamedTuple{readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}, SubString{String}, SubString{String}}})() at try_with_timeout.jl:82, 1)]))" - ] - ] - ], - "has_stacktrace": false, - "warnings": [] - }, - "status": "error" -} diff --git a/test/wind_results_2.json b/test/wind_results_2.json deleted file mode 100644 index 6a8d9b5e7..000000000 --- a/test/wind_results_2.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "Messages": { - "errors": [ - [ - "core_production_factor.jl_87", - [ - "Error occurred when calling Wind Toolkit: HTTP.Exceptions.ConnectError(\"https://gds-hsds-wtk-files.nrelcloud.org/data/wind/852132_39.91_-105.24_2012_e6bd2c0f0a6064af19205776fc3d1da8.csv\", CapturedException(OpenSSL.OpenSSLError(\"self-signed certificate in certificate chain\"), Any[(macro expansion at ssl.jl:512 [inlined], 1), (macro expansion at lock.jl:267 [inlined], 1), (connect(ssl::OpenSSL.SSLStream; require_ssl_verification::Bool) at ssl.jl:505, 1), (connect at ssl.jl:489 [inlined], 1), (#sslconnection#19 at Connections.jl:594 [inlined], 1), (sslconnection at Connections.jl:584 [inlined], 1), (getconnection(::Type{OpenSSL.SSLStream}, host::SubString{String}, port::SubString{String}; kw::Base.Pairs{Symbol, Union{Nothing, Integer}, NTuple{6, Symbol}, @NamedTuple{require_ssl_verification::Bool, keepalive::Bool, readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}) at Connections.jl:581, 1), (getconnection at Connections.jl:573 [inlined], 1), (#10 at Connections.jl:462 [inlined], 1), ((::ConcurrentUtilities.var\"#2#4\"{Any, Timer, ConcurrentUtilities.TimedOut{Any}, Channel{Any}, HTTP.Connections.var\"#10#13\"{OpenSSL.SSLStream, Bool, Bool, Base.Pairs{Symbol, Union{Nothing, Int64}, NTuple{4, Symbol}, @NamedTuple{readtimeout::Int64, iofunction::Nothing, decompress::Nothing, verbose::Int64}}, SubString{String}, SubString{String}}})() at try_with_timeout.jl:82, 1)]))" - ] - ] - ], - "has_stacktrace": false, - "warnings": [ - [ - "core_reopt_inputs.jl_619", - [ - "User-provided maximum wind kW is greater than the calculated land-constrained kW (site.land_acres/wind.acres_per_kw). Wind max kW has been updated to land-constrained max of 2000.0 kW." - ] - ] - ] - }, - "status": "error" -} diff --git a/test/wind_results_3.json b/test/wind_results_3.json deleted file mode 100644 index 2f47d6866..000000000 --- a/test/wind_results_3.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "Messages": { - "errors": [ - [ - "core_reopt_inputs.jl_623", - [ - "User-provided minimum wind kW is greater than either wind.max_kw or calculated land-constrained kW (site.land_acres/wind.acres_per_kw). Update wind.min_kw or site.land_acres" - ] - ] - ], - "has_stacktrace": false, - "warnings": [ - [ - "core_reopt_inputs.jl_619", - [ - "User-provided maximum wind kW is greater than the calculated land-constrained kW (site.land_acres/wind.acres_per_kw). Wind max kW has been updated to land-constrained max of 2000.0 kW." - ] - ] - ] - }, - "status": "error" -} From 53f7b57295d1872c4514f174ec14e173055747d2 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 13:47:31 -0600 Subject: [PATCH 120/151] fix auto merge errors 1 --- src/constraints/battery_degradation.jl | 6 +++--- src/core/reopt.jl | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/constraints/battery_degradation.jl b/src/constraints/battery_degradation.jl index 13666db99..91fdd147d 100644 --- a/src/constraints/battery_degradation.jl +++ b/src/constraints/battery_degradation.jl @@ -10,7 +10,7 @@ function add_degradation_variables(m, p) end -function constrain_degradation_variables(m, p; b="ElectricStorage") +function constrain_degradation_variables(m, p, b) days = 1:365*p.s.financial.analysis_years ts_per_day = 24 / p.hours_per_time_step ts_per_year = ts_per_day * 365 @@ -41,12 +41,12 @@ end """ - add_degradation(m, p; b="ElectricStorage") + add_degradation(m, p, b) NOTE the average SOC and EFC variables are in absolute units. For example, the SOH variable starts at the battery capacity in kWh. """ -function add_degradation(m, p; b="ElectricStorage") +function add_degradation(m, p, b) days = 1:365*p.s.financial.analysis_years strategy = p.s.storage.attr[b].degradation.maintenance_strategy diff --git a/src/core/reopt.jl b/src/core/reopt.jl index c23c90210..77910b305 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -531,7 +531,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) for b in p.s.storage.types.elec if p.s.storage.attr[b].model_degradation - add_degradation(m, p; b=b) + add_degradation(m, p, b) if p.s.settings.add_soc_incentive @warn "Settings.add_soc_incentive is set to true but no incentive will be added because it conflicts with the battery degradation model." end From 7d53be8afad05ca2f32a4cd21a0975fd8a9c2465 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 13:50:23 -0600 Subject: [PATCH 121/151] correct changelog merge --- CHANGELOG.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 077dfcbf1..c879bca0d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,7 +23,7 @@ Classify the change according to the following categories: ### Deprecated ### Removed -## Develop 2024-09-07 +## Develop LDES ### Added - Added inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M - Added outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` @@ -37,7 +37,7 @@ Classify the change according to the following categories: - Added missing outputs **lifecycle_export_benefit_before_tax** and **year_one_export_benefit_after_tax** to `ElectricTariff` - Add missing output **year_one_om_cost_before_tax** to `PV` -## v0.47.3 +## Develop 08-09-2024 ### Changed - Improve the full test suite reporting with a verbose summary table, and update the structure to reflect long-term open-source solver usage - Removed MacOS from the runner list and just run with Windows OS, since MacOS commonly freezes and gets cancelled. We have not seen Windows OS pass while other OS's fail. . From 95aa65e7ea42b1f3739ecd2d6da3a803869527f7 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:06:29 -0600 Subject: [PATCH 122/151] fix auto merge errors 2 --- src/core/energy_storage/electric_storage.jl | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 63cb028df..900bea1e8 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -172,6 +172,7 @@ end fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true # Simultaneous charge/discharge is typically suboptimal anyway and allowing this avoids additional binary variables (which can slow solve time) + name::String = "ElectricStorage" ``` """ Base.@kwdef struct ElectricStorageDefaults @@ -212,6 +213,7 @@ Base.@kwdef struct ElectricStorageDefaults fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false allow_simultaneous_charge_discharge::Bool = true + name::String = "ElectricStorage" end @@ -260,6 +262,7 @@ struct ElectricStorage <: AbstractElectricStorage fixed_duration::Union{Real, Nothing} optimize_soc_init_fraction::Bool allow_simultaneous_charge_discharge::Bool + name::String function ElectricStorage(d::Dict, f::Financial) s = ElectricStorageDefaults(;d...) @@ -354,7 +357,8 @@ struct ElectricStorage <: AbstractElectricStorage s.self_discharge_fraction_per_timestep, s.fixed_duration, s.optimize_soc_init_fraction, - s.allow_simultaneous_charge_discharge + s.allow_simultaneous_charge_discharge, + s.name ) end end From ccb64128832581af565ac2bf2072e03f18294bd8 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:06:57 -0600 Subject: [PATCH 123/151] fix auto merge errors 3 --- src/core/scenario.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index 98e49b6bc..21c74c017 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -53,7 +53,7 @@ A Scenario struct can contain the following keys: - [SteamTurbine](@ref) (optional) - [ElectricHeater](@ref) (optional) -All values of `d` are expected to be `Dicts` except for `PV` and `GHP`, which can be either a `Dict` or `Dict[]` (for multiple PV arrays or GHP options). +All values of `d` are expected to be `Dicts` except for `PV`, `ElectricStorage`, and `GHP`, which can be either a `Dict` or `Dict[]` (for multiple PV arrays or GHP options). !!! note Set `flex_hvac_from_json=true` if `FlexibleHVAC` values were loaded in from JSON (necessary to From cc346fbbbe59b580c54a76bb91cd62791ba32fd5 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:08:31 -0600 Subject: [PATCH 124/151] fix auto merge errors 4 --- src/constraints/storage_constraints.jl | 4 ++-- src/mpc/model.jl | 8 ++++---- src/results/electric_storage.jl | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 050953b0c..e6a8fbfbd 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -27,10 +27,10 @@ end function add_general_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4a): initial and final state of charge if hasproperty(p.s.storage.attr[b], :optimize_soc_init_fraction) && p.s.storage.attr[b].optimize_soc_init_fraction - # @constraint(m, m[:dvStoredEnergy]["ElectricStorage",maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) + # @constraint(m, m[:dvStoredEnergy][b,maximum(p.time_steps)] == p.s.storage.attr[b].soc_init_fraction * m[Symbol("dvStorageEnergy"*_n)][b] ) print("\nOptimizing "*b*" inital SOC and constraining initial SOC = final SOC\n") @constraint(m, - m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy]["ElectricStorage", maximum(p.time_steps)] + m[Symbol("dvStoredEnergy"*_n)][b, 0] == m[:dvStoredEnergy][b, maximum(p.time_steps)] ) else @constraint(m, diff --git a/src/mpc/model.jl b/src/mpc/model.jl index fa58ca66e..2132b80d0 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -34,10 +34,10 @@ Returns a Dict of results with keys matching those in the `MPCScenario`. function run_mpc(m::JuMP.AbstractModel, p::MPCInputs) build_mpc!(m, p) - if !p.s.settings.add_soc_incentive || !("ElectricStorage" in p.s.storage.types.elec) + if !p.s.settings.add_soc_incentive || isempty(p.s.storage.types.elec) @objective(m, Min, m[:Costs]) else # Keep SOC high - @objective(m, Min, m[:Costs] - sum(m[:dvStoredEnergy]["ElectricStorage", ts] for ts in p.time_steps) / + @objective(m, Min, m[:Costs] - sum(m[:dvStoredEnergy][b, ts] for ts in p.time_steps, b in p.s.storage.types.elec) / (8760. / p.hours_per_time_step) ) end @@ -259,8 +259,8 @@ function add_variables!(m::JuMP.AbstractModel, p::MPCInputs) m[:dvSize] = p.existing_sizes for b in p.s.storage.types.all - fix(m[:dvStoragePower][b], p.s.storage.attr["ElectricStorage"].size_kw, force=true) - fix(m[:dvStorageEnergy][b], p.s.storage.attr["ElectricStorage"].size_kwh, force=true) + fix(m[:dvStoragePower][b], p.s.storage.attr[b].size_kw, force=true) + fix(m[:dvStorageEnergy][b], p.s.storage.attr[b].size_kwh, force=true) end # not modeling min charges since control does not affect them diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 14233cd0b..7548052c4 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -43,7 +43,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: r["year_one_om_cost_after_tax"] = round(value(StoragePerUnitOMCosts) * (1 - p.s.financial.owner_tax_rate_fraction) / (p.pwf_om * p.third_party_factor), digits=0) if p.s.storage.attr[b].model_degradation - r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])["ElectricStorage"]; + r["state_of_health"] = value.(m[:SOH]).data / value.(m[:dvStorageEnergy])[b]; r["maintenance_cost"] = value(m[:degr_cost]) if p.s.storage.attr[b].degradation.maintenance_strategy == "replacement" r["replacement_month"] = Int(value( From 8bbcd38c31b81854c80f975c848c3eb069ea9245 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:08:36 -0600 Subject: [PATCH 125/151] fix auto merge errors 5 --- src/outagesim/outage_simulator.jl | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/outagesim/outage_simulator.jl b/src/outagesim/outage_simulator.jl index 4b3a8f8a5..47a4d9826 100644 --- a/src/outagesim/outage_simulator.jl +++ b/src/outagesim/outage_simulator.jl @@ -223,9 +223,6 @@ Returns a dict ``` """ function simulate_outages(d::Dict, p::REoptInputs; microgrid_only::Bool=false) - batt_roundtrip_efficiency = (p.s.storage.attr["ElectricStorage"].charge_efficiency * - p.s.storage.attr["ElectricStorage"].discharge_efficiency) - # TODO handle generic PV names pv_kw_ac_hourly = zeros(length(p.time_steps)) if "PV" in keys(d) && !(microgrid_only && !Bool(get(d["Outages"], "PV_upgraded", false))) From fec2f30ad5cbec98cfe2a1f43efa5dae07c4dc49 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:08:44 -0600 Subject: [PATCH 126/151] fix auto merge errors 6 --- src/results/chp.jl | 2 +- src/results/electric_utility.jl | 2 +- src/results/generator.jl | 2 +- src/results/steam_turbine.jl | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/results/chp.jl b/src/results/chp.jl index 21be26d47..b154becbf 100644 --- a/src/results/chp.jl +++ b/src/results/chp.jl @@ -60,7 +60,7 @@ function add_chp_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dict; _n="") r["electric_to_grid_series_kw"] = round.(value.(CHPtoGrid), digits=3) if !isempty(p.s.storage.types.elec) @expression(m, CHPtoBatt[ts in p.time_steps], - sum(m[Symbol("dvProductionToStorage"*_n)]["ElectricStorage",t,ts] for t in p.techs.chp)) + sum(m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.chp, b in p.s.storage.types.elec)) else CHPtoBatt = zeros(length(p.time_steps)) end diff --git a/src/results/electric_utility.jl b/src/results/electric_utility.jl index d5b21f467..d5e60805c 100644 --- a/src/results/electric_utility.jl +++ b/src/results/electric_utility.jl @@ -94,7 +94,7 @@ function add_electric_utility_results(m::JuMP.AbstractModel, p::MPCInputs, d::Di tier in 1:p.s.electric_tariff.n_energy_tiers) r["energy_supplied_kwh"] = round(value(Year1UtilityEnergy), digits=2) - if p.s.storage.attr["ElectricStorage"].size_kwh > 0 + if any([p.s.storage.attr[b].size_kwh for b in p.s.storage.types.elec] .> 0) GridToBatt = @expression(m, [ts in p.time_steps], sum(m[Symbol("dvGridToStorage"*_n)][b, ts] for b in p.s.storage.types.elec) ) diff --git a/src/results/generator.jl b/src/results/generator.jl index 22e6ab975..16a00bae1 100644 --- a/src/results/generator.jl +++ b/src/results/generator.jl @@ -90,7 +90,7 @@ function add_generator_results(m::JuMP.AbstractModel, p::MPCInputs, d::Dict; _n= r["variable_om_cost"] = round(value(m[:TotalPerUnitProdOMCosts]), digits=0) r["fuel_cost"] = round(value(m[:TotalGenFuelCosts]), digits=2) - if p.s.storage.attr["ElectricStorage"].size_kw > 0 + if any([p.s.storage.attr[b].size_kw for b in p.s.storage.types.elec] .> 0) generatorToBatt = @expression(m, [ts in p.time_steps], sum(m[:dvProductionToStorage][b, t, ts] for b in p.s.storage.types.elec, t in p.techs.gen)) r["to_battery_series_kw"] = round.(value.(generatorToBatt), digits=3).data diff --git a/src/results/steam_turbine.jl b/src/results/steam_turbine.jl index d0b6d283d..48fe2b40b 100644 --- a/src/results/steam_turbine.jl +++ b/src/results/steam_turbine.jl @@ -51,7 +51,7 @@ function add_steam_turbine_results(m::JuMP.AbstractModel, p::REoptInputs, d::Dic r["electric_to_grid_series_kw"] = round.(value.(SteamTurbinetoGrid), digits=3) if !isempty(p.s.storage.types.elec) @expression(m, SteamTurbinetoBatt[ts in p.time_steps], - sum(m[Symbol("dvProductionToStorage"*_n)]["ElectricStorage",t,ts] for t in p.techs.steam_turbine)) + sum(m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.steam_turbine, b in p.s.storage.types.elec)) else SteamTurbinetoBatt = zeros(length(p.time_steps)) end From e7f8d1689bf99f2f57d3bb141bff1ed7e97100e3 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:20:33 -0600 Subject: [PATCH 127/151] add Logging to test project --- test/Project.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/test/Project.toml b/test/Project.toml index dad017e0e..066554eb4 100644 --- a/test/Project.toml +++ b/test/Project.toml @@ -4,6 +4,7 @@ DotEnv = "4dc1fcf4-5e3b-5448-94ab-0c38ec0385c1" HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" JuMP = "4076af6c-e467-56ae-b986-b466b2749572" +Logging = "56ddb016-857b-54e1-b83d-db4d58db5568" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" Xpress = "9e70acf3-d6c9-5be6-b5bd-4e2c73e3e054" From a138d27c8acdda4547f04aec8cb5afe076b1fde1 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 14:23:38 -0600 Subject: [PATCH 128/151] add b index to binBattCharging (new set approach) --- src/constraints/storage_constraints.jl | 24 +++++++++++------------- src/core/energy_storage/storage.jl | 10 ++++++++-- src/core/reopt.jl | 12 ++++++------ src/mpc/model.jl | 3 +++ src/mpc/model_multinode.jl | 3 +++ 5 files changed, 31 insertions(+), 21 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index e6a8fbfbd..3598f6d7e 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -128,21 +128,19 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") if p.s.storage.attr[b] isa ElectricStorage && !isnothing(p.s.storage.attr[b].fixed_duration) @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end +end +function add_simultaneous_charge_discharge_constraint(m, p, b; _n="") # Prevent charging and discharging of the battery at the same time - if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) - #TODO: implement indicator constraint version for solvers that support it - @constraint(m, [ts in p.time_steps], - m[Symbol("dvGridToStorage"*_n)][b, ts] + - sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) - @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid"*_n)][b, ts] + - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) - end - - + #TODO: implement indicator constraint version for solvers that support it + @constraint(m, [ts in p.time_steps], + m[Symbol("dvGridToStorage"*_n)][b, ts] + + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= + p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][b, ts]) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvStorageToGrid"*_n)][b, ts] + + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= + p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][b, ts])) end function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") diff --git a/src/core/energy_storage/storage.jl b/src/core/energy_storage/storage.jl index f276db728..eb16b7f37 100644 --- a/src/core/energy_storage/storage.jl +++ b/src/core/energy_storage/storage.jl @@ -22,6 +22,7 @@ end mutable struct StorageTypes all::Vector{String} elec::Vector{String} + elec_no_simultaneous_charge_discharge::Vector{String} thermal::Vector{String} hot::Vector{String} cold::Vector{String} @@ -33,6 +34,7 @@ mutable struct StorageTypes String[], String[], String[], + String[], String[] ) end @@ -40,6 +42,7 @@ mutable struct StorageTypes function StorageTypes(d::Dict{String, AbstractStorage}) all_storage = String[] elec_storage = String[] + elec_storage_no_simul_charge_discharge = String[] hot_storage = String[] cold_storage = String[] @@ -50,13 +53,15 @@ mutable struct StorageTypes if typeof(v) <: AbstractElectricStorage push!(elec_storage, k) - + if !v.allow_simultaneous_charge_discharge + push!(elec_storage_no_simul_charge_discharge, k) + end elseif typeof(v) <: HotThermalStorage push!(hot_storage, k) elseif typeof(v) <: ColdThermalStorage push!(cold_storage, k) else - throw(@error("Storage not labeled as Hot or Cold, or Electric.")) + throw(@error("Storage not labeled as Hot, Cold, or Electric.")) end end end @@ -66,6 +71,7 @@ mutable struct StorageTypes new( all_storage, elec_storage, + elec_storage_no_simul_charge_discharge, thermal_storage, hot_storage, cold_storage diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 77910b305..c0f32ee84 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -247,6 +247,9 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) else throw(@error("Invalid storage does not fall in a thermal or electrical set")) end + if b in p.s.storage.types.elec_no_simultaneous_charge_discharge + add_simultaneous_charge_discharge_constraint(m, p, b) + end end end @@ -610,18 +613,15 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) dvStoredEnergy[p.s.storage.types.all, 0:p.time_steps[end]] >= 0 # State of charge of storage system b dvStoragePower[p.s.storage.types.all] >= 0 # Power capacity of storage system b [kW] dvStorageEnergy[p.s.storage.types.all] >= 0 # Energy capacity of storage system b [kWh] + binBattCharging[p.s.storage.types.elec_no_simultaneous_charge_discharge, p.time_steps], Bin # Binary for battery charging (vs discharging) dvPeakDemandTOU[p.ratchets, 1:p.s.electric_tariff.n_tou_demand_tiers] >= 0 # Peak electrical power demand during ratchet r [kW] dvPeakDemandMonth[p.months, 1:p.s.electric_tariff.n_monthly_demand_tiers] >= 0 # Peak electrical power demand during month m [kW] MinChargeAdder >= 0 binGHP[p.ghp_options], Bin # Can be <= 1 if require_ghp_purchase=0, and is ==1 if require_ghp_purchase=1 end - - for b in p.s.storage.types.elec - if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) - @warn "Adding binary variable to prevent simultaneous battery charge/discharge. Some solvers are very slow with integer variables." - @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) - end + if !isempty(p.s.storage.types.elec_no_simultaneous_charge_discharge) + @warn "Adding binary variable to prevent simultaneous battery charge/discharge. Some solvers are very slow with integer variables." end if !isempty(p.techs.gen) # Problem becomes a MILP diff --git a/src/mpc/model.jl b/src/mpc/model.jl index 2132b80d0..8b957fd4e 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -112,6 +112,9 @@ function build_mpc!(m::JuMP.AbstractModel, p::MPCInputs) else throw(@error("Invalid storage does not fall in a thermal or electrical set")) end + if b in p.s.storage.types.elec_no_simultaneous_charge_discharge + add_simultaneous_charge_discharge_constraint(m, p, b) + end end end diff --git a/src/mpc/model_multinode.jl b/src/mpc/model_multinode.jl index 03d1677d1..c4526a353 100644 --- a/src/mpc/model_multinode.jl +++ b/src/mpc/model_multinode.jl @@ -78,6 +78,9 @@ function build_mpc!(m::JuMP.AbstractModel, ps::AbstractVector{MPCInputs}) else @error("Invalid storage does not fall in a thermal or electrical set") end + if b in p.s.storage.types.elec_no_simultaneous_charge_discharge + add_simultaneous_charge_discharge_constraint(m, p, b; _n=_n) + end end end From a4dd6615a5a4d1775dfe8859d291a5fa94c64f68 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 14:23:45 -0600 Subject: [PATCH 129/151] Revert "add b index to binBattCharging (new set approach)" This reverts commit a138d27c8acdda4547f04aec8cb5afe076b1fde1. --- src/constraints/storage_constraints.jl | 24 +++++++++++++----------- src/core/energy_storage/storage.jl | 10 ++-------- src/core/reopt.jl | 12 ++++++------ src/mpc/model.jl | 3 --- src/mpc/model_multinode.jl | 3 --- 5 files changed, 21 insertions(+), 31 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 3598f6d7e..e6a8fbfbd 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -128,19 +128,21 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") if p.s.storage.attr[b] isa ElectricStorage && !isnothing(p.s.storage.attr[b].fixed_duration) @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end -end -function add_simultaneous_charge_discharge_constraint(m, p, b; _n="") # Prevent charging and discharging of the battery at the same time - #TODO: implement indicator constraint version for solvers that support it - @constraint(m, [ts in p.time_steps], - m[Symbol("dvGridToStorage"*_n)][b, ts] + - sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][b, ts]) - @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid"*_n)][b, ts] + - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][b, ts])) + if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) + #TODO: implement indicator constraint version for solvers that support it + @constraint(m, [ts in p.time_steps], + m[Symbol("dvGridToStorage"*_n)][b, ts] + + sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= + p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) + @constraint(m, [ts in p.time_steps], + m[Symbol("dvStorageToGrid"*_n)][b, ts] + + m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= + p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) + end + + end function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") diff --git a/src/core/energy_storage/storage.jl b/src/core/energy_storage/storage.jl index eb16b7f37..f276db728 100644 --- a/src/core/energy_storage/storage.jl +++ b/src/core/energy_storage/storage.jl @@ -22,7 +22,6 @@ end mutable struct StorageTypes all::Vector{String} elec::Vector{String} - elec_no_simultaneous_charge_discharge::Vector{String} thermal::Vector{String} hot::Vector{String} cold::Vector{String} @@ -34,7 +33,6 @@ mutable struct StorageTypes String[], String[], String[], - String[], String[] ) end @@ -42,7 +40,6 @@ mutable struct StorageTypes function StorageTypes(d::Dict{String, AbstractStorage}) all_storage = String[] elec_storage = String[] - elec_storage_no_simul_charge_discharge = String[] hot_storage = String[] cold_storage = String[] @@ -53,15 +50,13 @@ mutable struct StorageTypes if typeof(v) <: AbstractElectricStorage push!(elec_storage, k) - if !v.allow_simultaneous_charge_discharge - push!(elec_storage_no_simul_charge_discharge, k) - end + elseif typeof(v) <: HotThermalStorage push!(hot_storage, k) elseif typeof(v) <: ColdThermalStorage push!(cold_storage, k) else - throw(@error("Storage not labeled as Hot, Cold, or Electric.")) + throw(@error("Storage not labeled as Hot or Cold, or Electric.")) end end end @@ -71,7 +66,6 @@ mutable struct StorageTypes new( all_storage, elec_storage, - elec_storage_no_simul_charge_discharge, thermal_storage, hot_storage, cold_storage diff --git a/src/core/reopt.jl b/src/core/reopt.jl index c0f32ee84..77910b305 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -247,9 +247,6 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) else throw(@error("Invalid storage does not fall in a thermal or electrical set")) end - if b in p.s.storage.types.elec_no_simultaneous_charge_discharge - add_simultaneous_charge_discharge_constraint(m, p, b) - end end end @@ -613,15 +610,18 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) dvStoredEnergy[p.s.storage.types.all, 0:p.time_steps[end]] >= 0 # State of charge of storage system b dvStoragePower[p.s.storage.types.all] >= 0 # Power capacity of storage system b [kW] dvStorageEnergy[p.s.storage.types.all] >= 0 # Energy capacity of storage system b [kWh] - binBattCharging[p.s.storage.types.elec_no_simultaneous_charge_discharge, p.time_steps], Bin # Binary for battery charging (vs discharging) dvPeakDemandTOU[p.ratchets, 1:p.s.electric_tariff.n_tou_demand_tiers] >= 0 # Peak electrical power demand during ratchet r [kW] dvPeakDemandMonth[p.months, 1:p.s.electric_tariff.n_monthly_demand_tiers] >= 0 # Peak electrical power demand during month m [kW] MinChargeAdder >= 0 binGHP[p.ghp_options], Bin # Can be <= 1 if require_ghp_purchase=0, and is ==1 if require_ghp_purchase=1 end - if !isempty(p.s.storage.types.elec_no_simultaneous_charge_discharge) - @warn "Adding binary variable to prevent simultaneous battery charge/discharge. Some solvers are very slow with integer variables." + + for b in p.s.storage.types.elec + if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) + @warn "Adding binary variable to prevent simultaneous battery charge/discharge. Some solvers are very slow with integer variables." + @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) + end end if !isempty(p.techs.gen) # Problem becomes a MILP diff --git a/src/mpc/model.jl b/src/mpc/model.jl index 8b957fd4e..2132b80d0 100644 --- a/src/mpc/model.jl +++ b/src/mpc/model.jl @@ -112,9 +112,6 @@ function build_mpc!(m::JuMP.AbstractModel, p::MPCInputs) else throw(@error("Invalid storage does not fall in a thermal or electrical set")) end - if b in p.s.storage.types.elec_no_simultaneous_charge_discharge - add_simultaneous_charge_discharge_constraint(m, p, b) - end end end diff --git a/src/mpc/model_multinode.jl b/src/mpc/model_multinode.jl index c4526a353..03d1677d1 100644 --- a/src/mpc/model_multinode.jl +++ b/src/mpc/model_multinode.jl @@ -78,9 +78,6 @@ function build_mpc!(m::JuMP.AbstractModel, ps::AbstractVector{MPCInputs}) else @error("Invalid storage does not fall in a thermal or electrical set") end - if b in p.s.storage.types.elec_no_simultaneous_charge_discharge - add_simultaneous_charge_discharge_constraint(m, p, b; _n=_n) - end end end From dae181d49d11f60c20bfefd1fd801040e24b9a7a Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 14:32:09 -0600 Subject: [PATCH 130/151] merge fix --- src/constraints/outage_constraints.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/constraints/outage_constraints.jl b/src/constraints/outage_constraints.jl index f74359121..e50609bb8 100644 --- a/src/constraints/outage_constraints.jl +++ b/src/constraints/outage_constraints.jl @@ -289,8 +289,8 @@ function add_MG_elec_storage_dispatch_constraints(m,p,b) # Prevent simultaneous charge and discharge by limitting charging alone to not make the SOC exceed 100% @constraint(m, [ts in p.time_steps_without_grid], - m[:dvStorageEnergy]["ElectricStorage"] >= m[:dvMGStoredEnergy][s, tz, ts-1] + p.hours_per_time_step * ( - p.s.storage.attr["ElectricStorage"].charge_efficiency * sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) + m[:dvStorageEnergy][b] >= m[:dvMGStoredEnergy][s, tz, ts-1] + p.hours_per_time_step * ( + p.s.storage.attr[b].charge_efficiency * sum(m[:dvMGProductionToStorage][t, s, tz, ts] for t in p.techs.elec) ) ) From 728dfcbce5ca0ec05645ba19dfceb94a1d99e579 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 14:40:55 -0600 Subject: [PATCH 131/151] rm binBattCharging --- src/constraints/storage_constraints.jl | 15 --------------- src/core/reopt.jl | 7 ------- 2 files changed, 22 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 65606790b..a62b950c4 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -142,21 +142,6 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") if p.s.storage.attr[b] isa ElectricStorage && !isnothing(p.s.storage.attr[b].fixed_duration) @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) end - - # Prevent charging and discharging of the battery at the same time - if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) - #TODO: implement indicator constraint version for solvers that support it - @constraint(m, [ts in p.time_steps], - m[Symbol("dvGridToStorage"*_n)][b, ts] + - sum(m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) <= - p.s.storage.attr[b].max_kw * m[Symbol("binBattCharging")][ts]) - @constraint(m, [ts in p.time_steps], - m[Symbol("dvStorageToGrid"*_n)][b, ts] + - m[Symbol("dvDischargeFromStorage"*_n)][b, ts] <= - p.s.storage.attr[b].max_kw * (1-m[Symbol("binBattCharging")][ts])) - end - - end function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 9e25f0f35..23a86307e 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -617,13 +617,6 @@ function add_variables!(m::JuMP.AbstractModel, p::REoptInputs) end - for b in p.s.storage.types.elec - if !(p.s.storage.attr[b].allow_simultaneous_charge_discharge) - @warn "Adding binary variable to prevent simultaneous battery charge/discharge. Some solvers are very slow with integer variables." - @variable(m, binBattCharging[p.time_steps], Bin) # Binary for battery charging (vs discharging) - end - end - if !isempty(p.techs.gen) # Problem becomes a MILP @warn "Adding binary variable to model gas generator. Some solvers are very slow with integer variables." @variables m begin From 3d9eb9090f98f19d62b6ad7f46d59111948b1757 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 14:44:21 -0600 Subject: [PATCH 132/151] update Multiple Electric Storage to include more edge cases --- test/runtests.jl | 5158 +++++++++++---------- test/scenarios/multiple_elec_storage.json | 7 +- 2 files changed, 2584 insertions(+), 2581 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index 9e89e8787..30ae3d8c7 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -26,7 +26,9 @@ else # run HiGHS tests @testset "Multiple Electric Storage" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) r = run_reopt(model, "./scenarios/multiple_elec_storage.json") - + open("debug_results.json","w") do f + JSON.print(f, r, 4) + end @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 for stor in r["ElectricStorage"] @@ -39,2643 +41,2643 @@ else # run HiGHS tests end end end - @testset "Prevent simultaneous charge and discharge" begin - logger = SimpleLogger() - results = nothing - with_logger(logger) do - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/simultaneous_charge_discharge.json") - end - @test any(.&( - results["ElectricStorage"]["storage_to_load_series_kw"] .!= 0.0, - ( - results["ElectricUtility"]["electric_to_storage_series_kw"] .+ - results["PV"]["electric_to_storage_series_kw"] - ) .!= 0.0 - ) - ) ≈ false - @test any(.&( - results["Outages"]["storage_discharge_series_kw"] .!= 0.0, - results["Outages"]["pv_to_storage_series_kw"] .!= 0.0 - ) - ) ≈ false - end - @testset "hybrid profile" begin - electric_load = REopt.ElectricLoad(; - blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], - blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], - annual_kwh = 50000.0, - year = 2017, - city = "Atlanta", - latitude = 35.2468, - longitude = -91.7337 - ) - @test sum(electric_load.loads_kw) ≈ 50000.0 - end - @testset "Solar dataset" begin - - # 1. Dallas TX - latitude, longitude = 32.775212075983646, -96.78105623767185 - radius = 0 - dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - @test dataset ≈ "nsrdb" - - # 2. Merefa, Ukraine - latitude, longitude = 49.80670544975866, 36.05418033509974 - radius = 0 - dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - @test dataset ≈ "nsrdb" - - # 3. Oulu, Findland - latitude, longitude = 65.0102196310875, 25.465387094897675 - radius = 0 - dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - @test dataset ≈ "intl" - - # 4. Fairbanks, AK - site = "Fairbanks" - latitude, longitude = 64.84112047064114, -147.71570239058084 - radius = 20 - dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - @test dataset ≈ "tmy3" - end - - @testset "January Export Rates" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/monthly_rate.json") - - # create wholesale_rate with compensation in January > retail rate - jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - data["ElectricTariff"]["wholesale_rate"] = - append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(model, inputs) - - @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 - @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC - @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) - end - - @testset "Blended tariff" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/no_techs.json") - @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - end - - @testset "Solar and Storage" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(model, "./scenarios/pv_storage.json") - - @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 - @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 - @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - end - - @testset "Outage with Generator" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/generator.json") - @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - p = REoptInputs("./scenarios/generator.json") - simresults = simulate_outages(results, p) - @test simresults["resilience_hours_max"] == 11 - end - - # TODO test MPC with outages - @testset "MPC" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_mpc(model, "./scenarios/mpc.json") - @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - end - - @testset "MPC Multi-node" begin - # not doing much yet; just testing that two identical sites have the same costs - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - ps = MPCInputs[] - push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); - push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); - r = run_mpc(model, ps) - @test r[1]["Costs"] ≈ r[2]["Costs"] - end - - @testset "Complex Incentives" begin - """ - This test was compared against the API test: - reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives - when using the hardcoded levelization_factor in this package's REoptInputs function. - The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) - """ - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/incentives.json") - @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 - end - - @testset "Fifteen minute load" begin - d = JSON.parsefile("scenarios/no_techs.json") - d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) - d["Settings"] = Dict("time_steps_per_hour" => 4) - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, d) - @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 - end - - try - rm("Highs.log", force=true) - catch - @warn "Could not delete test/Highs.log" - end - - @testset "AVERT region abberviations" begin - """ - This test checks 5 scenarios (in order) - 1. Coordinate pair inside an AVERT polygon - 2. Coordinate pair near a US border - 3. Coordinate pair < 5 miles from US border - 4. Coordinate pair > 5 miles from US border - 5. Coordinate pair >> 5 miles from US border - """ - (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) - @test r == "AKGD" - (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) - @test r == "HIOA" - (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) - @test r == "HIMS" - (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) - @test r == "RM" - @test d ≈ 0.0 atol=1 - (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) - @test r == "NE" - @test d ≈ 7986 atol=1 - (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) - @test r === nothing - @test d ≈ 10297 atol=1 - (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) - @test r === nothing - end - - @testset "PVspecs" begin - ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) - post_name = "pv.json" - post = JSON.parsefile("./scenarios/$post_name") - scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 20 - @test scen.pvs[1].azimuth ≈ 180 + # @testset "Prevent simultaneous charge and discharge" begin + # logger = SimpleLogger() + # results = nothing + # with_logger(logger) do + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/simultaneous_charge_discharge.json") + # end + # @test any(.&( + # results["ElectricStorage"]["storage_to_load_series_kw"] .!= 0.0, + # ( + # results["ElectricUtility"]["electric_to_storage_series_kw"] .+ + # results["PV"]["electric_to_storage_series_kw"] + # ) .!= 0.0 + # ) + # ) ≈ false + # @test any(.&( + # results["Outages"]["storage_discharge_series_kw"] .!= 0.0, + # results["Outages"]["pv_to_storage_series_kw"] .!= 0.0 + # ) + # ) ≈ false + # end + # @testset "hybrid profile" begin + # electric_load = REopt.ElectricLoad(; + # blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], + # blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], + # annual_kwh = 50000.0, + # year = 2017, + # city = "Atlanta", + # latitude = 35.2468, + # longitude = -91.7337 + # ) + # @test sum(electric_load.loads_kw) ≈ 50000.0 + # end + # @testset "Solar dataset" begin + + # # 1. Dallas TX + # latitude, longitude = 32.775212075983646, -96.78105623767185 + # radius = 0 + # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + # @test dataset ≈ "nsrdb" + + # # 2. Merefa, Ukraine + # latitude, longitude = 49.80670544975866, 36.05418033509974 + # radius = 0 + # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + # @test dataset ≈ "nsrdb" + + # # 3. Oulu, Findland + # latitude, longitude = 65.0102196310875, 25.465387094897675 + # radius = 0 + # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + # @test dataset ≈ "intl" + + # # 4. Fairbanks, AK + # site = "Fairbanks" + # latitude, longitude = 64.84112047064114, -147.71570239058084 + # radius = 20 + # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + # @test dataset ≈ "tmy3" + # end + + # @testset "January Export Rates" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/monthly_rate.json") + + # # create wholesale_rate with compensation in January > retail rate + # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + # data["ElectricTariff"]["wholesale_rate"] = + # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(model, inputs) + + # @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 + # @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC + # @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) + # end + + # @testset "Blended tariff" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/no_techs.json") + # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + # end + + # @testset "Solar and Storage" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(model, "./scenarios/pv_storage.json") + + # @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + # @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 + # @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 + # @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + # end + + # @testset "Outage with Generator" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/generator.json") + # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + # p = REoptInputs("./scenarios/generator.json") + # simresults = simulate_outages(results, p) + # @test simresults["resilience_hours_max"] == 11 + # end + + # # TODO test MPC with outages + # @testset "MPC" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_mpc(model, "./scenarios/mpc.json") + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + # end + + # @testset "MPC Multi-node" begin + # # not doing much yet; just testing that two identical sites have the same costs + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # ps = MPCInputs[] + # push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); + # push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); + # r = run_mpc(model, ps) + # @test r[1]["Costs"] ≈ r[2]["Costs"] + # end + + # @testset "Complex Incentives" begin + # """ + # This test was compared against the API test: + # reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives + # when using the hardcoded levelization_factor in this package's REoptInputs function. + # The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) + # """ + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/incentives.json") + # @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 + # end + + # @testset "Fifteen minute load" begin + # d = JSON.parsefile("scenarios/no_techs.json") + # d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) + # d["Settings"] = Dict("time_steps_per_hour" => 4) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, d) + # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 + # end + + # try + # rm("Highs.log", force=true) + # catch + # @warn "Could not delete test/Highs.log" + # end + + # @testset "AVERT region abberviations" begin + # """ + # This test checks 5 scenarios (in order) + # 1. Coordinate pair inside an AVERT polygon + # 2. Coordinate pair near a US border + # 3. Coordinate pair < 5 miles from US border + # 4. Coordinate pair > 5 miles from US border + # 5. Coordinate pair >> 5 miles from US border + # """ + # (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) + # @test r == "AKGD" + # (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) + # @test r == "HIOA" + # (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) + # @test r == "HIMS" + # (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) + # @test r == "RM" + # @test d ≈ 0.0 atol=1 + # (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) + # @test r == "NE" + # @test d ≈ 7986 atol=1 + # (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) + # @test r === nothing + # @test d ≈ 10297 atol=1 + # (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) + # @test r === nothing + # end + + # @testset "PVspecs" begin + # ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) + # post_name = "pv.json" + # post = JSON.parsefile("./scenarios/$post_name") + # scen = Scenario(post) + # @test scen.pvs[1].tilt ≈ 20 + # @test scen.pvs[1].azimuth ≈ 180 - ## Scenario 2: Palmdale, CA; array-type = 1 (roof) - post["PV"]["array_type"] = 1 - scen = Scenario(post) + # ## Scenario 2: Palmdale, CA; array-type = 1 (roof) + # post["PV"]["array_type"] = 1 + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 + # @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 - ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) - post["PV"]["array_type"] = 2 - scen = Scenario(post) + # ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) + # post["PV"]["array_type"] = 2 + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 + # @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 - ## Scenario 4: Cape Town; array-type = 0 (ground) - post["Site"]["latitude"] = -33.974732 - post["Site"]["longitude"] = 19.130050 - post["PV"]["array_type"] = 0 - scen = Scenario(post) + # ## Scenario 4: Cape Town; array-type = 0 (ground) + # post["Site"]["latitude"] = -33.974732 + # post["Site"]["longitude"] = 19.130050 + # post["PV"]["array_type"] = 0 + # scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 20 - @test scen.pvs[1].azimuth ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - - ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) - post["PV"]["tilt"] = 17 - scen = Scenario(post) - @test scen.pvs[1].tilt ≈ 17 - end - - @testset "AlternativeFlatLoads" begin - input_data = JSON.parsefile("./scenarios/flatloads.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - - # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year - active_hours_8_5 = 8 * 5 * 52 - @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 - # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year - @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 - @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 - end + # @test scen.pvs[1].tilt ≈ 20 + # @test scen.pvs[1].azimuth ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + + # ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) + # post["PV"]["tilt"] = 17 + # scen = Scenario(post) + # @test scen.pvs[1].tilt ≈ 17 + # end + + # @testset "AlternativeFlatLoads" begin + # input_data = JSON.parsefile("./scenarios/flatloads.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year + # active_hours_8_5 = 8 * 5 * 52 + # @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 + # # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year + # @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 + # @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 + # end - # removed Wind test for two reasons - # 1. reduce WindToolKit calls in tests - # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints + # # removed Wind test for two reasons + # # 1. reduce WindToolKit calls in tests + # # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints - @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin - """ + # @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin + # """ - This tests the consistency between getting DOE commercial reference building (CRB) load data - from the simulated_load function and the processing of REoptInputs.s (Scenario struct). + # This tests the consistency between getting DOE commercial reference building (CRB) load data + # from the simulated_load function and the processing of REoptInputs.s (Scenario struct). - The simulated_load function is used for the /simulated_load endpoint in the REopt API, - in particular for the webtool/UI to display loads before running REopt, but is also generally - an external way to access CRB load data without running REopt. - - One particular test specifically for the webtool/UI is for the heating load because there is just a - single heating load instead of separated space heating and domestic hot water loads. - - """ - input_data = JSON.parsefile("./scenarios/simulated_load.json") - - input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), - ("blended_doe_reference_percents", [0.2, 0.8]) - ]) - - input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["Warehouse", "FlatLoad"]), - ("blended_doe_reference_percents", [0.5, 0.5]) - ]) - - # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu - annual_mmbtu_hvac = 7000.0 - annual_mmbtu_process = 3000.0 - doe_reference_name_heating = ["Warehouse", "FlatLoad"] - percent_share_heating = [0.3, 0.7] - - d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), - ("longitude", input_data["Site"]["longitude"]), - ("load_type", "heating"), # since annual_tonhour is not given - ("doe_reference_name", doe_reference_name_heating), - ("percent_share", percent_share_heating), - ("annual_mmbtu", annual_mmbtu_hvac) - ]) - - sim_load_response_heating = simulated_load(d_sim_load_heating) - - d_sim_load_process = copy(d_sim_load_heating) - d_sim_load_process["load_type"] = "process_heat" - d_sim_load_process["annual_mmbtu"] = annual_mmbtu_process - sim_load_response_process = simulated_load(d_sim_load_process) - - input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - ("blended_doe_reference_percents", percent_share_heating), - ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) - ]) - - input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - ("blended_doe_reference_percents", percent_share_heating), - ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) - ]) - - input_data["ProcessHeatLoad"] = Dict([("blended_industry_reference_names", doe_reference_name_heating), - ("blended_industry_reference_percents", percent_share_heating), - ("annual_mmbtu", annual_mmbtu_process) - ]) + # The simulated_load function is used for the /simulated_load endpoint in the REopt API, + # in particular for the webtool/UI to display loads before running REopt, but is also generally + # an external way to access CRB load data without running REopt. + + # One particular test specifically for the webtool/UI is for the heating load because there is just a + # single heating load instead of separated space heating and domestic hot water loads. + + # """ + # input_data = JSON.parsefile("./scenarios/simulated_load.json") + + # input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), + # ("blended_doe_reference_percents", [0.2, 0.8]) + # ]) + + # input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["Warehouse", "FlatLoad"]), + # ("blended_doe_reference_percents", [0.5, 0.5]) + # ]) + + # # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu + # annual_mmbtu_hvac = 7000.0 + # annual_mmbtu_process = 3000.0 + # doe_reference_name_heating = ["Warehouse", "FlatLoad"] + # percent_share_heating = [0.3, 0.7] + + # d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), + # ("longitude", input_data["Site"]["longitude"]), + # ("load_type", "heating"), # since annual_tonhour is not given + # ("doe_reference_name", doe_reference_name_heating), + # ("percent_share", percent_share_heating), + # ("annual_mmbtu", annual_mmbtu_hvac) + # ]) + + # sim_load_response_heating = simulated_load(d_sim_load_heating) + + # d_sim_load_process = copy(d_sim_load_heating) + # d_sim_load_process["load_type"] = "process_heat" + # d_sim_load_process["annual_mmbtu"] = annual_mmbtu_process + # sim_load_response_process = simulated_load(d_sim_load_process) + + # input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + # ("blended_doe_reference_percents", percent_share_heating), + # ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) + # ]) + + # input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + # ("blended_doe_reference_percents", percent_share_heating), + # ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) + # ]) + + # input_data["ProcessHeatLoad"] = Dict([("blended_industry_reference_names", doe_reference_name_heating), + # ("blended_industry_reference_percents", percent_share_heating), + # ("annual_mmbtu", annual_mmbtu_process) + # ]) - s = Scenario(input_data) - inputs = REoptInputs(s) - - # Call simulated_load function to check cooling - d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), - ("longitude", input_data["Site"]["longitude"]), - ("load_type", "electric"), # since annual_tonhour is not given - ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), - ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), - ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), - ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), - ]) - - sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) - sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] - sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] - - total_heating_thermal_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw + s.process_heat_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY - - @test round.(sim_load_response_heating["loads_mmbtu_per_hour"] + - sim_load_response_process["loads_mmbtu_per_hour"], digits=2) ≈ - round.(total_heating_thermal_load_reopt_inputs, digits=2) rtol=0.02 - - @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 - @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 - end - - @testset verbose=true "Backup Generator Reliability" begin - - @testset "Compare backup_reliability and simulate_outages" begin - # Tests ensure `backup_reliability()` consistent with `simulate_outages()` - # First, just battery - reopt_inputs = Dict( - "Site" => Dict( - "longitude" => -106.42077256104001, - "latitude" => 31.810468380036337 - ), - "ElectricStorage" => Dict( - "min_kw" => 4000, - "max_kw" => 4000, - "min_kwh" => 400000, - "max_kwh" => 400000, - "soc_min_fraction" => 0.8, - "soc_init_fraction" => 0.9 - ), - "ElectricLoad" => Dict( - "doe_reference_name" => "FlatLoad", - "annual_kwh" => 175200000.0, - "critical_load_fraction" => 0.2 - ), - "ElectricTariff" => Dict( - "urdb_label" => "5ed6c1a15457a3367add15ae" - ), - ) - p = REoptInputs(reopt_inputs) - model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, p) - simresults = simulate_outages(results, p) - - reliability_inputs = Dict( - "generator_size_kw" => 0, - "max_outage_duration" => 100, - "generator_operational_availability" => 1.0, - "generator_failure_to_start" => 0.0, - "generator_mean_time_to_failure" => 10000000000, - "fuel_limit" => 0, - "battery_size_kw" => 4000, - "battery_size_kwh" => 400000, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_operational_availability" => 1.0, - "battery_minimum_soc_fraction" => 0.0, - "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], - "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw - ) - reliability_results = backup_reliability(reliability_inputs) - - #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early - for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 - @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 - @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 - end - - # Second, gen, PV, Wind, battery - reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") - reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] - p = REoptInputs(reopt_inputs) - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, p) - simresults = simulate_outages(results, p) - reliability_inputs = Dict( - "max_outage_duration" => 48, - "generator_operational_availability" => 1.0, - "generator_failure_to_start" => 0.0, - "generator_mean_time_to_failure" => 10000000000, - "fuel_limit" => 1000000000, - "battery_operational_availability" => 1.0, - "battery_minimum_soc_fraction" => 0.0, - "pv_operational_availability" => 1.0, - "wind_operational_availability" => 1.0 - ) - reliability_results = backup_reliability(results, p, reliability_inputs) - for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 - end - end - - # Test survival with no generator decreasing and same as with generator but no fuel - reliability_inputs = Dict( - "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), - "num_generators" => 0, - "generator_size_kw" => 312.0, - "fuel_limit" => 0.0, - "max_outage_duration" => 10, - "battery_size_kw" => 428.0, - "battery_size_kwh" => 1585.0, - "num_battery_bins" => 5 - ) - reliability_results1 = backup_reliability(reliability_inputs) - reliability_inputs["generator_size_kw"] = 0 - reliability_inputs["fuel_limit"] = 1e10 - reliability_results2 = backup_reliability(reliability_inputs) - for i in 1:reliability_inputs["max_outage_duration"] - if i != 1 - @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] - @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] - end - @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] - end - - #test fuel limit - input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") - results = backup_reliability(input_dict) - @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 - @test results["cumulative_survival_final_time_step"][1] ≈ 1 - - input_dict = Dict( - "critical_loads_kw" => [1,2,2,1], - "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], - "max_outage_duration" => 3, - "num_generators" => 2, "generator_size_kw" => 1, - "generator_operational_availability" => 1, - "generator_failure_to_start" => 0.0, - "generator_mean_time_to_failure" => 5, - "battery_operational_availability" => 1, - "num_battery_bins" => 3, - "battery_size_kwh" => 4, - "battery_size_kw" => 1, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_minimum_soc_fraction" => 0.5) - - - #Given outage starts in time period 1 - #____________________________________ - #Outage hour 1: - #2 generators: Prob = 0.64, Battery = 2, Survived - #1 generator: Prob = 0.32, Battery = 1, Survived - #0 generator: Prob = 0.04, Battery = 0, Survived - #Survival Probability 1.0 - - #Outage hour 2: - #2 generators: Prob = 0.4096, Battery = 2, Survived - #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived - #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived - #0 generators: Prob = 0.1296, Battery = -1, Failed - #Survival Probability: 0.8704 - - #Outage hour 3: - #2 generators: Prob = 0.262144, Battery = 0, Survived - #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived - #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived - #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed - #0 generators Prob = 0.238144, Battery = -1, Failed - #Survival Probability: 0.557056 - @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - #Test multiple generator types - input_dict = Dict( - "critical_loads_kw" => [1,2,2,1], - "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], - "max_outage_duration" => 3, - "num_generators" => [1,1], - "generator_size_kw" => [1,1], - "generator_operational_availability" => [1,1], - "generator_failure_to_start" => [0.0, 0.0], - "generator_mean_time_to_failure" => [5, 5], - "battery_operational_availability" => 1.0, - "num_battery_bins" => 3, - "battery_size_kwh" => 2, - "battery_size_kw" => 1, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_minimum_soc_fraction" => 0) - - @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - #8760 of flat load. Battery can survive 4 hours. - #Survival after 24 hours should be chance of generator surviving 20 or more hours - input_dict = Dict( - "critical_loads_kw" => 100 .* ones(8760), - "max_outage_duration" => 24, - "num_generators" => 1, - "generator_size_kw" => 100, - "generator_operational_availability" => 0.98, - "generator_failure_to_start" => 0.1, - "generator_mean_time_to_failure" => 100, - "battery_operational_availability" => 1.0, - "num_battery_bins" => 101, - "battery_size_kwh" => 400, - "battery_size_kw" => 100, - "battery_charge_efficiency" => 1, - "battery_discharge_efficiency" => 1, - "battery_minimum_soc_fraction" => 0) - - reliability_results = backup_reliability(input_dict) - @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 - - #More complex case of hospital load with 2 generators, PV, wind, and battery - reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") - reliability_results = backup_reliability(reliability_inputs) - @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # # Call simulated_load function to check cooling + # d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), + # ("longitude", input_data["Site"]["longitude"]), + # ("load_type", "electric"), # since annual_tonhour is not given + # ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), + # ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), + # ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), + # ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), + # ]) + + # sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) + # sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] + # sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] + + # total_heating_thermal_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw + s.process_heat_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY + + # @test round.(sim_load_response_heating["loads_mmbtu_per_hour"] + + # sim_load_response_process["loads_mmbtu_per_hour"], digits=2) ≈ + # round.(total_heating_thermal_load_reopt_inputs, digits=2) rtol=0.02 + + # @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 + # @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 + # end + + # @testset verbose=true "Backup Generator Reliability" begin + + # @testset "Compare backup_reliability and simulate_outages" begin + # # Tests ensure `backup_reliability()` consistent with `simulate_outages()` + # # First, just battery + # reopt_inputs = Dict( + # "Site" => Dict( + # "longitude" => -106.42077256104001, + # "latitude" => 31.810468380036337 + # ), + # "ElectricStorage" => Dict( + # "min_kw" => 4000, + # "max_kw" => 4000, + # "min_kwh" => 400000, + # "max_kwh" => 400000, + # "soc_min_fraction" => 0.8, + # "soc_init_fraction" => 0.9 + # ), + # "ElectricLoad" => Dict( + # "doe_reference_name" => "FlatLoad", + # "annual_kwh" => 175200000.0, + # "critical_load_fraction" => 0.2 + # ), + # "ElectricTariff" => Dict( + # "urdb_label" => "5ed6c1a15457a3367add15ae" + # ), + # ) + # p = REoptInputs(reopt_inputs) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, p) + # simresults = simulate_outages(results, p) + + # reliability_inputs = Dict( + # "generator_size_kw" => 0, + # "max_outage_duration" => 100, + # "generator_operational_availability" => 1.0, + # "generator_failure_to_start" => 0.0, + # "generator_mean_time_to_failure" => 10000000000, + # "fuel_limit" => 0, + # "battery_size_kw" => 4000, + # "battery_size_kwh" => 400000, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_operational_availability" => 1.0, + # "battery_minimum_soc_fraction" => 0.0, + # "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], + # "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw + # ) + # reliability_results = backup_reliability(reliability_inputs) + + # #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early + # for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 + # end + + # # Second, gen, PV, Wind, battery + # reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") + # reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] + # p = REoptInputs(reopt_inputs) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, p) + # simresults = simulate_outages(results, p) + # reliability_inputs = Dict( + # "max_outage_duration" => 48, + # "generator_operational_availability" => 1.0, + # "generator_failure_to_start" => 0.0, + # "generator_mean_time_to_failure" => 10000000000, + # "fuel_limit" => 1000000000, + # "battery_operational_availability" => 1.0, + # "battery_minimum_soc_fraction" => 0.0, + # "pv_operational_availability" => 1.0, + # "wind_operational_availability" => 1.0 + # ) + # reliability_results = backup_reliability(results, p, reliability_inputs) + # for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 + # end + # end + + # # Test survival with no generator decreasing and same as with generator but no fuel + # reliability_inputs = Dict( + # "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), + # "num_generators" => 0, + # "generator_size_kw" => 312.0, + # "fuel_limit" => 0.0, + # "max_outage_duration" => 10, + # "battery_size_kw" => 428.0, + # "battery_size_kwh" => 1585.0, + # "num_battery_bins" => 5 + # ) + # reliability_results1 = backup_reliability(reliability_inputs) + # reliability_inputs["generator_size_kw"] = 0 + # reliability_inputs["fuel_limit"] = 1e10 + # reliability_results2 = backup_reliability(reliability_inputs) + # for i in 1:reliability_inputs["max_outage_duration"] + # if i != 1 + # @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] + # @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] + # end + # @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] + # end + + # #test fuel limit + # input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") + # results = backup_reliability(input_dict) + # @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 + # @test results["cumulative_survival_final_time_step"][1] ≈ 1 + + # input_dict = Dict( + # "critical_loads_kw" => [1,2,2,1], + # "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], + # "max_outage_duration" => 3, + # "num_generators" => 2, "generator_size_kw" => 1, + # "generator_operational_availability" => 1, + # "generator_failure_to_start" => 0.0, + # "generator_mean_time_to_failure" => 5, + # "battery_operational_availability" => 1, + # "num_battery_bins" => 3, + # "battery_size_kwh" => 4, + # "battery_size_kw" => 1, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_minimum_soc_fraction" => 0.5) + + + # #Given outage starts in time period 1 + # #____________________________________ + # #Outage hour 1: + # #2 generators: Prob = 0.64, Battery = 2, Survived + # #1 generator: Prob = 0.32, Battery = 1, Survived + # #0 generator: Prob = 0.04, Battery = 0, Survived + # #Survival Probability 1.0 + + # #Outage hour 2: + # #2 generators: Prob = 0.4096, Battery = 2, Survived + # #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived + # #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived + # #0 generators: Prob = 0.1296, Battery = -1, Failed + # #Survival Probability: 0.8704 + + # #Outage hour 3: + # #2 generators: Prob = 0.262144, Battery = 0, Survived + # #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived + # #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived + # #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed + # #0 generators Prob = 0.238144, Battery = -1, Failed + # #Survival Probability: 0.557056 + # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + # #Test multiple generator types + # input_dict = Dict( + # "critical_loads_kw" => [1,2,2,1], + # "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], + # "max_outage_duration" => 3, + # "num_generators" => [1,1], + # "generator_size_kw" => [1,1], + # "generator_operational_availability" => [1,1], + # "generator_failure_to_start" => [0.0, 0.0], + # "generator_mean_time_to_failure" => [5, 5], + # "battery_operational_availability" => 1.0, + # "num_battery_bins" => 3, + # "battery_size_kwh" => 2, + # "battery_size_kw" => 1, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_minimum_soc_fraction" => 0) + + # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + # #8760 of flat load. Battery can survive 4 hours. + # #Survival after 24 hours should be chance of generator surviving 20 or more hours + # input_dict = Dict( + # "critical_loads_kw" => 100 .* ones(8760), + # "max_outage_duration" => 24, + # "num_generators" => 1, + # "generator_size_kw" => 100, + # "generator_operational_availability" => 0.98, + # "generator_failure_to_start" => 0.1, + # "generator_mean_time_to_failure" => 100, + # "battery_operational_availability" => 1.0, + # "num_battery_bins" => 101, + # "battery_size_kwh" => 400, + # "battery_size_kw" => 100, + # "battery_charge_efficiency" => 1, + # "battery_discharge_efficiency" => 1, + # "battery_minimum_soc_fraction" => 0) + + # reliability_results = backup_reliability(input_dict) + # @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 + + # #More complex case of hospital load with 2 generators, PV, wind, and battery + # reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") + # reliability_results = backup_reliability(reliability_inputs) + # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 - # Test gens+pv+wind+batt with 3 arg version of backup_reliability - # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json - # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: - # results = run_reopt(model, p) - # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f - # JSON.print(f, results, 4) - # end - for input_key in [ - "generator_size_kw", - "battery_size_kw", - "battery_size_kwh", - "pv_size_kw", - "wind_size_kw", - "critical_loads_kw", - "pv_production_factor_series", - "wind_production_factor_series" - ] - delete!(reliability_inputs, input_key) - end - # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) - p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") - results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") - reliability_results = backup_reliability(results, p, reliability_inputs) - - @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 - end - - #candidate for hot stor - @testset verbose=true "Disaggregated Heating Loads" begin - @testset "Process Heat Load Inputs" begin - d = JSON.parsefile("./scenarios/electric_heater.json") - d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 - s = Scenario(d) - inputs = REoptInputs(s) - @test inputs.heating_loads_kw["ProcessHeat"][1] ≈ 117.228428 atol=1.0e-3 - end - @testset "Separate Heat Load Results" begin - d = JSON.parsefile("./scenarios/electric_heater.json") - d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 - d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 - d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 - d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] - d["HotThermalStorage"]["max_gal"] = 0.0 - s = Scenario(d) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, inputs) - @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 - @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 - @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 - @test sum(results["ElectricHeater"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 - @test sum(results["ElectricHeater"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 - @test sum(results["ElectricHeater"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 - end - end - - @testset verbose=true "Net Metering" begin - @testset "Net Metering Limit and Wholesale" begin - #case 1: net metering limit is met by PV - d = JSON.parsefile("./scenarios/net_metering.json") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, d) - @test results["PV"]["size_kw"] ≈ 30.0 atol=1e-3 + # # Test gens+pv+wind+batt with 3 arg version of backup_reliability + # # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json + # # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: + # # results = run_reopt(model, p) + # # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f + # # JSON.print(f, results, 4) + # # end + # for input_key in [ + # "generator_size_kw", + # "battery_size_kw", + # "battery_size_kwh", + # "pv_size_kw", + # "wind_size_kw", + # "critical_loads_kw", + # "pv_production_factor_series", + # "wind_production_factor_series" + # ] + # delete!(reliability_inputs, input_key) + # end + # # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) + # p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") + # results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") + # reliability_results = backup_reliability(results, p, reliability_inputs) + + # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 + # end + + # #candidate for hot stor + # @testset verbose=true "Disaggregated Heating Loads" begin + # @testset "Process Heat Load Inputs" begin + # d = JSON.parsefile("./scenarios/electric_heater.json") + # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + # d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 + # s = Scenario(d) + # inputs = REoptInputs(s) + # @test inputs.heating_loads_kw["ProcessHeat"][1] ≈ 117.228428 atol=1.0e-3 + # end + # @testset "Separate Heat Load Results" begin + # d = JSON.parsefile("./scenarios/electric_heater.json") + # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + # d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 + # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 + # d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 + # d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] + # d["HotThermalStorage"]["max_gal"] = 0.0 + # s = Scenario(d) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, inputs) + # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 + # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 + # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 + # @test sum(results["ElectricHeater"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 + # @test sum(results["ElectricHeater"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 + # @test sum(results["ElectricHeater"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 + # end + # end + + # @testset verbose=true "Net Metering" begin + # @testset "Net Metering Limit and Wholesale" begin + # #case 1: net metering limit is met by PV + # d = JSON.parsefile("./scenarios/net_metering.json") + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, d) + # @test results["PV"]["size_kw"] ≈ 30.0 atol=1e-3 - #case 2: wholesale rate is high, big-M is met - d["ElectricTariff"]["wholesale_rate"] = 5.0 - d["PV"]["can_wholesale"] = true - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, d) - @test results["PV"]["size_kw"] ≈ 7440.0 atol=1e-3 #max benefit provides the upper bound + # #case 2: wholesale rate is high, big-M is met + # d["ElectricTariff"]["wholesale_rate"] = 5.0 + # d["PV"]["can_wholesale"] = true + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, d) + # @test results["PV"]["size_kw"] ≈ 7440.0 atol=1e-3 #max benefit provides the upper bound - end - end + # end + # end - @testset "Electric Storage O&M and Self-Discharge" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/storage_om.json") + # @testset "Electric Storage O&M and Self-Discharge" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/storage_om.json") - data["ElectricStorage"]["om_cost_per_kw"] = 10 - data["ElectricStorage"]["om_cost_per_kwh"] = 0 + # data["ElectricStorage"]["om_cost_per_kw"] = 10 + # data["ElectricStorage"]["om_cost_per_kwh"] = 0 - inputs = REoptInputs(data) - results1 = run_reopt(model, inputs) + # inputs = REoptInputs(data) + # results1 = run_reopt(model, inputs) - @test results1["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 - @test results1["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 + # @test results1["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 + # @test results1["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 - data["ElectricStorage"]["om_cost_per_kw"] = 10 - data["ElectricStorage"]["om_cost_per_kwh"] = 5 + # data["ElectricStorage"]["om_cost_per_kw"] = 10 + # data["ElectricStorage"]["om_cost_per_kwh"] = 5 - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - inputs = REoptInputs(data) - results2 = run_reopt(model, inputs) + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # inputs = REoptInputs(data) + # results2 = run_reopt(model, inputs) - @test results2["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 - @test results2["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 - @test results2["Financial"]["lcc"] ≈ results1["Financial"]["lcc"] + 12543 - 6272 atol=1 + # @test results2["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 + # @test results2["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 + # @test results2["Financial"]["lcc"] ≈ results1["Financial"]["lcc"] + 12543 - 6272 atol=1 - data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - inputs = REoptInputs(data) - results3 = run_reopt(model, inputs) - @test sum(results2["ElectricStorage"]["storage_to_load_series_kw"]) - - sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 - end + # data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # inputs = REoptInputs(data) + # results3 = run_reopt(model, inputs) + # @test sum(results2["ElectricStorage"]["storage_to_load_series_kw"]) - + # sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + # end - @testset "Heating loads and addressable load fraction" begin - # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, "./scenarios/thermal_load.json") + # @testset "Heating loads and addressable load fraction" begin + # # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, "./scenarios/thermal_load.json") - @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 12904 - - # Hourly fuel load inputs with addressable_load_fraction are served as expected - data = JSON.parsefile("./scenarios/thermal_load.json") - - data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 - data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 - data["ProcessHeatLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.3], 8760) - data["ProcessHeatLoad"]["addressable_load_fraction"] = 0.7 - - s = Scenario(data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, inputs) - @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8 + 0.3 * 0.7) atol = 1.0 - - # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load - data = JSON.parsefile("./scenarios/thermal_load.json") - data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) - data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) - data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) - data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) - data["ProcessHeatLoad"]["monthly_mmbtu"] = repeat([150], 12) - data["ProcessHeatLoad"]["addressable_load_fraction"] = repeat([0.7], 12) - - # Assuming Scenario and REoptInputs are defined functions/classes in your code - s = Scenario(data) - inputs = REoptInputs(s) - - dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency - space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency - process_thermal_load_expected = sum(data["ProcessHeatLoad"]["monthly_mmbtu"] .* data["ProcessHeatLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency - - @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) - @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) - @test round(sum(s.process_heat_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(process_thermal_load_expected) - end + # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 12904 + + # # Hourly fuel load inputs with addressable_load_fraction are served as expected + # data = JSON.parsefile("./scenarios/thermal_load.json") + + # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 + # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + # data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 + # data["ProcessHeatLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.3], 8760) + # data["ProcessHeatLoad"]["addressable_load_fraction"] = 0.7 + + # s = Scenario(data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, inputs) + # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8 + 0.3 * 0.7) atol = 1.0 + + # # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load + # data = JSON.parsefile("./scenarios/thermal_load.json") + # data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) + # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) + # data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) + # data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) + # data["ProcessHeatLoad"]["monthly_mmbtu"] = repeat([150], 12) + # data["ProcessHeatLoad"]["addressable_load_fraction"] = repeat([0.7], 12) + + # # Assuming Scenario and REoptInputs are defined functions/classes in your code + # s = Scenario(data) + # inputs = REoptInputs(s) + + # dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency + # space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency + # process_thermal_load_expected = sum(data["ProcessHeatLoad"]["monthly_mmbtu"] .* data["ProcessHeatLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency + + # @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) + # @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) + # @test round(sum(s.process_heat_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(process_thermal_load_expected) + # end - @testset verbose=true "CHP" begin - @testset "CHP Sizing" begin - # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") - s = Scenario(data_sizing) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt(m, inputs) - - @test round(results["CHP"]["size_kw"], digits=0) ≈ 400.0 atol=50.0 - @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 rtol=1.0e-2 - end + # @testset verbose=true "CHP" begin + # @testset "CHP Sizing" begin + # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + # data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") + # s = Scenario(data_sizing) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # results = run_reopt(m, inputs) + + # @test round(results["CHP"]["size_kw"], digits=0) ≈ 400.0 atol=50.0 + # @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 rtol=1.0e-2 + # end - @testset "CHP Cost Curve and Min Allowable Size" begin - # Fixed size CHP with cost curve, no unavailability_periods - data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") - data_cost_curve["CHP"] = Dict() - data_cost_curve["CHP"]["prime_mover"] = "recip_engine" - data_cost_curve["CHP"]["size_class"] = 1 - data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 - data_cost_curve["CHP"]["min_kw"] = 0 - data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 - data_cost_curve["CHP"]["max_kw"] = 555.51 - data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 - data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] - data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] - - data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 - data_cost_curve["CHP"]["macrs_option_years"] = 0 - data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 - data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 - - expected_x = data_cost_curve["CHP"]["min_allowable_kw"] - cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] - cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] - slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) - init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope - lifecycle_capex_chp_expected = init_capex_chp_expected - - REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) - - #PV - data_cost_curve["PV"] = Dict() - data_cost_curve["PV"]["min_kw"] = 1500 - data_cost_curve["PV"]["max_kw"] = 1500 - data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 - data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 - data_cost_curve["PV"]["macrs_option_years"] = 0 - data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 - data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 - - init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] - lifecycle_capex_pv_expected = init_capex_pv_expected - - REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) - - s = Scenario(data_cost_curve) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) - - init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected - lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected - - init_capex_total = results["Financial"]["initial_capital_costs"] - lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] - - - # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect - @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected - @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected - - # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero - @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 - end + # @testset "CHP Cost Curve and Min Allowable Size" begin + # # Fixed size CHP with cost curve, no unavailability_periods + # data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") + # data_cost_curve["CHP"] = Dict() + # data_cost_curve["CHP"]["prime_mover"] = "recip_engine" + # data_cost_curve["CHP"]["size_class"] = 1 + # data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 + # data_cost_curve["CHP"]["min_kw"] = 0 + # data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 + # data_cost_curve["CHP"]["max_kw"] = 555.51 + # data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 + # data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] + # data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] + + # data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 + # data_cost_curve["CHP"]["macrs_option_years"] = 0 + # data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 + # data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 + + # expected_x = data_cost_curve["CHP"]["min_allowable_kw"] + # cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] + # cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] + # slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) + # init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope + # lifecycle_capex_chp_expected = init_capex_chp_expected - + # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + # [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) + + # #PV + # data_cost_curve["PV"] = Dict() + # data_cost_curve["PV"]["min_kw"] = 1500 + # data_cost_curve["PV"]["max_kw"] = 1500 + # data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 + # data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 + # data_cost_curve["PV"]["macrs_option_years"] = 0 + # data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 + # data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 + + # init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] + # lifecycle_capex_pv_expected = init_capex_pv_expected - + # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + # [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) + + # s = Scenario(data_cost_curve) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) + + # init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected + # lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected + + # init_capex_total = results["Financial"]["initial_capital_costs"] + # lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] + + + # # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect + # @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected + # @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected + + # # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero + # @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 + # end - @testset "CHP Unavailability and Outage" begin - """ - Validation to ensure that: - 1) CHP meets load during outage without exporting - 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) - 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) - 4) CHP min_turn_down_fraction is ignored during an outage - 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage - 6) Unavailability intervals that intersect with grid-outages get ignored - 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production - """ - # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") - - # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period - data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), - ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), - Dict([("month", 1), ("start_week_of_month", 2), - ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] - - # Manually doing the math from the unavailability defined above - unavail_1_start = 24 + 1 - unavail_1_end = unavail_1_start + 8 - 1 - unavail_2_start = 24*3 + 9 - unavail_2_end = unavail_2_start + 8 - 1 + # @testset "CHP Unavailability and Outage" begin + # """ + # Validation to ensure that: + # 1) CHP meets load during outage without exporting + # 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) + # 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) + # 4) CHP min_turn_down_fraction is ignored during an outage + # 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage + # 6) Unavailability intervals that intersect with grid-outages get ignored + # 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production + # """ + # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + # data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") + + # # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period + # data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), + # ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), + # Dict([("month", 1), ("start_week_of_month", 2), + # ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] + + # # Manually doing the math from the unavailability defined above + # unavail_1_start = 24 + 1 + # unavail_1_end = unavail_1_start + 8 - 1 + # unavail_2_start = 24*3 + 9 + # unavail_2_end = unavail_2_start + 8 - 1 - # Specify the CHP.min_turn_down_fraction which is NOT used during an outage - data["CHP"]["min_turn_down_fraction"] = 0.5 - # Specify outage period; outage time_steps are 1-indexed - outage_start = unavail_1_start - data["ElectricUtility"]["outage_start_time_step"] = outage_start - outage_end = unavail_1_end - data["ElectricUtility"]["outage_end_time_step"] = outage_end - data["ElectricLoad"]["critical_load_fraction"] = 0.25 - - s = Scenario(data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) - - tot_elec_load = results["ElectricLoad"]["load_series_kw"] - chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] - chp_to_load = results["CHP"]["electric_to_load_series_kw"] - chp_export = results["CHP"]["electric_to_grid_series_kw"] - cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] - - # The values compared to the expected values - @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 - critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] - @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 - @test sum(chp_export) == 0.0 - @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) - @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 - @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 - end + # # Specify the CHP.min_turn_down_fraction which is NOT used during an outage + # data["CHP"]["min_turn_down_fraction"] = 0.5 + # # Specify outage period; outage time_steps are 1-indexed + # outage_start = unavail_1_start + # data["ElectricUtility"]["outage_start_time_step"] = outage_start + # outage_end = unavail_1_end + # data["ElectricUtility"]["outage_end_time_step"] = outage_end + # data["ElectricLoad"]["critical_load_fraction"] = 0.25 + + # s = Scenario(data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) + + # tot_elec_load = results["ElectricLoad"]["load_series_kw"] + # chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] + # chp_to_load = results["CHP"]["electric_to_load_series_kw"] + # chp_export = results["CHP"]["electric_to_grid_series_kw"] + # cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] + + # # The values compared to the expected values + # @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 + # critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] + # @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 + # @test sum(chp_export) == 0.0 + # @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) + # @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 + # @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 + # end - @testset "CHP Supplementary firing and standby" begin - """ - Test to ensure that supplementary firing and standby charges work as intended. The thermal and - electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a - similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower - value than that of the supplementary firing. The test also ensures that demand charges are - correctly calculated when CHP is and is not allowed to reduce demand charges. - """ - data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") - data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 - data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) - data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - #part 1: supplementary firing not used when less efficient than the boiler and expensive - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(m1, inputs) - @test results["CHP"]["size_kw"] == 800 - @test results["CHP"]["size_supplemental_firing_kw"] == 0 - @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 - @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 - @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 - @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] - @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - - #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP - data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 - data["CHP"]["reduces_demand_charges"] = false - data["ExistingBoiler"]["efficiency"] = 0.85 - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(m2, inputs) - @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 - @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 - @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 - end - - @testset "CHP to Waste Heat" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d = JSON.parsefile("./scenarios/chp_waste.json") - results = run_reopt(m, d) - @test sum(results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"]) ≈ 4174.455 atol=1e-3 - end - end + # @testset "CHP Supplementary firing and standby" begin + # """ + # Test to ensure that supplementary firing and standby charges work as intended. The thermal and + # electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a + # similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower + # value than that of the supplementary firing. The test also ensures that demand charges are + # correctly calculated when CHP is and is not allowed to reduce demand charges. + # """ + # data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") + # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 + # data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) + # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + # #part 1: supplementary firing not used when less efficient than the boiler and expensive + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(m1, inputs) + # @test results["CHP"]["size_kw"] == 800 + # @test results["CHP"]["size_supplemental_firing_kw"] == 0 + # @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 + # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 + # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 + # @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] + # @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + # @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + + # #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP + # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 + # data["CHP"]["reduces_demand_charges"] = false + # data["ExistingBoiler"]["efficiency"] = 0.85 + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(m2, inputs) + # @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 + # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 + # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 + # end + + # @testset "CHP to Waste Heat" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d = JSON.parsefile("./scenarios/chp_waste.json") + # results = run_reopt(m, d) + # @test sum(results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"]) ≈ 4174.455 atol=1e-3 + # end + # end - @testset verbose=true "FlexibleHVAC" begin + # @testset verbose=true "FlexibleHVAC" begin - @testset "Single RC Model heating only" begin - #= - Single RC model: - 1 state/control node - 2 inputs: Ta and Qheat - A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] - NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here + # @testset "Single RC Model heating only" begin + # #= + # Single RC model: + # 1 state/control node + # 2 inputs: Ta and Qheat + # A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] + # NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here - We start with no technologies except ExistingBoiler and ExistingChiller. - FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) - or if there is a time-varying fuel and/or electricity cost - (and the FlexibleHVAC installed_cost is less than the achievable savings). - =# + # We start with no technologies except ExistingBoiler and ExistingChiller. + # FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) + # or if there is a time-varying fuel and/or electricity cost + # (and the FlexibleHVAC installed_cost is less than the achievable savings). + # =# - # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC - pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); - R = 0.00025 # K/kW - C = 1e5 # kJ/K - # the starting scenario has flat fuel and electricty costs - d = JSON.parsefile("./scenarios/thermal_load.json"); - A = reshape([-1/(R*C)], 1,1) - B = [1/(R*C) 1/C] - u = [tamb zeros(8760)]'; - d["FlexibleHVAC"] = Dict( - "control_node" => 1, - "initial_temperatures" => [21], - "temperature_upper_bound_degC" => 22.0, - "temperature_lower_bound_degC" => 19.8, - "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it - "system_matrix" => A, - "input_matrix" => B, - "exogenous_inputs" => u - ) + # # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC + # pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); + # R = 0.00025 # K/kW + # C = 1e5 # kJ/K + # # the starting scenario has flat fuel and electricty costs + # d = JSON.parsefile("./scenarios/thermal_load.json"); + # A = reshape([-1/(R*C)], 1,1) + # B = [1/(R*C) 1/C] + # u = [tamb zeros(8760)]'; + # d["FlexibleHVAC"] = Dict( + # "control_node" => 1, + # "initial_temperatures" => [21], + # "temperature_upper_bound_degC" => 22.0, + # "temperature_lower_bound_degC" => 19.8, + # "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it + # "system_matrix" => A, + # "input_matrix" => B, + # "exogenous_inputs" => u + # ) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # @test r["Financial"]["npv"] == 0 + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # # @test r["Financial"]["npv"] == 0 - # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical - # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC - d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # all of the savings are from the ExistingBoiler fuel costs - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true - # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical + # # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC + # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # all of the savings are from the ExistingBoiler fuel costs + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true + # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased - # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # @test r["Financial"]["npv"] == 0 + # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased + # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # # @test r["Financial"]["npv"] == 0 - # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently - d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") + # # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently + # d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + - # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - - # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - - # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] + # # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + + # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - + # # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - + # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] - # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings - # + 100 and expect that the FlexibleHVAC is not purchased - # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # @test r["Financial"]["npv"] == 0 + # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings + # # + 100 and expect that the FlexibleHVAC is not purchased + # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # # @test r["Financial"]["npv"] == 0 - end - end - - #= - add a time-of-export rate that is greater than retail rate for the month of January, - check to make sure that PV does NOT export unless the site load is met first for the month of January. - =# - @testset "Do not allow_simultaneous_export_import" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/monthly_rate.json") - - # create wholesale_rate with compensation in January > retail rate - jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - data["ElectricTariff"]["wholesale_rate"] = - append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) - - s = Scenario(data) - inputs = REoptInputs(s) - results = run_reopt(model, inputs) - - @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) - if results["PV"]["electric_to_grid_series_kw"][i] > 0) - end - - @testset "Solar and ElectricStorage w/BAU and degradation" begin - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - d = JSON.parsefile("scenarios/pv_storage.json"); - d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) - results = run_reopt([m1,m2], d) - - @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 - @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 - @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 - @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 - @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - - results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) - @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 - - # compare avg soc with and without degradation, - # using default augmentation battery maintenance strategy - avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 - d["ElectricStorage"]["model_degradation"] = true - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r_degr = run_reopt(m, d) - avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 - @test avg_soc_no_degr > avg_soc_degr - - # test the replacement strategy - d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - set_optimizer_attribute(m, "mip_rel_gap", 0.01) - r = run_reopt(m, d) - @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # #optimal SOH at end of horizon is 80\% to prevent any replacement - # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 - # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 - # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match - # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 - # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 - # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 - - # test minimum_avg_soc_fraction - d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - set_optimizer_attribute(m, "mip_rel_gap", 0.01) - r = run_reopt(m, d) - @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 - end - - @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - p = REoptInputs("./scenarios/generator.json") - results = run_reopt([m1,m2], p) - @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - @test results["ElectricLoad"]["bau_critical_load_met"] == false - @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 - - simresults = simulate_outages(results, p) - @test simresults["resilience_hours_max"] == 11 - end - - #candidate for outages - @testset "Minimize Unserved Load" begin - d = JSON.parsefile("./scenarios/outage.json") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt(m, d) + # end + # end + + # #= + # add a time-of-export rate that is greater than retail rate for the month of January, + # check to make sure that PV does NOT export unless the site load is met first for the month of January. + # =# + # @testset "Do not allow_simultaneous_export_import" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/monthly_rate.json") + + # # create wholesale_rate with compensation in January > retail rate + # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + # data["ElectricTariff"]["wholesale_rate"] = + # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + # data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) + + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(model, inputs) + + # @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) + # if results["PV"]["electric_to_grid_series_kw"][i] > 0) + # end + + # @testset "Solar and ElectricStorage w/BAU and degradation" begin + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # d = JSON.parsefile("scenarios/pv_storage.json"); + # d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) + # results = run_reopt([m1,m2], d) + + # @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + # @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 + # @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 + # @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 + # @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 + # @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + # proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - + # results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) + # @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 + + # # compare avg soc with and without degradation, + # # using default augmentation battery maintenance strategy + # avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 + # d["ElectricStorage"]["model_degradation"] = true + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r_degr = run_reopt(m, d) + # avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 + # @test avg_soc_no_degr > avg_soc_degr + + # # test the replacement strategy + # d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # set_optimizer_attribute(m, "mip_rel_gap", 0.01) + # r = run_reopt(m, d) + # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # # #optimal SOH at end of horizon is 80\% to prevent any replacement + # # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 + # # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 + # # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match + # # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 + # # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 + # # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 + + # # test minimum_avg_soc_fraction + # d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # set_optimizer_attribute(m, "mip_rel_gap", 0.01) + # r = run_reopt(m, d) + # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 + # end + + # @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # p = REoptInputs("./scenarios/generator.json") + # results = run_reopt([m1,m2], p) + # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + # @test results["ElectricLoad"]["bau_critical_load_met"] == false + # @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 + + # simresults = simulate_outages(results, p) + # @test simresults["resilience_hours_max"] == 11 + # end + + # #candidate for outages + # @testset "Minimize Unserved Load" begin + # d = JSON.parsefile("./scenarios/outage.json") + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # results = run_reopt(m, d) - @test results["Outages"]["expected_outage_cost"] ≈ 0 atol=0.1 - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 atol=0.1 - @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 - @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - @test value(m[:binMGStorageUsed]) ≈ 1 + # @test results["Outages"]["expected_outage_cost"] ≈ 0 atol=0.1 + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 atol=0.1 + # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + # @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 + # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + # @test value(m[:binMGStorageUsed]) ≈ 1 - # Increase cost of microgrid upgrade and PV Size, PV not used and some load not met - d["Financial"]["microgrid_upgrade_cost_fraction"] = 0.3 - d["PV"]["min_kw"] = 200.0 - d["PV"]["max_kw"] = 200.0 - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt(m, d) - @test value(m[:binMGTechUsed]["PV"]) ≈ 0 - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 24.16 atol=0.1 - - #= - Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 - - should meet 168 kWh in each outage such that the total unserved load is 12 kWh - =# - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 - - # testing dvUnserved load, which would output 100 kWh for this scenario before output fix - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") - @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 - @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) - @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 - - # Scenario with generator, PV, electric storage - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") - @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 - @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 - - # Scenario with generator, PV, wind, electric storage - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") - @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 - @test results["Outages"]["expected_outage_cost"] ≈ 1.296319791276051e6 atol=1.0 - @test results["Financial"]["lcc"] ≈ 4.8046446434e6 rtol=0.001 - - end - - @testset "Outages with Wind and supply-to-load no greater than critical load" begin - input_data = JSON.parsefile("./scenarios/wind_outages.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - results = run_reopt([m1,m2], inputs) + # # Increase cost of microgrid upgrade and PV Size, PV not used and some load not met + # d["Financial"]["microgrid_upgrade_cost_fraction"] = 0.3 + # d["PV"]["min_kw"] = 200.0 + # d["PV"]["max_kw"] = 200.0 + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # results = run_reopt(m, d) + # @test value(m[:binMGTechUsed]["PV"]) ≈ 0 + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 24.16 atol=0.1 + + # #= + # Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 + # - should meet 168 kWh in each outage such that the total unserved load is 12 kWh + # =# + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 + + # # testing dvUnserved load, which would output 100 kWh for this scenario before output fix + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") + # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 + # @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) + # @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 + + # # Scenario with generator, PV, electric storage + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") + # @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 + # @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 + + # # Scenario with generator, PV, wind, electric storage + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") + # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + # @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 + # @test results["Outages"]["expected_outage_cost"] ≈ 1.296319791276051e6 atol=1.0 + # @test results["Financial"]["lcc"] ≈ 4.8046446434e6 rtol=0.001 + + # end + + # @testset "Outages with Wind and supply-to-load no greater than critical load" begin + # input_data = JSON.parsefile("./scenarios/wind_outages.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # results = run_reopt([m1,m2], inputs) - # Check that supply-to-load is equal to critical load during outages, including wind - supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] - supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] - critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] - check = .≈(supply_to_load, critical_load, atol=0.001) - @test !(0 in check) - - # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw - @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) - end - - @testset "Multiple Sites" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - ps = [ - REoptInputs("./scenarios/pv_storage.json"), - REoptInputs("./scenarios/monthly_rate.json"), - ]; - results = run_reopt(m, ps) - @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 - end - - @testset "MPC" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_mpc(model, "./scenarios/mpc.json") - @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] - # the grid draw limit in the 10th time step is set to 90 - # without the 90 limit the grid draw is 98 in the 10th time step - @test grid_draw[10] <= 90 - end - - @testset verbose=true "Rate Structures" begin - - @testset "Tiered Energy" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, "./scenarios/tiered_energy_rate.json") - @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 - @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 - end - - @testset "Lookback Demand Charges" begin - # 1. Testing rate from URDB - data = JSON.parsefile("./scenarios/lookback_rate.json") - # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand - # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month - data["ElectricLoad"]["loads_kw"] = ones(8760) - data["ElectricLoad"]["loads_kw"][8] = 100.0 - inputs = REoptInputs(Scenario(data)) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, inputs) - # Expected result is 100 kW demand for January, 35% of that for all other months and - # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) - - # 2. Testing custom rate from user with demand_lookback_months - d = JSON.parsefile("./scenarios/lookback_rate.json") - d["ElectricTariff"] = Dict() - d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec - d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, REoptInputs(Scenario(d))) - - monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June - expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - # 3. Testing custom rate from user with demand_lookback_range - d = JSON.parsefile("./scenarios/lookback_rate.json") - d["ElectricTariff"] = Dict() - d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - d["ElectricTariff"]["demand_lookback_range"] = 6 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, REoptInputs(Scenario(d))) - - monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] - expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - end - - @testset "Blended tariff" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/no_techs.json") - @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - end - - @testset "Coincident Peak Charges" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, "./scenarios/coincident_peak.json") - @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 - end - - @testset "URDB sell rate" begin - #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) - =# - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - p = REoptInputs("./scenarios/URDB_customer_generation.json") - results = run_reopt(model, p) - @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] - end - - @testset "Custom URDB with Sub-Hourly" begin - # Avoid excessive JuMP warning messages about += with Expressions - logger = SimpleLogger() - with_logger(logger) do - # Testing a 15-min post with a urdb_response with multiple n_energy_tiers - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) - p = REoptInputs("./scenarios/subhourly_with_urdb.json") - results = run_reopt(model, p) - @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 - @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw - end - end - - @testset "Multi-tier demand and energy rates" begin - #This test ensures that when multiple energy or demand regimes are included, that the tier limits load appropriately - d = JSON.parsefile("./scenarios/no_techs.json") - d["ElectricTariff"] = Dict() - d["ElectricTariff"]["urdb_response"] = JSON.parsefile("./scenarios/multi_tier_urdb_response.json") - s = Scenario(d) - p = REoptInputs(s) - @test p.s.electric_tariff.tou_demand_tier_limits[1, 1] ≈ 1.0e8 atol=1.0 - @test p.s.electric_tariff.tou_demand_tier_limits[1, 2] ≈ 1.0e8 atol=1.0 - @test p.s.electric_tariff.tou_demand_tier_limits[2, 1] ≈ 100.0 atol=1.0 - @test p.s.electric_tariff.tou_demand_tier_limits[2, 2] ≈ 1.0e8 atol=1.0 - @test p.s.electric_tariff.energy_tier_limits[1, 1] ≈ 1.0e10 atol=1.0 - @test p.s.electric_tariff.energy_tier_limits[1, 2] ≈ 1.0e10 atol=1.0 - @test p.s.electric_tariff.energy_tier_limits[6, 1] ≈ 20000.0 atol=1.0 - @test p.s.electric_tariff.energy_tier_limits[6, 2] ≈ 1.0e10 atol=1.0 - end - - @testset "Tiered TOU Demand" begin - data = JSON.parsefile("./scenarios/tiered_tou_demand.json") - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(model, data) - max_demand = data["ElectricLoad"]["annual_kwh"] / 8760 - tier1_max = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["max"] - tier1_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["rate"] - tier2_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][2]["rate"] - expected_demand_charges = 12 * (tier1_max * tier1_rate + (max_demand - tier1_max) * tier2_rate) - @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_charges atol=1 - end - - # # tiered monthly demand rate TODO: expected results? - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/tiered_energy_rate.json") - # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(m, inputs) - - @testset "Non-Standard Units for Energy Rates" begin - d = JSON.parsefile("./scenarios/no_techs.json") - d["ElectricTariff"] = Dict( - "urdb_label" => "6272e4ae7eb76766c247d469" - ) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, d) - @test occursin("URDB energy tiers have non-standard units of", string(results["Messages"])) - end - - end - - @testset "EASIUR" begin - d = JSON.parsefile("./scenarios/pv.json") - d["Site"]["latitude"] = 30.2672 - d["Site"]["longitude"] = -97.7431 - scen = Scenario(d) - @test scen.financial.NOx_grid_cost_per_tonne ≈ 5510.61 atol=0.1 - end - - @testset "Wind" begin - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d = JSON.parsefile("./scenarios/wind.json") - results = run_reopt(m, d) - @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 - @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 - #= - 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 - - both have zero curtailment - - same energy to grid: 5,839,317 vs 5,839,322 - - same energy to load: 4,160,683 vs 4,160,677 - - same city: Boulder - - same total wind prod factor - - REopt.jl has: - - bigger turbine: 3752 vs 3735 - - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 - - TODO: will these discrepancies be addressed once NMIL binaries are added? - =# - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) - results = run_reopt(m, d) - @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error - results = run_reopt(m, d) - @test "errors" ∈ keys(results["Messages"]) - @test length(results["Messages"]["errors"]) > 0 - end - - @testset "Multiple PVs" begin - logger = SimpleLogger() - with_logger(logger) do - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") - - ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] - roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] - roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] - - @test ground_pv["size_kw"] ≈ 15 atol=0.1 - @test roof_west["size_kw"] ≈ 7 atol=0.1 - @test roof_east["size_kw"] ≈ 4 atol=0.1 - @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 - @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 - @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 - @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 - @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 - end - end - - #candidate for hot and cold stor - @testset "Thermal Energy Storage + Absorption Chiller" begin - model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - data = JSON.parsefile("./scenarios/thermal_storage.json") - s = Scenario(data) - p = REoptInputs(s) + # # Check that supply-to-load is equal to critical load during outages, including wind + # supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] + # supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] + # critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] + # check = .≈(supply_to_load, critical_load, atol=0.001) + # @test !(0 in check) + + # # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw + # @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) + # end + + # @testset "Multiple Sites" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # ps = [ + # REoptInputs("./scenarios/pv_storage.json"), + # REoptInputs("./scenarios/monthly_rate.json"), + # ]; + # results = run_reopt(m, ps) + # @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 + # end + + # @testset "MPC" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_mpc(model, "./scenarios/mpc.json") + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + # grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] + # # the grid draw limit in the 10th time step is set to 90 + # # without the 90 limit the grid draw is 98 in the 10th time step + # @test grid_draw[10] <= 90 + # end + + # @testset verbose=true "Rate Structures" begin + + # @testset "Tiered Energy" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, "./scenarios/tiered_energy_rate.json") + # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 + # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 + # end + + # @testset "Lookback Demand Charges" begin + # # 1. Testing rate from URDB + # data = JSON.parsefile("./scenarios/lookback_rate.json") + # # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand + # # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month + # data["ElectricLoad"]["loads_kw"] = ones(8760) + # data["ElectricLoad"]["loads_kw"][8] = 100.0 + # inputs = REoptInputs(Scenario(data)) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, inputs) + # # Expected result is 100 kW demand for January, 35% of that for all other months and + # # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) + + # # 2. Testing custom rate from user with demand_lookback_months + # d = JSON.parsefile("./scenarios/lookback_rate.json") + # d["ElectricTariff"] = Dict() + # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + # d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec + # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, REoptInputs(Scenario(d))) + + # monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June + # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + # # 3. Testing custom rate from user with demand_lookback_range + # d = JSON.parsefile("./scenarios/lookback_rate.json") + # d["ElectricTariff"] = Dict() + # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + # d["ElectricTariff"]["demand_lookback_range"] = 6 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, REoptInputs(Scenario(d))) + + # monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] + # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + # end + + # @testset "Blended tariff" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/no_techs.json") + # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + # end + + # @testset "Coincident Peak Charges" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, "./scenarios/coincident_peak.json") + # @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 + # end + + # @testset "URDB sell rate" begin + # #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) + # =# + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # p = REoptInputs("./scenarios/URDB_customer_generation.json") + # results = run_reopt(model, p) + # @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] + # end + + # @testset "Custom URDB with Sub-Hourly" begin + # # Avoid excessive JuMP warning messages about += with Expressions + # logger = SimpleLogger() + # with_logger(logger) do + # # Testing a 15-min post with a urdb_response with multiple n_energy_tiers + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) + # p = REoptInputs("./scenarios/subhourly_with_urdb.json") + # results = run_reopt(model, p) + # @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 + # @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw + # end + # end + + # @testset "Multi-tier demand and energy rates" begin + # #This test ensures that when multiple energy or demand regimes are included, that the tier limits load appropriately + # d = JSON.parsefile("./scenarios/no_techs.json") + # d["ElectricTariff"] = Dict() + # d["ElectricTariff"]["urdb_response"] = JSON.parsefile("./scenarios/multi_tier_urdb_response.json") + # s = Scenario(d) + # p = REoptInputs(s) + # @test p.s.electric_tariff.tou_demand_tier_limits[1, 1] ≈ 1.0e8 atol=1.0 + # @test p.s.electric_tariff.tou_demand_tier_limits[1, 2] ≈ 1.0e8 atol=1.0 + # @test p.s.electric_tariff.tou_demand_tier_limits[2, 1] ≈ 100.0 atol=1.0 + # @test p.s.electric_tariff.tou_demand_tier_limits[2, 2] ≈ 1.0e8 atol=1.0 + # @test p.s.electric_tariff.energy_tier_limits[1, 1] ≈ 1.0e10 atol=1.0 + # @test p.s.electric_tariff.energy_tier_limits[1, 2] ≈ 1.0e10 atol=1.0 + # @test p.s.electric_tariff.energy_tier_limits[6, 1] ≈ 20000.0 atol=1.0 + # @test p.s.electric_tariff.energy_tier_limits[6, 2] ≈ 1.0e10 atol=1.0 + # end + + # @testset "Tiered TOU Demand" begin + # data = JSON.parsefile("./scenarios/tiered_tou_demand.json") + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(model, data) + # max_demand = data["ElectricLoad"]["annual_kwh"] / 8760 + # tier1_max = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["max"] + # tier1_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["rate"] + # tier2_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][2]["rate"] + # expected_demand_charges = 12 * (tier1_max * tier1_rate + (max_demand - tier1_max) * tier2_rate) + # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_charges atol=1 + # end + + # # # tiered monthly demand rate TODO: expected results? + # # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # # data = JSON.parsefile("./scenarios/tiered_energy_rate.json") + # # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" + # # s = Scenario(data) + # # inputs = REoptInputs(s) + # # results = run_reopt(m, inputs) + + # @testset "Non-Standard Units for Energy Rates" begin + # d = JSON.parsefile("./scenarios/no_techs.json") + # d["ElectricTariff"] = Dict( + # "urdb_label" => "6272e4ae7eb76766c247d469" + # ) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, d) + # @test occursin("URDB energy tiers have non-standard units of", string(results["Messages"])) + # end + + # end + + # @testset "EASIUR" begin + # d = JSON.parsefile("./scenarios/pv.json") + # d["Site"]["latitude"] = 30.2672 + # d["Site"]["longitude"] = -97.7431 + # scen = Scenario(d) + # @test scen.financial.NOx_grid_cost_per_tonne ≈ 5510.61 atol=0.1 + # end + + # @testset "Wind" begin + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d = JSON.parsefile("./scenarios/wind.json") + # results = run_reopt(m, d) + # @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 + # @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 + # #= + # 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 + # - both have zero curtailment + # - same energy to grid: 5,839,317 vs 5,839,322 + # - same energy to load: 4,160,683 vs 4,160,677 + # - same city: Boulder + # - same total wind prod factor + + # REopt.jl has: + # - bigger turbine: 3752 vs 3735 + # - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 + + # TODO: will these discrepancies be addressed once NMIL binaries are added? + # =# + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) + # results = run_reopt(m, d) + # @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error + # results = run_reopt(m, d) + # @test "errors" ∈ keys(results["Messages"]) + # @test length(results["Messages"]["errors"]) > 0 + # end + + # @testset "Multiple PVs" begin + # logger = SimpleLogger() + # with_logger(logger) do + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") + + # ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] + # roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] + # roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] + + # @test ground_pv["size_kw"] ≈ 15 atol=0.1 + # @test roof_west["size_kw"] ≈ 7 atol=0.1 + # @test roof_east["size_kw"] ≈ 4 atol=0.1 + # @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + # @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + # @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 + # @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 + # @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 + # @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 + # @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 + # end + # end + + # #candidate for hot and cold stor + # @testset "Thermal Energy Storage + Absorption Chiller" begin + # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/thermal_storage.json") + # s = Scenario(data) + # p = REoptInputs(s) - #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. - htf_defaults_response = get_absorption_chiller_defaults(; - thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), - boiler_type=get(data["ExistingBoiler"], "production_type", nothing), - load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - ) - - expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] - expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] - - @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 - - #load test values - p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR - p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR - p.s.absorption_chiller.cop_thermal = 0.7 - - #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period - for ts in p.time_steps - #heating and cooling loads only - if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge - p.s.electric_load.loads_kw[ts] = 10 - p.s.dhw_load.loads_kw[ts] = 5 - p.s.space_heating_load.loads_kw[ts] = 5 - p.s.cooling_load.loads_kw_thermal[ts] = 10 - p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 - for tier in 1:p.s.electric_tariff.n_energy_tiers - p.s.electric_tariff.energy_rates[ts, tier] = 100 - end - else #in odd periods, there is no load and energy is cheaper - storage should charge - p.s.electric_load.loads_kw[ts] = 0 - p.s.dhw_load.loads_kw[ts] = 0 - p.s.space_heating_load.loads_kw[ts] = 0 - p.s.cooling_load.loads_kw_thermal[ts] = 0 - p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 - for tier in 1:p.s.electric_tariff.n_energy_tiers - p.s.electric_tariff.energy_rates[ts, tier] = 50 - end - end - end - - r = run_reopt(model, p) - - #dispatch to load should be 10kW every other period = 4,380 * 10 - @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 - @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 - #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC - @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 - @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 - #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. - @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 - @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 - @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 - end - - #condidate for hot/cold stor - @testset "Heat and cool energy balance" begin - """ - - This is an "energy balance" type of test which tests the model formulation/math as opposed - to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting - - Validation to ensure that: - 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage - 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load - 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly - - """ - input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) - - # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load - # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load - crb_cop = REopt.get_existing_chiller_default_cop(; - existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, - max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) - cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) - cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR - - #Test cooling load results - @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 - - # Convert fuel input to thermal using user input boiler efficiency - boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) - boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency - - # Cooling outputs - cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] - cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] - cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] - cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] - cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) - cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) - cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] - cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) - tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total - cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total - cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses - - # Absorption Chiller electric consumption addition - absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series - absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) - absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] - absorpchl_cop_elec = s.absorption_chiller.cop_electric - - # Check if sum of electric and absorption chillers equals cooling thermal total - @test tes_effic_with_decay < 0.97 - @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 - @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 - - # Heating outputs - boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] - boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] - boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] - chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] - chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] - chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] - absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) - total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) - tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) - total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) - boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency - total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows - - @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 - @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 - - # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) - absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU - absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR - absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh - - @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 - end - - @testset "Heating and cooling inputs + CHP defaults" begin - """ - - This tests the various ways to input heating and cooling loads to make sure they are processed correctly. - There are no "new" technologies in this test, so heating is served by ExistingBoiler, and - cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. - - """ - input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") - s = Scenario(input_data) - inputs = REoptInputs(s) - - # Heating load is input as **fuel**, not thermal - # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal - expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] - total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 - total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 - # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal - input_data["ExistingBoiler"]["efficiency"] = 0.72 - s = Scenario(input_data) - inputs = REoptInputs(s) - total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 - total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 - - # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input - # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile - total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop - @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** - - #Test CHP defaults use average fuel load, size class 2 for recip_engine - @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 - @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 - - delete!(input_data, "SpaceHeatingLoad") - delete!(input_data, "DomesticHotWaterLoad") - annual_fraction_of_electric_load_input = 0.5 - input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) - - s = Scenario(input_data) - inputs = REoptInputs(s) - - expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input - total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 - @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 - - # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load - @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 - - input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) - - s = Scenario(input_data) - inputs = REoptInputs(s) - - #Test CHP defaults use average fuel load, size class changes to 3 - @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 - @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 - #Update CHP prime_mover and test new defaults - input_data["CHP"]["prime_mover"] = "combustion_turbine" - input_data["CHP"]["size_class"] = 1 - # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw - input_data["CHP"]["max_kw"] = 2500.0 - - s = Scenario(input_data) - inputs = REoptInputs(s) - - @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 - @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 - - total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 - total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 - - input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) - - s = Scenario(input_data) - inputs = REoptInputs(s) - - total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 - @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 - - # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied - input_data["SpaceHeatingLoad"] = Dict{Any, Any}() - input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() - annual_tonhour = 25000.0 - input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", - "annual_tonhour" => annual_tonhour) - input_data["ExistingChiller"] = Dict{Any, Any}() - - s = Scenario(input_data) - inputs = REoptInputs(s) - - @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 - - # Test for prime generator CHP inputs (electric only) - # First get CHP cost to compare later with prime generator - input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", - "annual_kwh" => 876000) - input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, - "blended_annual_demand_rate" => 0.0 ) - s_chp = Scenario(input_data) - inputs_chp = REoptInputs(s) - installed_cost_chp = s_chp.chp.installed_cost_per_kw - - # Now get prime generator (electric only) - input_data["CHP"]["is_electric_only"] = true - delete!(input_data["CHP"], "max_kw") - s = Scenario(input_data) - inputs = REoptInputs(s) - # Costs are 75% of CHP - @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 - @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 - @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 - # Thermal efficiency set to zero - @test inputs.s.chp.thermal_efficiency_full_load == 0 - @test inputs.s.chp.thermal_efficiency_half_load == 0 - # Max size based on electric load, not heating load - @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 - end - - @testset "Hybrid/blended heating and cooling loads" begin - """ - - This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names - and blended_doe_reference_percents are given and blended to create an aggregate load profile - - """ - input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") - - hospital_fraction = 0.75 - hotel_fraction = 1.0 - hospital_fraction - - # Hospital only - input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 - input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 - input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" - input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" - - s = Scenario(input_data) - inputs = REoptInputs(s) - - elec_hospital = inputs.s.electric_load.loads_kw - space_hospital = inputs.s.space_heating_load.loads_kw # thermal - dhw_hospital = inputs.s.dhw_load.loads_kw # thermal - cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal - cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital - - # Hotel only - input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 - input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 - input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" - input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" - - s = Scenario(input_data) - inputs = REoptInputs(s) - - elec_hotel = inputs.s.electric_load.loads_kw - space_hotel = inputs.s.space_heating_load.loads_kw # thermal - dhw_hotel = inputs.s.dhw_load.loads_kw # thermal - cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal - cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel - - # Hybrid mix of hospital and hotel - # Remove previous assignment of doe_reference_name - for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] - delete!(input_data[load], "doe_reference_name") - end - annual_energy = (hospital_fraction + hotel_fraction) * 100 - building_list = ["Hospital", "LargeHotel"] - percent_share_list = [hospital_fraction, hotel_fraction] - input_data["ElectricLoad"]["annual_kwh"] = annual_energy - input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list - input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list - - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy - input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list - input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy - input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list - input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list - - # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided - input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list - input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list - - s = Scenario(input_data) - inputs = REoptInputs(s) - - elec_hybrid = inputs.s.electric_load.loads_kw - space_hybrid = inputs.s.space_heating_load.loads_kw # thermal - dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal - cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal - cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric - cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid - - # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step - - @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 - @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 - @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 - # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles - cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ - cooling_elec_frac_of_total_hotel * hotel_fraction) - @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 - end - - @testset "Boiler (new) test" begin - input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], inputs) - - # BAU boiler loads - load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU - existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) - - # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only - # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year - @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 - @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 - end - - @testset "OffGrid" begin - ## Scenario 1: Solar, Storage, Fixed Generator - post_name = "off_grid.json" - post = JSON.parsefile("./scenarios/$post_name") - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - scen = Scenario(post) - r = run_reopt(m, scen) - - # Test default values - @test scen.electric_utility.outage_start_time_step ≈ 1 - @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour - @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 - @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false - @test scen.generator.fuel_avail_gal ≈ 1.0e9 - @test scen.generator.min_turn_down_fraction ≈ 0.15 - @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw - @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 - - # Test outputs - @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid - @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc - @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required - @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - @test r["PV"]["size_kw"] ≈ 5050.0 - f = r["Financial"] - @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - - ## Scenario 2: Fixed Generator only - post["ElectricLoad"]["annual_kwh"] = 100.0 - post["PV"]["max_kw"] = 0.0 - post["ElectricStorage"]["max_kw"] = 0.0 - post["Generator"]["min_turn_down_fraction"] = 0.0 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, post) - - # Test generator outputs - @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh - @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 - @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 - @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 - @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) - @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible - @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 - @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 - @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 - - ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement - ## This test ensures the load operating reserve requirement is being enforced - post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" - post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen - post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity - post["PV"]["max_kw"] = 0.0 - post["ElectricStorage"]["max_kw"] = 0.0 - post["Generator"]["min_turn_down_fraction"] = 0.0 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, post) - - # Test generator outputs - @test typeof(r) == Model # this is true when the model is infeasible - - ### Scenario 3: Indonesia. Wind (custom prod) and Generator only - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - post_name = "wind_intl_offgrid.json" - post = JSON.parsefile("./scenarios/$post_name") - post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] - scen = Scenario(post) - post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) - - results = run_reopt(m, post) - - @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - f = results["Financial"] - @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - - windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) - loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) - @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 - - end - - @testset "GHP" begin - """ - - This tests multiple unique aspects of GHP: - 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one - 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load - 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW - 4. GHP serves all the Cooling load - 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly - 6. Hybrid GHP capability functions as expected - - """ - # Load base inputs - input_data = JSON.parsefile("scenarios/ghp_inputs.json") - - # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl - # Heat pump performance maps - cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) - data = cop_map_mat_header[1] - headers = cop_map_mat_header[2] - # Generate a "records" style dictionary from the - cop_map_list = [] - for i in axes(data,1) - dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) - push!(cop_map_list, dict_record) - end - input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list - - # Due to GhpGhx not being a registered package (no OSI-approved license), - # the registered REopt package cannot have GhpGhx as a "normal" dependency; - # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an - # input to REopt to avoid GhpGhx module calls - response_1 = JSON.parsefile("scenarios/ghpghx_response.json") - response_2 = deepcopy(response_1) - # Reduce the electric consumption of response 2 which should then be the chosen system - response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 - input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] - - # Heating load - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) - input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 - input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 - - # Call REopt - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt([m1,m2], inputs) - - ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - @test ghp_option_chosen == 2 - - # Test GHP heating and cooling load reduced - hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) - cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) - @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 - @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 - - # Test GHP serving space heating with VAV thermal efficiency improvements - heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) - expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) - @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 - - # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit - boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) - @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 - - # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production - bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) - @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 - - # Custom heat pump COP map is used properly - ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] - cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] - # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map - @test heating_cop_avg <= 4.0 - @test cooling_cop_avg <= 8.0 - end - - @testset "Hybrid GHX and GHP calculated costs validation" begin - ## Hybrid GHP validation. - # Load base inputs - input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") - - inputs = REoptInputs(input_data) - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt([m1,m2], inputs) - - calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* - input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* - inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + - (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* - input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* - inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + - (inputs.s.ghp_option_list[1].building_sqft* - inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) - - @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 - - calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* - inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om - - @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 - - calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) - @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 - - @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 - - @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 - - @test abs(results["Financial"]["npv"] - 840621) < 1.0 - @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 - @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 - - @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") - - ## Hybrid - input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] - input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 - input_data["GHP"]["ghx_useful_life_years"] = 35 - - inputs = REoptInputs(input_data) - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt([m1,m2], inputs) - - pop!(input_data["GHP"], "ghpghx_inputs", nothing) - pop!(input_data["GHP"], "ghpghx_responses", nothing) - ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) - - calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* - ( - (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years - )/( - (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years - ) - - @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 - @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true - - # Test centralized GHP cost calculations - input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") - response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") - input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] - - s_wwhp = Scenario(input_data_wwhp) - inputs_wwhp = REoptInputs(s_wwhp) - m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results_wwhp = run_reopt(m3, inputs_wwhp) - - - heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * - input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] - - cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * - input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] - - ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * - results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] - - # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount - capex_reduction_factor = 0.455005797 - - calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) - - reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] - @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 - end - - @testset "Cambium Emissions" begin - """ - 1) Location in contiguous US - - Correct data from Cambium (returned location and values) - - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) - - co2 pct increase should be zero - 2) HI and AK locations - - Should use AVERT data and give an "info" message - - Adjust for load year vs. AVERT year - - co2 pct increase should be the default value unless user provided value - 3) International - - all emissions should be zero unless provided - """ - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. + # htf_defaults_response = get_absorption_chiller_defaults(; + # thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), + # boiler_type=get(data["ExistingBoiler"], "production_type", nothing), + # load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + # ) + + # expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] + # expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] + + # @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + # @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + # @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 + + # #load test values + # p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR + # p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR + # p.s.absorption_chiller.cop_thermal = 0.7 + + # #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period + # for ts in p.time_steps + # #heating and cooling loads only + # if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge + # p.s.electric_load.loads_kw[ts] = 10 + # p.s.dhw_load.loads_kw[ts] = 5 + # p.s.space_heating_load.loads_kw[ts] = 5 + # p.s.cooling_load.loads_kw_thermal[ts] = 10 + # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 + # for tier in 1:p.s.electric_tariff.n_energy_tiers + # p.s.electric_tariff.energy_rates[ts, tier] = 100 + # end + # else #in odd periods, there is no load and energy is cheaper - storage should charge + # p.s.electric_load.loads_kw[ts] = 0 + # p.s.dhw_load.loads_kw[ts] = 0 + # p.s.space_heating_load.loads_kw[ts] = 0 + # p.s.cooling_load.loads_kw_thermal[ts] = 0 + # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 + # for tier in 1:p.s.electric_tariff.n_energy_tiers + # p.s.electric_tariff.energy_rates[ts, tier] = 50 + # end + # end + # end + + # r = run_reopt(model, p) + + # #dispatch to load should be 10kW every other period = 4,380 * 10 + # @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 + # @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 + # #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC + # @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 + # @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 + # #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. + # @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 + # @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 + # @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 + # end + + # #condidate for hot/cold stor + # @testset "Heat and cool energy balance" begin + # """ + + # This is an "energy balance" type of test which tests the model formulation/math as opposed + # to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting + + # Validation to ensure that: + # 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage + # 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load + # 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly + + # """ + # input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) + + # # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load + # # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load + # crb_cop = REopt.get_existing_chiller_default_cop(; + # existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, + # max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) + # cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) + # cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR + + # #Test cooling load results + # @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 + + # # Convert fuel input to thermal using user input boiler efficiency + # boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) + # boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency + + # # Cooling outputs + # cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] + # cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] + # cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] + # cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] + # cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) + # cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) + # cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] + # cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) + # tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total + # cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total + # cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses + + # # Absorption Chiller electric consumption addition + # absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series + # absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) + # absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] + # absorpchl_cop_elec = s.absorption_chiller.cop_electric + + # # Check if sum of electric and absorption chillers equals cooling thermal total + # @test tes_effic_with_decay < 0.97 + # @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 + # @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 + + # # Heating outputs + # boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + # boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] + # boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + # boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] + # chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] + # chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] + # chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] + # absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + # hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + # tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) + # total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) + # tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) + # total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) + # boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency + # total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows + + # @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 + # @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 + + # # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) + # absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU + # absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR + # absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh + + # @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 + # end + + # @testset "Heating and cooling inputs + CHP defaults" begin + # """ + + # This tests the various ways to input heating and cooling loads to make sure they are processed correctly. + # There are no "new" technologies in this test, so heating is served by ExistingBoiler, and + # cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. + + # """ + # input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # # Heating load is input as **fuel**, not thermal + # # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal + # expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] + # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 + # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 + # # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal + # input_data["ExistingBoiler"]["efficiency"] = 0.72 + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 + # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 + + # # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input + # # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile + # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop + # @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** + + # #Test CHP defaults use average fuel load, size class 2 for recip_engine + # @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 + # @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 + + # delete!(input_data, "SpaceHeatingLoad") + # delete!(input_data, "DomesticHotWaterLoad") + # annual_fraction_of_electric_load_input = 0.5 + # input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input + # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + # @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 + # @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 + + # # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load + # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 + + # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + # input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # #Test CHP defaults use average fuel load, size class changes to 3 + # @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 + # @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 + # #Update CHP prime_mover and test new defaults + # input_data["CHP"]["prime_mover"] = "combustion_turbine" + # input_data["CHP"]["size_class"] = 1 + # # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw + # input_data["CHP"]["max_kw"] = 2500.0 + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 + # @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 + + # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 + # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + # @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 + + # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + # input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 + # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 + + # # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied + # input_data["SpaceHeatingLoad"] = Dict{Any, Any}() + # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() + # annual_tonhour = 25000.0 + # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", + # "annual_tonhour" => annual_tonhour) + # input_data["ExistingChiller"] = Dict{Any, Any}() + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 + + # # Test for prime generator CHP inputs (electric only) + # # First get CHP cost to compare later with prime generator + # input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", + # "annual_kwh" => 876000) + # input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, + # "blended_annual_demand_rate" => 0.0 ) + # s_chp = Scenario(input_data) + # inputs_chp = REoptInputs(s) + # installed_cost_chp = s_chp.chp.installed_cost_per_kw + + # # Now get prime generator (electric only) + # input_data["CHP"]["is_electric_only"] = true + # delete!(input_data["CHP"], "max_kw") + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # # Costs are 75% of CHP + # @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 + # @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 + # @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 + # # Thermal efficiency set to zero + # @test inputs.s.chp.thermal_efficiency_full_load == 0 + # @test inputs.s.chp.thermal_efficiency_half_load == 0 + # # Max size based on electric load, not heating load + # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 + # end + + # @testset "Hybrid/blended heating and cooling loads" begin + # """ + + # This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names + # and blended_doe_reference_percents are given and blended to create an aggregate load profile + + # """ + # input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") + + # hospital_fraction = 0.75 + # hotel_fraction = 1.0 - hospital_fraction + + # # Hospital only + # input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 + # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 + # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" + # input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # elec_hospital = inputs.s.electric_load.loads_kw + # space_hospital = inputs.s.space_heating_load.loads_kw # thermal + # dhw_hospital = inputs.s.dhw_load.loads_kw # thermal + # cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal + # cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital + + # # Hotel only + # input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 + # input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 + # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" + # input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # elec_hotel = inputs.s.electric_load.loads_kw + # space_hotel = inputs.s.space_heating_load.loads_kw # thermal + # dhw_hotel = inputs.s.dhw_load.loads_kw # thermal + # cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal + # cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel + + # # Hybrid mix of hospital and hotel + # # Remove previous assignment of doe_reference_name + # for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] + # delete!(input_data[load], "doe_reference_name") + # end + # annual_energy = (hospital_fraction + hotel_fraction) * 100 + # building_list = ["Hospital", "LargeHotel"] + # percent_share_list = [hospital_fraction, hotel_fraction] + # input_data["ElectricLoad"]["annual_kwh"] = annual_energy + # input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list + # input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list + + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy + # input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list + # input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy + # input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list + # input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list + + # # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided + # input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list + # input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + + # elec_hybrid = inputs.s.electric_load.loads_kw + # space_hybrid = inputs.s.space_heating_load.loads_kw # thermal + # dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal + # cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal + # cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric + # cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid + + # # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step + + # @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 + # @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 + # @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 + # # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles + # cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ + # cooling_elec_frac_of_total_hotel * hotel_fraction) + # @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 + # end + + # @testset "Boiler (new) test" begin + # input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], inputs) + + # # BAU boiler loads + # load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU + # existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + # boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) + + # # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only + # # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year + # @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 + # @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 + # end + + # @testset "OffGrid" begin + # ## Scenario 1: Solar, Storage, Fixed Generator + # post_name = "off_grid.json" + # post = JSON.parsefile("./scenarios/$post_name") + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # scen = Scenario(post) + # r = run_reopt(m, scen) + + # # Test default values + # @test scen.electric_utility.outage_start_time_step ≈ 1 + # @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour + # @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 + # @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false + # @test scen.generator.fuel_avail_gal ≈ 1.0e9 + # @test scen.generator.min_turn_down_fraction ≈ 0.15 + # @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw + # @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 + + # # Test outputs + # @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid + # @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc + # @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required + # @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + # @test r["PV"]["size_kw"] ≈ 5050.0 + # f = r["Financial"] + # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + + # ## Scenario 2: Fixed Generator only + # post["ElectricLoad"]["annual_kwh"] = 100.0 + # post["PV"]["max_kw"] = 0.0 + # post["ElectricStorage"]["max_kw"] = 0.0 + # post["Generator"]["min_turn_down_fraction"] = 0.0 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, post) + + # # Test generator outputs + # @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh + # @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 + # @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 + # @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 + # @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) + # @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible + # @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 + # @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 + # @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 + + # ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement + # ## This test ensures the load operating reserve requirement is being enforced + # post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" + # post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen + # post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity + # post["PV"]["max_kw"] = 0.0 + # post["ElectricStorage"]["max_kw"] = 0.0 + # post["Generator"]["min_turn_down_fraction"] = 0.0 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, post) + + # # Test generator outputs + # @test typeof(r) == Model # this is true when the model is infeasible + + # ### Scenario 3: Indonesia. Wind (custom prod) and Generator only + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + # post_name = "wind_intl_offgrid.json" + # post = JSON.parsefile("./scenarios/$post_name") + # post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] + # scen = Scenario(post) + # post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) + + # results = run_reopt(m, post) + + # @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + # f = results["Financial"] + # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + + # windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) + # loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) + # @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 + + # end + + # @testset "GHP" begin + # """ + + # This tests multiple unique aspects of GHP: + # 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one + # 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load + # 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW + # 4. GHP serves all the Cooling load + # 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly + # 6. Hybrid GHP capability functions as expected + + # """ + # # Load base inputs + # input_data = JSON.parsefile("scenarios/ghp_inputs.json") + + # # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl + # # Heat pump performance maps + # cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) + # data = cop_map_mat_header[1] + # headers = cop_map_mat_header[2] + # # Generate a "records" style dictionary from the + # cop_map_list = [] + # for i in axes(data,1) + # dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) + # push!(cop_map_list, dict_record) + # end + # input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list + + # # Due to GhpGhx not being a registered package (no OSI-approved license), + # # the registered REopt package cannot have GhpGhx as a "normal" dependency; + # # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an + # # input to REopt to avoid GhpGhx module calls + # response_1 = JSON.parsefile("scenarios/ghpghx_response.json") + # response_2 = deepcopy(response_1) + # # Reduce the electric consumption of response 2 which should then be the chosen system + # response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 + # input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] + + # # Heating load + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + # input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) + # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 + # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 + + # # Call REopt + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt([m1,m2], inputs) + + # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + # @test ghp_option_chosen == 2 + + # # Test GHP heating and cooling load reduced + # hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) + # cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) + # @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 + # @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 + + # # Test GHP serving space heating with VAV thermal efficiency improvements + # heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) + # expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) + # @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 + + # # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit + # boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + # expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) + # @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 + + # # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production + # bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + # ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) + # @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 + + # # Custom heat pump COP map is used properly + # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + # heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] + # cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] + # # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map + # @test heating_cop_avg <= 4.0 + # @test cooling_cop_avg <= 8.0 + # end + + # @testset "Hybrid GHX and GHP calculated costs validation" begin + # ## Hybrid GHP validation. + # # Load base inputs + # input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") + + # inputs = REoptInputs(input_data) + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt([m1,m2], inputs) + + # calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* + # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* + # inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + + # (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* + # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* + # inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + + # (inputs.s.ghp_option_list[1].building_sqft* + # inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) + + # @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 + + # calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* + # inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om + + # @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 + + # calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) + # @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 + + # @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 + + # @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 + + # @test abs(results["Financial"]["npv"] - 840621) < 1.0 + # @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 + # @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 + + # @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") + + # ## Hybrid + # input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] + # input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 + # input_data["GHP"]["ghx_useful_life_years"] = 35 + + # inputs = REoptInputs(input_data) + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt([m1,m2], inputs) + + # pop!(input_data["GHP"], "ghpghx_inputs", nothing) + # pop!(input_data["GHP"], "ghpghx_responses", nothing) + # ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) + + # calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* + # ( + # (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years + # )/( + # (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years + # ) + + # @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 + # @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true + + # # Test centralized GHP cost calculations + # input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") + # response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") + # input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] + + # s_wwhp = Scenario(input_data_wwhp) + # inputs_wwhp = REoptInputs(s_wwhp) + # m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results_wwhp = run_reopt(m3, inputs_wwhp) + + + # heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * + # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] + + # cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * + # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] + + # ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * + # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] + + # # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount + # capex_reduction_factor = 0.455005797 + + # calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) + + # reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] + # @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 + # end + + # @testset "Cambium Emissions" begin + # """ + # 1) Location in contiguous US + # - Correct data from Cambium (returned location and values) + # - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) + # - co2 pct increase should be zero + # 2) HI and AK locations + # - Should use AVERT data and give an "info" message + # - Adjust for load year vs. AVERT year + # - co2 pct increase should be the default value unless user provided value + # 3) International + # - all emissions should be zero unless provided + # """ + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - post_name = "cambium.json" - post = JSON.parsefile("./scenarios/$post_name") + # post_name = "cambium.json" + # post = JSON.parsefile("./scenarios/$post_name") - cities = Dict( - "Denver" => (39.7413753050447, -104.99965032911328), - "Fairbanks" => (64.84053664406181, -147.71913656313163), - "Santiago" => (-33.44485437650408, -70.69031905547853) - ) + # cities = Dict( + # "Denver" => (39.7413753050447, -104.99965032911328), + # "Fairbanks" => (64.84053664406181, -147.71913656313163), + # "Santiago" => (-33.44485437650408, -70.69031905547853) + # ) - # 1) Location in contiguous US - city = "Denver" - post["Site"]["latitude"] = cities[city][1] - post["Site"]["longitude"] = cities[city][2] - post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] - post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri - scen = Scenario(post) + # # 1) Location in contiguous US + # city = "Denver" + # post["Site"]["latitude"] = cities[city][1] + # post["Site"]["longitude"] = cities[city][2] + # post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] + # post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri + # scen = Scenario(post) - @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" - @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - @test scen.electric_utility.cambium_emissions_region == "RMPAc" - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 - @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday - @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday - @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region - @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data - @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # 2) AK location - city = "Fairbanks" - post["Site"]["latitude"] = cities[city][1] - post["Site"]["longitude"] = cities[city][2] - scen = Scenario(post) + # @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" + # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + # @test scen.electric_utility.cambium_emissions_region == "RMPAc" + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 + # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday + # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday + # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region + # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data + # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # # 2) AK location + # city = "Fairbanks" + # post["Site"]["latitude"] = cities[city][1] + # post["Site"]["longitude"] = cities[city][2] + # scen = Scenario(post) - @test scen.electric_utility.avert_emissions_region == "Alaska" - @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used - @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value - @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # 3) International location - city = "Santiago" - post["Site"]["latitude"] = cities[city][1] - post["Site"]["longitude"] = cities[city][2] - scen = Scenario(post) - - @test scen.electric_utility.avert_emissions_region == "" - @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 - @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 - @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 + # @test scen.electric_utility.avert_emissions_region == "Alaska" + # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used + # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value + # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # # 3) International location + # city = "Santiago" + # post["Site"]["latitude"] = cities[city][1] + # post["Site"]["longitude"] = cities[city][2] + # scen = Scenario(post) + + # @test scen.electric_utility.avert_emissions_region == "" + # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 + # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 + # @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 - end - - @testset "Emissions and Renewable Energy Percent" begin - #renewable energy and emissions reduction targets - include_exported_RE_in_total = [true,false,true] - include_exported_ER_in_total = [true,false,true] - RE_target = [0.8,nothing,nothing] - ER_target = [nothing,0.8,nothing] - with_outage = [true,false,false] - - for i in range(1, stop=3) - if i == 3 - inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") - else - inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") - end - if i == 1 - inputs["Site"]["latitude"] = 37.746 - inputs["Site"]["longitude"] = -122.448 - # inputs["ElectricUtility"]["emissions_region"] = "California" - end - inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] - inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] - inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end - inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] - inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] - inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] - if with_outage[i] - outage_start_hour = 4032 - outage_duration = 2000 #hrs - inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 - inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration - inputs["Generator"]["max_kw"] = 20 - inputs["Generator"]["existing_kw"] = 2 - inputs["Generator"]["fuel_avail_gal"] = 1000 - end - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt([m1, m2], inputs) - - if !isnothing(ER_target[i]) - ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] - @test ER_target[i] ≈ ER_fraction_out atol=1e-3 - lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] - lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] - ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out - ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) - @test ER_fraction_diff ≈ 0.0 atol=1e-2 - end - - annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] - yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] - yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] - yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out - @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 - if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 - end + # end + + # @testset "Emissions and Renewable Energy Percent" begin + # #renewable energy and emissions reduction targets + # include_exported_RE_in_total = [true,false,true] + # include_exported_ER_in_total = [true,false,true] + # RE_target = [0.8,nothing,nothing] + # ER_target = [nothing,0.8,nothing] + # with_outage = [true,false,false] + + # for i in range(1, stop=3) + # if i == 3 + # inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") + # else + # inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") + # end + # if i == 1 + # inputs["Site"]["latitude"] = 37.746 + # inputs["Site"]["longitude"] = -122.448 + # # inputs["ElectricUtility"]["emissions_region"] = "California" + # end + # inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] + # inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] + # inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end + # inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] + # inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] + # inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] + # if with_outage[i] + # outage_start_hour = 4032 + # outage_duration = 2000 #hrs + # inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 + # inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration + # inputs["Generator"]["max_kw"] = 20 + # inputs["Generator"]["existing_kw"] = 2 + # inputs["Generator"]["fuel_avail_gal"] = 1000 + # end + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt([m1, m2], inputs) + + # if !isnothing(ER_target[i]) + # ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] + # @test ER_target[i] ≈ ER_fraction_out atol=1e-3 + # lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] + # lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] + # ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out + # ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) + # @test ER_fraction_diff ≈ 0.0 atol=1e-2 + # end + + # annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] + # yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] + # yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] + # yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out + # @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 + # if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 + # end - if i == 1 - @test results["PV"]["size_kw"] ≈ 59.7222 atol=1e-1 - @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 - @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 - @test results["Generator"]["size_kw"] ≈ 9.13 atol=1e-1 - @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 - @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.148375 atol=1e-4 - @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.57403012 atol=1e-4 - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 332.4 atol=1 - @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.85 atol=1e-2 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.427 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8459.45 atol=1 - @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 236.95 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 148.54 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 27.813 atol=1e-1 - @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 556.26 - elseif i == 2 - #commented out values are results using same levelization factor as API - @test results["PV"]["size_kw"] ≈ 106.13 atol=1 - @test results["ElectricStorage"]["size_kw"] ≈ 20.09 atol=1 # 20.29 - @test results["ElectricStorage"]["size_kwh"] ≈ 170.94 atol=1 - @test !haskey(results, "Generator") - # Renewable energy - @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 - @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 - @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13308.5 atol=10 # 13542.62 atol=10 - @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 - # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven - @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 491.5 atol=1e-1 - @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 - @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8397.85 atol=1 - @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 - @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 + # if i == 1 + # @test results["PV"]["size_kw"] ≈ 59.7222 atol=1e-1 + # @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 + # @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 + # @test results["Generator"]["size_kw"] ≈ 9.13 atol=1e-1 + # @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 + # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.148375 atol=1e-4 + # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.57403012 atol=1e-4 + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 332.4 atol=1 + # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.85 atol=1e-2 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.427 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8459.45 atol=1 + # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 236.95 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 148.54 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 27.813 atol=1e-1 + # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 556.26 + # elseif i == 2 + # #commented out values are results using same levelization factor as API + # @test results["PV"]["size_kw"] ≈ 106.13 atol=1 + # @test results["ElectricStorage"]["size_kw"] ≈ 20.09 atol=1 # 20.29 + # @test results["ElectricStorage"]["size_kwh"] ≈ 170.94 atol=1 + # @test !haskey(results, "Generator") + # # Renewable energy + # @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 + # @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 + # @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13308.5 atol=10 # 13542.62 atol=10 + # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 + # # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven + # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 491.5 atol=1e-1 + # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 + # @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8397.85 atol=1 + # @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 + # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 - #also test CO2 breakeven cost - inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] - inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] - inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] - inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] - inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] - inputs["Settings"]["include_climate_in_objective"] = true - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - results = run_reopt([m1, m2], inputs) - @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 - elseif i == 3 - @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 - @test !haskey(results, "Wind") - @test !haskey(results, "ElectricStorage") - @test !haskey(results, "Generator") - @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 - @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 - @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 - @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 - yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] - nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) - TONNE_PER_LB = 1/2204.62 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 - @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 - @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 - @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 - @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 - @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 - KWH_PER_MMBTU = 293.07107 - annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] - annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU - @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 - end - end - end - - @testset "Back pressure steam turbine" begin - """ - Validation to ensure that: - 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application - 2) SteamTurbine serves the heating load with the condensing steam - - """ - # Setup inputs, make heating load large to entice SteamTurbine - input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - latitude = input_data["Site"]["latitude"] - longitude = input_data["Site"]["longitude"] - building = "Hospital" - elec_load_multiplier = 5.0 - heat_load_multiplier = 100.0 - input_data["ElectricLoad"]["doe_reference_name"] = building - input_data["SpaceHeatingLoad"]["doe_reference_name"] = building - input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building - elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) - input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) - space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - s = Scenario(input_data) - inputs = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], inputs) - - # The expected values below were directly copied from the REopt_API V2 expected values - @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 - @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 - @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 - @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 - @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 - @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 - - # BAU boiler loads - load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency - load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency - - # ExistingBoiler and SteamTurbine production - boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] - boiler_total = boiler_to_load + boiler_to_st - st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] - - # Fuel/thermal **consumption** - boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - - # Check that all thermal supply to load meets the BAU load - thermal_to_load = sum(boiler_to_load) + sum(st_to_load) - @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 - - # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet - steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] - net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) - @test net_electric_efficiency ≈ 0.052 atol=0.005 - - # Check that the max production of the boiler is still less than peak heating load times thermal factor - factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] - boiler_capacity = maximum(load_boiler_thermal) * factor - @test maximum(boiler_total) <= boiler_capacity - end - - @testset "All heating supply/demand/storage energy balance" begin - """ - Validation to ensure that: - 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included - 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption - 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False - - :return: - """ - - # Start with steam turbine inputs, but adding a bunch below - input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks - input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - delete!(input_data, "DomesticHotWaterLoad") - - # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances - input_data["SteamTurbine"]["min_kw"] = 30.0 - input_data["SteamTurbine"]["max_kw"] = 30.0 - - # Add CHP - input_data["CHP"] = Dict{Any, Any}([ - ("prime_mover", "recip_engine"), - ("size_class", 4), - ("min_kw", 250.0), - ("min_allowable_kw", 0.0), - ("max_kw", 250.0), - ("can_supply_steam_turbine", false), - ("fuel_cost_per_mmbtu", 8.0), - ("cooling_thermal_factor", 1.0) - ]) - - input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 - - # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) - input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") - input_data["AbsorptionChiller"] = Dict{Any, Any}([ - ("min_ton", 600.0), - ("max_ton", 600.0), - ("cop_thermal", 0.7), - ("installed_cost_per_ton", 500.0), - ("om_cost_per_ton", 0.5), - ("heating_load_input", "SpaceHeating") - ]) - - # Add Hot TES - input_data["HotThermalStorage"] = Dict{Any, Any}([ - ("min_gal", 50000.0), - ("max_gal", 50000.0) - ]) - - s = Scenario(input_data) - inputs = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - results = run_reopt(m, inputs) - - thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] - thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech - tech_to_thermal_load = Dict{Any, Any}() - for tech in thermal_techs - tech_to_thermal_load[tech] = Dict{Any, Any}() - for load in thermal_loads - if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") - tech_to_thermal_load[tech][load] = [0.0] * 8760 - else - if load == "waste" - tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] - else - tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] - end - end - end - end - # Hot TES is the other thermal supply - hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - - # BAU boiler loads - load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU - load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] - - # Fuel/thermal **consumption** - boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] - steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - - # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked - alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) - thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) - @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 - - # Check that all thermal to steam turbine is equal to steam turbine thermal consumption - alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) - @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 - - # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False - for tech in ["ExistingBoiler", "CHP"] - if !(tech in inputs.techs.can_supply_steam_turbine) - @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 - end - end - end - - @testset "Electric Heater" begin - d = JSON.parsefile("./scenarios/electric_heater.json") - d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.4 * 8760 - d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.4 * 8760 - d["ProcessHeatLoad"]["annual_mmbtu"] = 0.2 * 8760 - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - - #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased - @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 - @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 - - d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 - d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 - d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - - annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour - annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP - annual_energy_supplied = 87600 + annual_electric_heater_consumption - - #Second run: ElectricHeater produces the required heat with free electricity - @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 - @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 - @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 - - end - - @testset "Process Heat Load" begin - d = JSON.parsefile("./scenarios/process_heat.json") + # #also test CO2 breakeven cost + # inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + # inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + # inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] + # inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] + # inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] + # inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] + # inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] + # inputs["Settings"]["include_climate_in_objective"] = true + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + # results = run_reopt([m1, m2], inputs) + # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 + # elseif i == 3 + # @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 + # @test !haskey(results, "Wind") + # @test !haskey(results, "ElectricStorage") + # @test !haskey(results, "Generator") + # @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 + # @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 + # @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 + # @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 + # yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] + # nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) + # TONNE_PER_LB = 1/2204.62 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 + # @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 + # @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 + # @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 + # @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 + # @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 + # KWH_PER_MMBTU = 293.07107 + # annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] + # annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU + # @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 + # end + # end + # end + + # @testset "Back pressure steam turbine" begin + # """ + # Validation to ensure that: + # 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application + # 2) SteamTurbine serves the heating load with the condensing steam + + # """ + # # Setup inputs, make heating load large to entice SteamTurbine + # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + # latitude = input_data["Site"]["latitude"] + # longitude = input_data["Site"]["longitude"] + # building = "Hospital" + # elec_load_multiplier = 5.0 + # heat_load_multiplier = 100.0 + # input_data["ElectricLoad"]["doe_reference_name"] = building + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = building + # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building + # elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) + # input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) + # space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + # dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], inputs) + + # # The expected values below were directly copied from the REopt_API V2 expected values + # @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 + # @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 + # @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 + # @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 + # @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 + # @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 + + # # BAU boiler loads + # load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency + # load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency + + # # ExistingBoiler and SteamTurbine production + # boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + # boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] + # boiler_total = boiler_to_load + boiler_to_st + # st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] + + # # Fuel/thermal **consumption** + # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + + # # Check that all thermal supply to load meets the BAU load + # thermal_to_load = sum(boiler_to_load) + sum(st_to_load) + # @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 + + # # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet + # steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] + # net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) + # @test net_electric_efficiency ≈ 0.052 atol=0.005 + + # # Check that the max production of the boiler is still less than peak heating load times thermal factor + # factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] + # boiler_capacity = maximum(load_boiler_thermal) * factor + # @test maximum(boiler_total) <= boiler_capacity + # end + + # @testset "All heating supply/demand/storage energy balance" begin + # """ + # Validation to ensure that: + # 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included + # 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption + # 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False + + # :return: + # """ + + # # Start with steam turbine inputs, but adding a bunch below + # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + # # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks + # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + # delete!(input_data, "DomesticHotWaterLoad") + + # # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances + # input_data["SteamTurbine"]["min_kw"] = 30.0 + # input_data["SteamTurbine"]["max_kw"] = 30.0 + + # # Add CHP + # input_data["CHP"] = Dict{Any, Any}([ + # ("prime_mover", "recip_engine"), + # ("size_class", 4), + # ("min_kw", 250.0), + # ("min_allowable_kw", 0.0), + # ("max_kw", 250.0), + # ("can_supply_steam_turbine", false), + # ("fuel_cost_per_mmbtu", 8.0), + # ("cooling_thermal_factor", 1.0) + # ]) + + # input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 + + # # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) + # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") + # input_data["AbsorptionChiller"] = Dict{Any, Any}([ + # ("min_ton", 600.0), + # ("max_ton", 600.0), + # ("cop_thermal", 0.7), + # ("installed_cost_per_ton", 500.0), + # ("om_cost_per_ton", 0.5), + # ("heating_load_input", "SpaceHeating") + # ]) + + # # Add Hot TES + # input_data["HotThermalStorage"] = Dict{Any, Any}([ + # ("min_gal", 50000.0), + # ("max_gal", 50000.0) + # ]) + + # s = Scenario(input_data) + # inputs = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + # results = run_reopt(m, inputs) + + # thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] + # thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech + # tech_to_thermal_load = Dict{Any, Any}() + # for tech in thermal_techs + # tech_to_thermal_load[tech] = Dict{Any, Any}() + # for load in thermal_loads + # if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") + # tech_to_thermal_load[tech][load] = [0.0] * 8760 + # else + # if load == "waste" + # tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] + # else + # tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] + # end + # end + # end + # end + # # Hot TES is the other thermal supply + # hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + + # # BAU boiler loads + # load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU + # load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] + + # # Fuel/thermal **consumption** + # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + # chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] + # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + # absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + + # # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked + # alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) + # thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) + # @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 + + # # Check that all thermal to steam turbine is equal to steam turbine thermal consumption + # alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) + # @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 + + # # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False + # for tech in ["ExistingBoiler", "CHP"] + # if !(tech in inputs.techs.can_supply_steam_turbine) + # @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 + # end + # end + # end + + # @testset "Electric Heater" begin + # d = JSON.parsefile("./scenarios/electric_heater.json") + # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.4 * 8760 + # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.4 * 8760 + # d["ProcessHeatLoad"]["annual_mmbtu"] = 0.2 * 8760 + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + + # #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased + # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 + # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 + + # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 + # d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 + # d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + + # annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour + # annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP + # annual_energy_supplied = 87600 + annual_electric_heater_consumption + + # #Second run: ElectricHeater produces the required heat with free electricity + # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 + # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 + # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 + + # end + + # @testset "Process Heat Load" begin + # d = JSON.parsefile("./scenarios/process_heat.json") - # Test set 1: Boiler has free fuel, no emissions, and serves all heating load. - d["Boiler"]["fuel_cost_per_mmbtu"] = 0.0 - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 - @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0.0 atol=0.1 + # # Test set 1: Boiler has free fuel, no emissions, and serves all heating load. + # d["Boiler"]["fuel_cost_per_mmbtu"] = 0.0 + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 + # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0.0 atol=0.1 - #Test set 2: Boiler only serves process heat - d["Boiler"]["can_serve_dhw"] = false - d["Boiler"]["can_serve_space_heating"] = false - d["Boiler"]["can_serve_process_heat"] = true - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 8.0 atol=0.1 - @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # #Test set 2: Boiler only serves process heat + # d["Boiler"]["can_serve_dhw"] = false + # d["Boiler"]["can_serve_space_heating"] = false + # d["Boiler"]["can_serve_process_heat"] = true + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 8.0 atol=0.1 + # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - #Test set 3: Boiler cannot serve process heat but serves DHW, space heating - d["Boiler"]["can_serve_dhw"] = true - d["Boiler"]["can_serve_space_heating"] = true - d["Boiler"]["can_serve_process_heat"] = false - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 16.0 atol=0.1 - @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # #Test set 3: Boiler cannot serve process heat but serves DHW, space heating + # d["Boiler"]["can_serve_dhw"] = true + # d["Boiler"]["can_serve_space_heating"] = true + # d["Boiler"]["can_serve_process_heat"] = false + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 16.0 atol=0.1 + # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - #Test set 4: Fuel expensive, but ExistingBoiler is retired - d["Boiler"]["can_serve_dhw"] = true - d["Boiler"]["can_serve_space_heating"] = true - d["Boiler"]["can_serve_process_heat"] = true - d["Boiler"]["fuel_cost_per_mmbtu"] = 30.0 - d["ExistingBoiler"]["retire_in_optimal"] = true - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 - @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # #Test set 4: Fuel expensive, but ExistingBoiler is retired + # d["Boiler"]["can_serve_dhw"] = true + # d["Boiler"]["can_serve_space_heating"] = true + # d["Boiler"]["can_serve_process_heat"] = true + # d["Boiler"]["fuel_cost_per_mmbtu"] = 30.0 + # d["ExistingBoiler"]["retire_in_optimal"] = true + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 + # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - #Test set 5: Fuel expensive, ExistingBoiler not retired - d["ExistingBoiler"]["retire_in_optimal"] = false - s = Scenario(d) - p = REoptInputs(s) - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt(m, p) - @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 - @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # #Test set 5: Fuel expensive, ExistingBoiler not retired + # d["ExistingBoiler"]["retire_in_optimal"] = false + # s = Scenario(d) + # p = REoptInputs(s) + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt(m, p) + # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 + # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # Test 6: reduce emissions by half, get half the new boiler size - d["Site"]["CO2_emissions_reduction_min_fraction"] = 0.50 - s = Scenario(d) - p = REoptInputs(s) - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - results = run_reopt([m1,m2], p) - @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 12.0 atol=0.1 - @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 - @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 - end - - @testset "Custom REopt logger" begin - - # Throw a handled error - d = JSON.parsefile("./scenarios/logger.json") - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - @test r["Messages"]["has_stacktrace"] == false - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - # Type is dict when errors, otherwise type REoptInputs - @test isa(REoptInputs(d), Dict) - - # Using filepath - n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([n1,n2], "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(n, "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging - d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" - d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" - - m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([m1,m2], d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(m, d) - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - # Type is dict when errors, otherwise type REoptInputs - @test isa(REoptInputs(d), Dict) - - # Using filepath - n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt([n1,n2], "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - - n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - r = run_reopt(n, "./scenarios/logger.json") - @test r["status"] == "error" - @test "Messages" ∈ keys(r) - @test "errors" ∈ keys(r["Messages"]) - @test "warnings" ∈ keys(r["Messages"]) - @test length(r["Messages"]["errors"]) > 0 - @test length(r["Messages"]["warnings"]) > 0 - end + # # Test 6: reduce emissions by half, get half the new boiler size + # d["Site"]["CO2_emissions_reduction_min_fraction"] = 0.50 + # s = Scenario(d) + # p = REoptInputs(s) + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # results = run_reopt([m1,m2], p) + # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 12.0 atol=0.1 + # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 + # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 + # end + + # @testset "Custom REopt logger" begin + + # # Throw a handled error + # d = JSON.parsefile("./scenarios/logger.json") + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + # @test r["Messages"]["has_stacktrace"] == false + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # # Type is dict when errors, otherwise type REoptInputs + # @test isa(REoptInputs(d), Dict) + + # # Using filepath + # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([n1,n2], "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(n, "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging + # d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" + # d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" + + # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([m1,m2], d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(m, d) + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # # Type is dict when errors, otherwise type REoptInputs + # @test isa(REoptInputs(d), Dict) + + # # Using filepath + # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt([n1,n2], "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + + # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # r = run_reopt(n, "./scenarios/logger.json") + # @test r["status"] == "error" + # @test "Messages" ∈ keys(r) + # @test "errors" ∈ keys(r["Messages"]) + # @test "warnings" ∈ keys(r["Messages"]) + # @test length(r["Messages"]["errors"]) > 0 + # @test length(r["Messages"]["warnings"]) > 0 + # end end end \ No newline at end of file diff --git a/test/scenarios/multiple_elec_storage.json b/test/scenarios/multiple_elec_storage.json index cbf135f20..99b31e0a8 100644 --- a/test/scenarios/multiple_elec_storage.json +++ b/test/scenarios/multiple_elec_storage.json @@ -39,10 +39,10 @@ "replace_cost_per_kwh": 230.0, "installed_cost_per_kw": 1000.0, "installed_cost_per_kwh": 500.0, - "total_itc_fraction": 0.0 + "total_itc_fraction": 0.0, + "allow_simultaneous_charge_discharge": false }, { - "name": "ExpensiveElecStor", "total_rebate_per_kw": 100.0, "macrs_option_years": 5, "can_grid_charge": true, @@ -51,7 +51,8 @@ "replace_cost_per_kwh": 230.0, "installed_cost_per_kw": 1500.0, "installed_cost_per_kwh": 500.0, - "total_itc_fraction": 0.0 + "total_itc_fraction": 0.0, + "allow_simultaneous_charge_discharge": false } ], "ElectricTariff": { From a896afb985596b57512571d4bd69c5e29ad1b6ce Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 15:00:07 -0600 Subject: [PATCH 133/151] uncomment tests --- test/runtests.jl | 5154 +++++++++++++++++++++++----------------------- 1 file changed, 2577 insertions(+), 2577 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index 30ae3d8c7..1168b4dd3 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -41,2643 +41,2643 @@ else # run HiGHS tests end end end - # @testset "Prevent simultaneous charge and discharge" begin - # logger = SimpleLogger() - # results = nothing - # with_logger(logger) do - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/simultaneous_charge_discharge.json") - # end - # @test any(.&( - # results["ElectricStorage"]["storage_to_load_series_kw"] .!= 0.0, - # ( - # results["ElectricUtility"]["electric_to_storage_series_kw"] .+ - # results["PV"]["electric_to_storage_series_kw"] - # ) .!= 0.0 - # ) - # ) ≈ false - # @test any(.&( - # results["Outages"]["storage_discharge_series_kw"] .!= 0.0, - # results["Outages"]["pv_to_storage_series_kw"] .!= 0.0 - # ) - # ) ≈ false - # end - # @testset "hybrid profile" begin - # electric_load = REopt.ElectricLoad(; - # blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], - # blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], - # annual_kwh = 50000.0, - # year = 2017, - # city = "Atlanta", - # latitude = 35.2468, - # longitude = -91.7337 - # ) - # @test sum(electric_load.loads_kw) ≈ 50000.0 - # end - # @testset "Solar dataset" begin - - # # 1. Dallas TX - # latitude, longitude = 32.775212075983646, -96.78105623767185 - # radius = 0 - # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - # @test dataset ≈ "nsrdb" - - # # 2. Merefa, Ukraine - # latitude, longitude = 49.80670544975866, 36.05418033509974 - # radius = 0 - # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - # @test dataset ≈ "nsrdb" - - # # 3. Oulu, Findland - # latitude, longitude = 65.0102196310875, 25.465387094897675 - # radius = 0 - # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - # @test dataset ≈ "intl" - - # # 4. Fairbanks, AK - # site = "Fairbanks" - # latitude, longitude = 64.84112047064114, -147.71570239058084 - # radius = 20 - # dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - # @test dataset ≈ "tmy3" - # end - - # @testset "January Export Rates" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/monthly_rate.json") - - # # create wholesale_rate with compensation in January > retail rate - # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - # data["ElectricTariff"]["wholesale_rate"] = - # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(model, inputs) - - # @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 - # @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC - # @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) - # end - - # @testset "Blended tariff" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/no_techs.json") - # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - # end - - # @testset "Solar and Storage" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(model, "./scenarios/pv_storage.json") - - # @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - # @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 - # @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 - # @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - # end - - # @testset "Outage with Generator" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/generator.json") - # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - # p = REoptInputs("./scenarios/generator.json") - # simresults = simulate_outages(results, p) - # @test simresults["resilience_hours_max"] == 11 - # end - - # # TODO test MPC with outages - # @testset "MPC" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_mpc(model, "./scenarios/mpc.json") - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - # end - - # @testset "MPC Multi-node" begin - # # not doing much yet; just testing that two identical sites have the same costs - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # ps = MPCInputs[] - # push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); - # push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); - # r = run_mpc(model, ps) - # @test r[1]["Costs"] ≈ r[2]["Costs"] - # end - - # @testset "Complex Incentives" begin - # """ - # This test was compared against the API test: - # reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives - # when using the hardcoded levelization_factor in this package's REoptInputs function. - # The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) - # """ - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/incentives.json") - # @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 - # end - - # @testset "Fifteen minute load" begin - # d = JSON.parsefile("scenarios/no_techs.json") - # d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) - # d["Settings"] = Dict("time_steps_per_hour" => 4) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, d) - # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 - # end - - # try - # rm("Highs.log", force=true) - # catch - # @warn "Could not delete test/Highs.log" - # end - - # @testset "AVERT region abberviations" begin - # """ - # This test checks 5 scenarios (in order) - # 1. Coordinate pair inside an AVERT polygon - # 2. Coordinate pair near a US border - # 3. Coordinate pair < 5 miles from US border - # 4. Coordinate pair > 5 miles from US border - # 5. Coordinate pair >> 5 miles from US border - # """ - # (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) - # @test r == "AKGD" - # (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) - # @test r == "HIOA" - # (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) - # @test r == "HIMS" - # (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) - # @test r == "RM" - # @test d ≈ 0.0 atol=1 - # (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) - # @test r == "NE" - # @test d ≈ 7986 atol=1 - # (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) - # @test r === nothing - # @test d ≈ 10297 atol=1 - # (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) - # @test r === nothing - # end - - # @testset "PVspecs" begin - # ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) - # post_name = "pv.json" - # post = JSON.parsefile("./scenarios/$post_name") - # scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 20 - # @test scen.pvs[1].azimuth ≈ 180 + @testset "Prevent simultaneous charge and discharge" begin + logger = SimpleLogger() + results = nothing + with_logger(logger) do + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/simultaneous_charge_discharge.json") + end + @test any(.&( + results["ElectricStorage"]["storage_to_load_series_kw"] .!= 0.0, + ( + results["ElectricUtility"]["electric_to_storage_series_kw"] .+ + results["PV"]["electric_to_storage_series_kw"] + ) .!= 0.0 + ) + ) ≈ false + @test any(.&( + results["Outages"]["storage_discharge_series_kw"] .!= 0.0, + results["Outages"]["pv_to_storage_series_kw"] .!= 0.0 + ) + ) ≈ false + end + @testset "hybrid profile" begin + electric_load = REopt.ElectricLoad(; + blended_doe_reference_percents = [0.2, 0.2, 0.2, 0.2, 0.2], + blended_doe_reference_names = ["RetailStore", "LargeOffice", "MediumOffice", "SmallOffice", "Warehouse"], + annual_kwh = 50000.0, + year = 2017, + city = "Atlanta", + latitude = 35.2468, + longitude = -91.7337 + ) + @test sum(electric_load.loads_kw) ≈ 50000.0 + end + @testset "Solar dataset" begin + + # 1. Dallas TX + latitude, longitude = 32.775212075983646, -96.78105623767185 + radius = 0 + dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + @test dataset ≈ "nsrdb" + + # 2. Merefa, Ukraine + latitude, longitude = 49.80670544975866, 36.05418033509974 + radius = 0 + dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + @test dataset ≈ "nsrdb" + + # 3. Oulu, Findland + latitude, longitude = 65.0102196310875, 25.465387094897675 + radius = 0 + dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + @test dataset ≈ "intl" + + # 4. Fairbanks, AK + site = "Fairbanks" + latitude, longitude = 64.84112047064114, -147.71570239058084 + radius = 20 + dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) + @test dataset ≈ "tmy3" + end + + @testset "January Export Rates" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/monthly_rate.json") + + # create wholesale_rate with compensation in January > retail rate + jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + data["ElectricTariff"]["wholesale_rate"] = + append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(model, inputs) + + @test results["PV"]["size_kw"] ≈ 68.9323 atol=0.01 + @test results["Financial"]["lcc"] ≈ 432681.26 rtol=1e-5 # with levelization_factor hack the LCC is within 5e-5 of REopt API LCC + @test all(x == 0.0 for x in results["PV"]["electric_to_load_series_kw"][1:744]) + end + + @testset "Blended tariff" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/no_techs.json") + @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + end + + @testset "Solar and Storage" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(model, "./scenarios/pv_storage.json") + + @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 + @test r["ElectricStorage"]["size_kw"] ≈ 49.0 atol=0.1 + @test r["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + end + + @testset "Outage with Generator" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/generator.json") + @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + p = REoptInputs("./scenarios/generator.json") + simresults = simulate_outages(results, p) + @test simresults["resilience_hours_max"] == 11 + end + + # TODO test MPC with outages + @testset "MPC" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_mpc(model, "./scenarios/mpc.json") + @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + end + + @testset "MPC Multi-node" begin + # not doing much yet; just testing that two identical sites have the same costs + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + ps = MPCInputs[] + push!(ps, MPCInputs("./scenarios/mpc_multinode1.json")); + push!(ps, MPCInputs("./scenarios/mpc_multinode2.json")); + r = run_mpc(model, ps) + @test r[1]["Costs"] ≈ r[2]["Costs"] + end + + @testset "Complex Incentives" begin + """ + This test was compared against the API test: + reo.tests.test_reopt_url.EntryResourceTest.test_complex_incentives + when using the hardcoded levelization_factor in this package's REoptInputs function. + The two LCC's matched within 0.00005%. (The Julia pkg LCC is 1.0971991e7) + """ + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/incentives.json") + @test results["Financial"]["lcc"] ≈ 1.096852612e7 atol=1e4 + end + + @testset "Fifteen minute load" begin + d = JSON.parsefile("scenarios/no_techs.json") + d["ElectricLoad"] = Dict("loads_kw" => repeat([1.0], 35040)) + d["Settings"] = Dict("time_steps_per_hour" => 4) + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, d) + @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 8760 + end + + try + rm("Highs.log", force=true) + catch + @warn "Could not delete test/Highs.log" + end + + @testset "AVERT region abberviations" begin + """ + This test checks 5 scenarios (in order) + 1. Coordinate pair inside an AVERT polygon + 2. Coordinate pair near a US border + 3. Coordinate pair < 5 miles from US border + 4. Coordinate pair > 5 miles from US border + 5. Coordinate pair >> 5 miles from US border + """ + (r, d) = REopt.avert_region_abbreviation(65.27661752129738, -149.59278391820223) + @test r == "AKGD" + (r, d) = REopt.avert_region_abbreviation(21.45440792261567, -157.93648793163402) + @test r == "HIOA" + (r, d) = REopt.avert_region_abbreviation(19.686877556659436, -155.4223641905743) + @test r == "HIMS" + (r, d) = REopt.avert_region_abbreviation(39.86357200140234, -104.67953917092028) + @test r == "RM" + @test d ≈ 0.0 atol=1 + (r, d) = REopt.avert_region_abbreviation(47.49137892652077, -69.3240287592685) + @test r == "NE" + @test d ≈ 7986 atol=1 + (r, d) = REopt.avert_region_abbreviation(47.50448307102053, -69.34882434376593) + @test r === nothing + @test d ≈ 10297 atol=1 + (r, d) = REopt.avert_region_abbreviation(55.860334445251354, -4.286554357755312) + @test r === nothing + end + + @testset "PVspecs" begin + ## Scenario 1: Palmdale, CA; array-type = 0 (Ground-mount) + post_name = "pv.json" + post = JSON.parsefile("./scenarios/$post_name") + scen = Scenario(post) + @test scen.pvs[1].tilt ≈ 20 + @test scen.pvs[1].azimuth ≈ 180 - # ## Scenario 2: Palmdale, CA; array-type = 1 (roof) - # post["PV"]["array_type"] = 1 - # scen = Scenario(post) + ## Scenario 2: Palmdale, CA; array-type = 1 (roof) + post["PV"]["array_type"] = 1 + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 + @test scen.pvs[1].tilt ≈ 20 # Correct tilt value for array_type = 1 - # ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) - # post["PV"]["array_type"] = 2 - # scen = Scenario(post) + ## Scenario 3: Palmdale, CA; array-type = 2 (axis-tracking) + post["PV"]["array_type"] = 2 + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 + @test scen.pvs[1].tilt ≈ 0 # Correct tilt value for array_type = 2 - # ## Scenario 4: Cape Town; array-type = 0 (ground) - # post["Site"]["latitude"] = -33.974732 - # post["Site"]["longitude"] = 19.130050 - # post["PV"]["array_type"] = 0 - # scen = Scenario(post) + ## Scenario 4: Cape Town; array-type = 0 (ground) + post["Site"]["latitude"] = -33.974732 + post["Site"]["longitude"] = 19.130050 + post["PV"]["array_type"] = 0 + scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 20 - # @test scen.pvs[1].azimuth ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - - # ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) - # post["PV"]["tilt"] = 17 - # scen = Scenario(post) - # @test scen.pvs[1].tilt ≈ 17 - # end - - # @testset "AlternativeFlatLoads" begin - # input_data = JSON.parsefile("./scenarios/flatloads.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year - # active_hours_8_5 = 8 * 5 * 52 - # @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 - # # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year - # @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 - # @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 - # end + @test scen.pvs[1].tilt ≈ 20 + @test scen.pvs[1].azimuth ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + + ## Scenario 4:Cape Town; array-type = 0 (ground); user-provided tilt (should not get overwritten) + post["PV"]["tilt"] = 17 + scen = Scenario(post) + @test scen.pvs[1].tilt ≈ 17 + end + + @testset "AlternativeFlatLoads" begin + input_data = JSON.parsefile("./scenarios/flatloads.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + + # FlatLoad_8_5 => 8 hrs/day, 5 days/week, 52 weeks/year + active_hours_8_5 = 8 * 5 * 52 + @test count(x->x>0, s.space_heating_load.loads_kw, dims=1)[1] == active_hours_8_5 + # FlatLoad_16_7 => only hours 6-22 should be >0, and each day is the same portion of the total year + @test sum(s.electric_load.loads_kw[1:5]) + sum(s.electric_load.loads_kw[23:24]) == 0.0 + @test sum(s.electric_load.loads_kw[6:22]) / sum(s.electric_load.loads_kw) - 1/365 ≈ 0.0 atol=0.000001 + end - # # removed Wind test for two reasons - # # 1. reduce WindToolKit calls in tests - # # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints + # removed Wind test for two reasons + # 1. reduce WindToolKit calls in tests + # 2. HiGHS does not support SOS or indicator constraints, which are needed for export constraints - # @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin - # """ + @testset "Simulated load function consistency with REoptInputs.s (Scenario)" begin + """ - # This tests the consistency between getting DOE commercial reference building (CRB) load data - # from the simulated_load function and the processing of REoptInputs.s (Scenario struct). + This tests the consistency between getting DOE commercial reference building (CRB) load data + from the simulated_load function and the processing of REoptInputs.s (Scenario struct). - # The simulated_load function is used for the /simulated_load endpoint in the REopt API, - # in particular for the webtool/UI to display loads before running REopt, but is also generally - # an external way to access CRB load data without running REopt. - - # One particular test specifically for the webtool/UI is for the heating load because there is just a - # single heating load instead of separated space heating and domestic hot water loads. - - # """ - # input_data = JSON.parsefile("./scenarios/simulated_load.json") - - # input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), - # ("blended_doe_reference_percents", [0.2, 0.8]) - # ]) - - # input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["Warehouse", "FlatLoad"]), - # ("blended_doe_reference_percents", [0.5, 0.5]) - # ]) - - # # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu - # annual_mmbtu_hvac = 7000.0 - # annual_mmbtu_process = 3000.0 - # doe_reference_name_heating = ["Warehouse", "FlatLoad"] - # percent_share_heating = [0.3, 0.7] - - # d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), - # ("longitude", input_data["Site"]["longitude"]), - # ("load_type", "heating"), # since annual_tonhour is not given - # ("doe_reference_name", doe_reference_name_heating), - # ("percent_share", percent_share_heating), - # ("annual_mmbtu", annual_mmbtu_hvac) - # ]) - - # sim_load_response_heating = simulated_load(d_sim_load_heating) - - # d_sim_load_process = copy(d_sim_load_heating) - # d_sim_load_process["load_type"] = "process_heat" - # d_sim_load_process["annual_mmbtu"] = annual_mmbtu_process - # sim_load_response_process = simulated_load(d_sim_load_process) - - # input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - # ("blended_doe_reference_percents", percent_share_heating), - # ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) - # ]) - - # input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), - # ("blended_doe_reference_percents", percent_share_heating), - # ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) - # ]) - - # input_data["ProcessHeatLoad"] = Dict([("blended_industry_reference_names", doe_reference_name_heating), - # ("blended_industry_reference_percents", percent_share_heating), - # ("annual_mmbtu", annual_mmbtu_process) - # ]) + The simulated_load function is used for the /simulated_load endpoint in the REopt API, + in particular for the webtool/UI to display loads before running REopt, but is also generally + an external way to access CRB load data without running REopt. + + One particular test specifically for the webtool/UI is for the heating load because there is just a + single heating load instead of separated space heating and domestic hot water loads. + + """ + input_data = JSON.parsefile("./scenarios/simulated_load.json") + + input_data["ElectricLoad"] = Dict([("blended_doe_reference_names", ["Hospital", "FlatLoad_16_5"]), + ("blended_doe_reference_percents", [0.2, 0.8]) + ]) + + input_data["CoolingLoad"] = Dict([("blended_doe_reference_names", ["Warehouse", "FlatLoad"]), + ("blended_doe_reference_percents", [0.5, 0.5]) + ]) + + # Heating load from the UI will call the /simulated_load endpoint first to parse single heating mmbtu into separate Space and DHW mmbtu + annual_mmbtu_hvac = 7000.0 + annual_mmbtu_process = 3000.0 + doe_reference_name_heating = ["Warehouse", "FlatLoad"] + percent_share_heating = [0.3, 0.7] + + d_sim_load_heating = Dict([("latitude", input_data["Site"]["latitude"]), + ("longitude", input_data["Site"]["longitude"]), + ("load_type", "heating"), # since annual_tonhour is not given + ("doe_reference_name", doe_reference_name_heating), + ("percent_share", percent_share_heating), + ("annual_mmbtu", annual_mmbtu_hvac) + ]) + + sim_load_response_heating = simulated_load(d_sim_load_heating) + + d_sim_load_process = copy(d_sim_load_heating) + d_sim_load_process["load_type"] = "process_heat" + d_sim_load_process["annual_mmbtu"] = annual_mmbtu_process + sim_load_response_process = simulated_load(d_sim_load_process) + + input_data["SpaceHeatingLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + ("blended_doe_reference_percents", percent_share_heating), + ("annual_mmbtu", sim_load_response_heating["space_annual_mmbtu"]) + ]) + + input_data["DomesticHotWaterLoad"] = Dict([("blended_doe_reference_names", doe_reference_name_heating), + ("blended_doe_reference_percents", percent_share_heating), + ("annual_mmbtu", sim_load_response_heating["dhw_annual_mmbtu"]) + ]) + + input_data["ProcessHeatLoad"] = Dict([("blended_industry_reference_names", doe_reference_name_heating), + ("blended_industry_reference_percents", percent_share_heating), + ("annual_mmbtu", annual_mmbtu_process) + ]) - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # # Call simulated_load function to check cooling - # d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), - # ("longitude", input_data["Site"]["longitude"]), - # ("load_type", "electric"), # since annual_tonhour is not given - # ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), - # ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), - # ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), - # ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), - # ]) - - # sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) - # sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] - # sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] - - # total_heating_thermal_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw + s.process_heat_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY - - # @test round.(sim_load_response_heating["loads_mmbtu_per_hour"] + - # sim_load_response_process["loads_mmbtu_per_hour"], digits=2) ≈ - # round.(total_heating_thermal_load_reopt_inputs, digits=2) rtol=0.02 - - # @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 - # @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 - # end - - # @testset verbose=true "Backup Generator Reliability" begin - - # @testset "Compare backup_reliability and simulate_outages" begin - # # Tests ensure `backup_reliability()` consistent with `simulate_outages()` - # # First, just battery - # reopt_inputs = Dict( - # "Site" => Dict( - # "longitude" => -106.42077256104001, - # "latitude" => 31.810468380036337 - # ), - # "ElectricStorage" => Dict( - # "min_kw" => 4000, - # "max_kw" => 4000, - # "min_kwh" => 400000, - # "max_kwh" => 400000, - # "soc_min_fraction" => 0.8, - # "soc_init_fraction" => 0.9 - # ), - # "ElectricLoad" => Dict( - # "doe_reference_name" => "FlatLoad", - # "annual_kwh" => 175200000.0, - # "critical_load_fraction" => 0.2 - # ), - # "ElectricTariff" => Dict( - # "urdb_label" => "5ed6c1a15457a3367add15ae" - # ), - # ) - # p = REoptInputs(reopt_inputs) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, p) - # simresults = simulate_outages(results, p) - - # reliability_inputs = Dict( - # "generator_size_kw" => 0, - # "max_outage_duration" => 100, - # "generator_operational_availability" => 1.0, - # "generator_failure_to_start" => 0.0, - # "generator_mean_time_to_failure" => 10000000000, - # "fuel_limit" => 0, - # "battery_size_kw" => 4000, - # "battery_size_kwh" => 400000, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_operational_availability" => 1.0, - # "battery_minimum_soc_fraction" => 0.0, - # "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], - # "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw - # ) - # reliability_results = backup_reliability(reliability_inputs) - - # #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early - # for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 - # end - - # # Second, gen, PV, Wind, battery - # reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") - # reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] - # p = REoptInputs(reopt_inputs) - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, p) - # simresults = simulate_outages(results, p) - # reliability_inputs = Dict( - # "max_outage_duration" => 48, - # "generator_operational_availability" => 1.0, - # "generator_failure_to_start" => 0.0, - # "generator_mean_time_to_failure" => 10000000000, - # "fuel_limit" => 1000000000, - # "battery_operational_availability" => 1.0, - # "battery_minimum_soc_fraction" => 0.0, - # "pv_operational_availability" => 1.0, - # "wind_operational_availability" => 1.0 - # ) - # reliability_results = backup_reliability(results, p, reliability_inputs) - # for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) - # @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 - # end - # end - - # # Test survival with no generator decreasing and same as with generator but no fuel - # reliability_inputs = Dict( - # "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), - # "num_generators" => 0, - # "generator_size_kw" => 312.0, - # "fuel_limit" => 0.0, - # "max_outage_duration" => 10, - # "battery_size_kw" => 428.0, - # "battery_size_kwh" => 1585.0, - # "num_battery_bins" => 5 - # ) - # reliability_results1 = backup_reliability(reliability_inputs) - # reliability_inputs["generator_size_kw"] = 0 - # reliability_inputs["fuel_limit"] = 1e10 - # reliability_results2 = backup_reliability(reliability_inputs) - # for i in 1:reliability_inputs["max_outage_duration"] - # if i != 1 - # @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] - # @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] - # end - # @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] - # end - - # #test fuel limit - # input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") - # results = backup_reliability(input_dict) - # @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 - # @test results["cumulative_survival_final_time_step"][1] ≈ 1 - - # input_dict = Dict( - # "critical_loads_kw" => [1,2,2,1], - # "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], - # "max_outage_duration" => 3, - # "num_generators" => 2, "generator_size_kw" => 1, - # "generator_operational_availability" => 1, - # "generator_failure_to_start" => 0.0, - # "generator_mean_time_to_failure" => 5, - # "battery_operational_availability" => 1, - # "num_battery_bins" => 3, - # "battery_size_kwh" => 4, - # "battery_size_kw" => 1, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_minimum_soc_fraction" => 0.5) - - - # #Given outage starts in time period 1 - # #____________________________________ - # #Outage hour 1: - # #2 generators: Prob = 0.64, Battery = 2, Survived - # #1 generator: Prob = 0.32, Battery = 1, Survived - # #0 generator: Prob = 0.04, Battery = 0, Survived - # #Survival Probability 1.0 - - # #Outage hour 2: - # #2 generators: Prob = 0.4096, Battery = 2, Survived - # #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived - # #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived - # #0 generators: Prob = 0.1296, Battery = -1, Failed - # #Survival Probability: 0.8704 - - # #Outage hour 3: - # #2 generators: Prob = 0.262144, Battery = 0, Survived - # #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived - # #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived - # #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed - # #0 generators Prob = 0.238144, Battery = -1, Failed - # #Survival Probability: 0.557056 - # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - # #Test multiple generator types - # input_dict = Dict( - # "critical_loads_kw" => [1,2,2,1], - # "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], - # "max_outage_duration" => 3, - # "num_generators" => [1,1], - # "generator_size_kw" => [1,1], - # "generator_operational_availability" => [1,1], - # "generator_failure_to_start" => [0.0, 0.0], - # "generator_mean_time_to_failure" => [5, 5], - # "battery_operational_availability" => 1.0, - # "num_battery_bins" => 3, - # "battery_size_kwh" => 2, - # "battery_size_kw" => 1, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_minimum_soc_fraction" => 0) - - # @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 - - # #8760 of flat load. Battery can survive 4 hours. - # #Survival after 24 hours should be chance of generator surviving 20 or more hours - # input_dict = Dict( - # "critical_loads_kw" => 100 .* ones(8760), - # "max_outage_duration" => 24, - # "num_generators" => 1, - # "generator_size_kw" => 100, - # "generator_operational_availability" => 0.98, - # "generator_failure_to_start" => 0.1, - # "generator_mean_time_to_failure" => 100, - # "battery_operational_availability" => 1.0, - # "num_battery_bins" => 101, - # "battery_size_kwh" => 400, - # "battery_size_kw" => 100, - # "battery_charge_efficiency" => 1, - # "battery_discharge_efficiency" => 1, - # "battery_minimum_soc_fraction" => 0) - - # reliability_results = backup_reliability(input_dict) - # @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 - - # #More complex case of hospital load with 2 generators, PV, wind, and battery - # reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") - # reliability_results = backup_reliability(reliability_inputs) - # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 - # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 + s = Scenario(input_data) + inputs = REoptInputs(s) + + # Call simulated_load function to check cooling + d_sim_load_elec_and_cooling = Dict([("latitude", input_data["Site"]["latitude"]), + ("longitude", input_data["Site"]["longitude"]), + ("load_type", "electric"), # since annual_tonhour is not given + ("doe_reference_name", input_data["ElectricLoad"]["blended_doe_reference_names"]), + ("percent_share", input_data["ElectricLoad"]["blended_doe_reference_percents"]), + ("cooling_doe_ref_name", input_data["CoolingLoad"]["blended_doe_reference_names"]), + ("cooling_pct_share", input_data["CoolingLoad"]["blended_doe_reference_percents"]), + ]) + + sim_load_response_elec_and_cooling = simulated_load(d_sim_load_elec_and_cooling) + sim_electric_kw = sim_load_response_elec_and_cooling["loads_kw"] + sim_cooling_ton = sim_load_response_elec_and_cooling["cooling_defaults"]["loads_ton"] + + total_heating_thermal_load_reopt_inputs = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw + s.process_heat_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ REopt.EXISTING_BOILER_EFFICIENCY + + @test round.(sim_load_response_heating["loads_mmbtu_per_hour"] + + sim_load_response_process["loads_mmbtu_per_hour"], digits=2) ≈ + round.(total_heating_thermal_load_reopt_inputs, digits=2) rtol=0.02 + + @test sim_electric_kw ≈ s.electric_load.loads_kw atol=0.1 + @test sim_cooling_ton ≈ s.cooling_load.loads_kw_thermal ./ REopt.KWH_THERMAL_PER_TONHOUR atol=0.1 + end + + @testset verbose=true "Backup Generator Reliability" begin + + @testset "Compare backup_reliability and simulate_outages" begin + # Tests ensure `backup_reliability()` consistent with `simulate_outages()` + # First, just battery + reopt_inputs = Dict( + "Site" => Dict( + "longitude" => -106.42077256104001, + "latitude" => 31.810468380036337 + ), + "ElectricStorage" => Dict( + "min_kw" => 4000, + "max_kw" => 4000, + "min_kwh" => 400000, + "max_kwh" => 400000, + "soc_min_fraction" => 0.8, + "soc_init_fraction" => 0.9 + ), + "ElectricLoad" => Dict( + "doe_reference_name" => "FlatLoad", + "annual_kwh" => 175200000.0, + "critical_load_fraction" => 0.2 + ), + "ElectricTariff" => Dict( + "urdb_label" => "5ed6c1a15457a3367add15ae" + ), + ) + p = REoptInputs(reopt_inputs) + model = Model(optimizer_with_attributes(HiGHS.Optimizer,"output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, p) + simresults = simulate_outages(results, p) + + reliability_inputs = Dict( + "generator_size_kw" => 0, + "max_outage_duration" => 100, + "generator_operational_availability" => 1.0, + "generator_failure_to_start" => 0.0, + "generator_mean_time_to_failure" => 10000000000, + "fuel_limit" => 0, + "battery_size_kw" => 4000, + "battery_size_kwh" => 400000, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_operational_availability" => 1.0, + "battery_minimum_soc_fraction" => 0.0, + "battery_starting_soc_series_fraction" => results["ElectricStorage"]["soc_series_fraction"], + "critical_loads_kw" => results["ElectricLoad"]["critical_load_series_kw"]#4000*ones(8760)#p.s.electric_load.critical_loads_kw + ) + reliability_results = backup_reliability(reliability_inputs) + + #TODO: resolve bug where unlimted fuel markov portion of results goes to zero 1 timestep early + for i = 1:99#min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.01 + @test simresults["probs_of_surviving"][i] ≈ reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][i] atol=0.01 + @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_fuel_survival_by_duration"][i] atol=0.01 + end + + # Second, gen, PV, Wind, battery + reopt_inputs = JSON.parsefile("./scenarios/backup_reliability_reopt_inputs.json") + reopt_inputs["ElectricLoad"]["annual_kwh"] = 4*reopt_inputs["ElectricLoad"]["annual_kwh"] + p = REoptInputs(reopt_inputs) + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, p) + simresults = simulate_outages(results, p) + reliability_inputs = Dict( + "max_outage_duration" => 48, + "generator_operational_availability" => 1.0, + "generator_failure_to_start" => 0.0, + "generator_mean_time_to_failure" => 10000000000, + "fuel_limit" => 1000000000, + "battery_operational_availability" => 1.0, + "battery_minimum_soc_fraction" => 0.0, + "pv_operational_availability" => 1.0, + "wind_operational_availability" => 1.0 + ) + reliability_results = backup_reliability(results, p, reliability_inputs) + for i = 1:min(length(simresults["probs_of_surviving"]), reliability_inputs["max_outage_duration"]) + @test simresults["probs_of_surviving"][i] ≈ reliability_results["mean_cumulative_survival_by_duration"][i] atol=0.001 + end + end + + # Test survival with no generator decreasing and same as with generator but no fuel + reliability_inputs = Dict( + "critical_loads_kw" => 200 .* (2 .+ sin.(collect(1:8760)*2*pi/24)), + "num_generators" => 0, + "generator_size_kw" => 312.0, + "fuel_limit" => 0.0, + "max_outage_duration" => 10, + "battery_size_kw" => 428.0, + "battery_size_kwh" => 1585.0, + "num_battery_bins" => 5 + ) + reliability_results1 = backup_reliability(reliability_inputs) + reliability_inputs["generator_size_kw"] = 0 + reliability_inputs["fuel_limit"] = 1e10 + reliability_results2 = backup_reliability(reliability_inputs) + for i in 1:reliability_inputs["max_outage_duration"] + if i != 1 + @test reliability_results1["mean_fuel_survival_by_duration"][i] <= reliability_results1["mean_fuel_survival_by_duration"][i-1] + @test reliability_results1["mean_cumulative_survival_by_duration"][i] <= reliability_results1["mean_cumulative_survival_by_duration"][i-1] + end + @test reliability_results2["mean_fuel_survival_by_duration"][i] == reliability_results1["mean_fuel_survival_by_duration"][i] + end + + #test fuel limit + input_dict = JSON.parsefile("./scenarios/erp_fuel_limit_inputs.json") + results = backup_reliability(input_dict) + @test results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 1 + @test results["cumulative_survival_final_time_step"][1] ≈ 1 + + input_dict = Dict( + "critical_loads_kw" => [1,2,2,1], + "battery_starting_soc_series_fraction" => [0.75,0.75,0.75,0.75], + "max_outage_duration" => 3, + "num_generators" => 2, "generator_size_kw" => 1, + "generator_operational_availability" => 1, + "generator_failure_to_start" => 0.0, + "generator_mean_time_to_failure" => 5, + "battery_operational_availability" => 1, + "num_battery_bins" => 3, + "battery_size_kwh" => 4, + "battery_size_kw" => 1, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_minimum_soc_fraction" => 0.5) + + + #Given outage starts in time period 1 + #____________________________________ + #Outage hour 1: + #2 generators: Prob = 0.64, Battery = 2, Survived + #1 generator: Prob = 0.32, Battery = 1, Survived + #0 generator: Prob = 0.04, Battery = 0, Survived + #Survival Probability 1.0 + + #Outage hour 2: + #2 generators: Prob = 0.4096, Battery = 2, Survived + #2 gen -> 1 gen: Prob = 0.2048, Battery = 1, Survived + #1 gen -> 1 gen: Prob = 0.256, Battery = 0, Survived + #0 generators: Prob = 0.1296, Battery = -1, Failed + #Survival Probability: 0.8704 + + #Outage hour 3: + #2 generators: Prob = 0.262144, Battery = 0, Survived + #2 gen -> 2 -> 1 Prob = 0.131072, Battery = 1, Survived + #2 gen -> 1 -> 1 Prob = 0.16384, Battery = 0, Survived + #1 gen -> 1 -> 1 Prob = 0.2048, Battery = -1, Failed + #0 generators Prob = 0.238144, Battery = -1, Failed + #Survival Probability: 0.557056 + @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + #Test multiple generator types + input_dict = Dict( + "critical_loads_kw" => [1,2,2,1], + "battery_starting_soc_series_fraction" => [0.5,0.5,0.5,0.5], + "max_outage_duration" => 3, + "num_generators" => [1,1], + "generator_size_kw" => [1,1], + "generator_operational_availability" => [1,1], + "generator_failure_to_start" => [0.0, 0.0], + "generator_mean_time_to_failure" => [5, 5], + "battery_operational_availability" => 1.0, + "num_battery_bins" => 3, + "battery_size_kwh" => 2, + "battery_size_kw" => 1, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_minimum_soc_fraction" => 0) + + @test backup_reliability(input_dict)["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.557056 + + #8760 of flat load. Battery can survive 4 hours. + #Survival after 24 hours should be chance of generator surviving 20 or more hours + input_dict = Dict( + "critical_loads_kw" => 100 .* ones(8760), + "max_outage_duration" => 24, + "num_generators" => 1, + "generator_size_kw" => 100, + "generator_operational_availability" => 0.98, + "generator_failure_to_start" => 0.1, + "generator_mean_time_to_failure" => 100, + "battery_operational_availability" => 1.0, + "num_battery_bins" => 101, + "battery_size_kwh" => 400, + "battery_size_kw" => 100, + "battery_charge_efficiency" => 1, + "battery_discharge_efficiency" => 1, + "battery_minimum_soc_fraction" => 0) + + reliability_results = backup_reliability(input_dict) + @test reliability_results["unlimited_fuel_mean_cumulative_survival_by_duration"][24] ≈ (0.99^20)*(0.9*0.98) atol=0.00001 + + #More complex case of hospital load with 2 generators, PV, wind, and battery + reliability_inputs = JSON.parsefile("./scenarios/backup_reliability_inputs.json") + reliability_results = backup_reliability(reliability_inputs) + @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.858756 atol=0.0001 + @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.904242 atol=0.0001#0.833224 - # # Test gens+pv+wind+batt with 3 arg version of backup_reliability - # # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json - # # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: - # # results = run_reopt(model, p) - # # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f - # # JSON.print(f, results, 4) - # # end - # for input_key in [ - # "generator_size_kw", - # "battery_size_kw", - # "battery_size_kwh", - # "pv_size_kw", - # "wind_size_kw", - # "critical_loads_kw", - # "pv_production_factor_series", - # "wind_production_factor_series" - # ] - # delete!(reliability_inputs, input_key) - # end - # # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) - # p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") - # results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") - # reliability_results = backup_reliability(results, p, reliability_inputs) - - # @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - # @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 - # @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 - # end - - # #candidate for hot stor - # @testset verbose=true "Disaggregated Heating Loads" begin - # @testset "Process Heat Load Inputs" begin - # d = JSON.parsefile("./scenarios/electric_heater.json") - # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - # d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 - # s = Scenario(d) - # inputs = REoptInputs(s) - # @test inputs.heating_loads_kw["ProcessHeat"][1] ≈ 117.228428 atol=1.0e-3 - # end - # @testset "Separate Heat Load Results" begin - # d = JSON.parsefile("./scenarios/electric_heater.json") - # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - # d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 - # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 - # d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 - # d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] - # d["HotThermalStorage"]["max_gal"] = 0.0 - # s = Scenario(d) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, inputs) - # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 - # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 - # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 - # @test sum(results["ElectricHeater"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 - # @test sum(results["ElectricHeater"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 - # @test sum(results["ElectricHeater"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 - # end - # end - - # @testset verbose=true "Net Metering" begin - # @testset "Net Metering Limit and Wholesale" begin - # #case 1: net metering limit is met by PV - # d = JSON.parsefile("./scenarios/net_metering.json") - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, d) - # @test results["PV"]["size_kw"] ≈ 30.0 atol=1e-3 + # Test gens+pv+wind+batt with 3 arg version of backup_reliability + # Attention! REopt optimization results are presaved in erp_gens_batt_pv_wind_reopt_results.json + # If you modify backup_reliability_reopt_inputs.json, you must add this before JSON.parsefile: + # results = run_reopt(model, p) + # open("scenarios/erp_gens_batt_pv_wind_reopt_results.json","w") do f + # JSON.print(f, results, 4) + # end + for input_key in [ + "generator_size_kw", + "battery_size_kw", + "battery_size_kwh", + "pv_size_kw", + "wind_size_kw", + "critical_loads_kw", + "pv_production_factor_series", + "wind_production_factor_series" + ] + delete!(reliability_inputs, input_key) + end + # note: the wind prod series in backup_reliability_reopt_inputs.json is actually a PV profile (to in order to test a wind scenario that should give same results as an existing PV one) + p = REoptInputs("./scenarios/backup_reliability_reopt_inputs.json") + results = JSON.parsefile("./scenarios/erp_gens_batt_pv_wind_reopt_results.json") + reliability_results = backup_reliability(results, p, reliability_inputs) + + @test reliability_results["unlimited_fuel_cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + @test reliability_results["cumulative_survival_final_time_step"][1] ≈ 0.802997 atol=0.0001 + @test reliability_results["mean_cumulative_survival_final_time_step"] ≈ 0.817586 atol=0.001 + end + + #candidate for hot stor + @testset verbose=true "Disaggregated Heating Loads" begin + @testset "Process Heat Load Inputs" begin + d = JSON.parsefile("./scenarios/electric_heater.json") + d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 + s = Scenario(d) + inputs = REoptInputs(s) + @test inputs.heating_loads_kw["ProcessHeat"][1] ≈ 117.228428 atol=1.0e-3 + end + @testset "Separate Heat Load Results" begin + d = JSON.parsefile("./scenarios/electric_heater.json") + d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + d["ProcessHeatLoad"]["annual_mmbtu"] = 0.5 * 8760 + d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 + d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 + d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] + d["HotThermalStorage"]["max_gal"] = 0.0 + s = Scenario(d) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, inputs) + @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 + @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 + @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 + @test sum(results["ElectricHeater"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 + @test sum(results["ElectricHeater"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.8*4380.0 atol=0.01 + @test sum(results["ElectricHeater"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.01 + end + end + + @testset verbose=true "Net Metering" begin + @testset "Net Metering Limit and Wholesale" begin + #case 1: net metering limit is met by PV + d = JSON.parsefile("./scenarios/net_metering.json") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, d) + @test results["PV"]["size_kw"] ≈ 30.0 atol=1e-3 - # #case 2: wholesale rate is high, big-M is met - # d["ElectricTariff"]["wholesale_rate"] = 5.0 - # d["PV"]["can_wholesale"] = true - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, d) - # @test results["PV"]["size_kw"] ≈ 7440.0 atol=1e-3 #max benefit provides the upper bound + #case 2: wholesale rate is high, big-M is met + d["ElectricTariff"]["wholesale_rate"] = 5.0 + d["PV"]["can_wholesale"] = true + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, d) + @test results["PV"]["size_kw"] ≈ 7440.0 atol=1e-3 #max benefit provides the upper bound - # end - # end + end + end - # @testset "Electric Storage O&M and Self-Discharge" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/storage_om.json") + @testset "Electric Storage O&M and Self-Discharge" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/storage_om.json") - # data["ElectricStorage"]["om_cost_per_kw"] = 10 - # data["ElectricStorage"]["om_cost_per_kwh"] = 0 + data["ElectricStorage"]["om_cost_per_kw"] = 10 + data["ElectricStorage"]["om_cost_per_kwh"] = 0 - # inputs = REoptInputs(data) - # results1 = run_reopt(model, inputs) + inputs = REoptInputs(data) + results1 = run_reopt(model, inputs) - # @test results1["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 - # @test results1["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 + @test results1["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 400 atol=1 + @test results1["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 6272 atol=1 - # data["ElectricStorage"]["om_cost_per_kw"] = 10 - # data["ElectricStorage"]["om_cost_per_kwh"] = 5 + data["ElectricStorage"]["om_cost_per_kw"] = 10 + data["ElectricStorage"]["om_cost_per_kwh"] = 5 - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # inputs = REoptInputs(data) - # results2 = run_reopt(model, inputs) + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + inputs = REoptInputs(data) + results2 = run_reopt(model, inputs) - # @test results2["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 - # @test results2["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 - # @test results2["Financial"]["lcc"] ≈ results1["Financial"]["lcc"] + 12543 - 6272 atol=1 + @test results2["ElectricStorage"]["year_one_om_cost_before_tax"] ≈ 800 atol=1 + @test results2["ElectricStorage"]["lifecycle_om_cost_after_tax"] ≈ 12543 atol=1 + @test results2["Financial"]["lcc"] ≈ results1["Financial"]["lcc"] + 12543 - 6272 atol=1 - # data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # inputs = REoptInputs(data) - # results3 = run_reopt(model, inputs) - # @test sum(results2["ElectricStorage"]["storage_to_load_series_kw"]) - - # sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 - # end + data["ElectricStorage"]["self_discharge_fraction_per_timestep"] = 0.0025/24 + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + inputs = REoptInputs(data) + results3 = run_reopt(model, inputs) + @test sum(results2["ElectricStorage"]["storage_to_load_series_kw"]) - + sum(results3["ElectricStorage"]["storage_to_load_series_kw"]) ≈ 15.382 atol=0.01 + end - # @testset "Heating loads and addressable load fraction" begin - # # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, "./scenarios/thermal_load.json") + @testset "Heating loads and addressable load fraction" begin + # Default LargeOffice CRB with SpaceHeatingLoad and DomesticHotWaterLoad are served by ExistingBoiler + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, "./scenarios/thermal_load.json") - # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 12904 - - # # Hourly fuel load inputs with addressable_load_fraction are served as expected - # data = JSON.parsefile("./scenarios/thermal_load.json") - - # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 - # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) - # data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 - # data["ProcessHeatLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.3], 8760) - # data["ProcessHeatLoad"]["addressable_load_fraction"] = 0.7 - - # s = Scenario(data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, inputs) - # @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8 + 0.3 * 0.7) atol = 1.0 - - # # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load - # data = JSON.parsefile("./scenarios/thermal_load.json") - # data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) - # data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) - # data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) - # data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) - # data["ProcessHeatLoad"]["monthly_mmbtu"] = repeat([150], 12) - # data["ProcessHeatLoad"]["addressable_load_fraction"] = repeat([0.7], 12) - - # # Assuming Scenario and REoptInputs are defined functions/classes in your code - # s = Scenario(data) - # inputs = REoptInputs(s) - - # dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency - # space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency - # process_thermal_load_expected = sum(data["ProcessHeatLoad"]["monthly_mmbtu"] .* data["ProcessHeatLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency - - # @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) - # @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) - # @test round(sum(s.process_heat_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(process_thermal_load_expected) - # end + @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 12904 + + # Hourly fuel load inputs with addressable_load_fraction are served as expected + data = JSON.parsefile("./scenarios/thermal_load.json") + + data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + data["DomesticHotWaterLoad"]["addressable_load_fraction"] = 0.6 + data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.5], 8760) + data["SpaceHeatingLoad"]["addressable_load_fraction"] = 0.8 + data["ProcessHeatLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([0.3], 8760) + data["ProcessHeatLoad"]["addressable_load_fraction"] = 0.7 + + s = Scenario(data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, inputs) + @test round(results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"], digits=0) ≈ 8760 * (0.5 * 0.6 + 0.5 * 0.8 + 0.3 * 0.7) atol = 1.0 + + # Monthly fuel load input with addressable_load_fraction is processed to expected thermal load + data = JSON.parsefile("./scenarios/thermal_load.json") + data["DomesticHotWaterLoad"]["monthly_mmbtu"] = repeat([100], 12) + data["DomesticHotWaterLoad"]["addressable_load_fraction"] = repeat([0.6], 12) + data["SpaceHeatingLoad"]["monthly_mmbtu"] = repeat([200], 12) + data["SpaceHeatingLoad"]["addressable_load_fraction"] = repeat([0.8], 12) + data["ProcessHeatLoad"]["monthly_mmbtu"] = repeat([150], 12) + data["ProcessHeatLoad"]["addressable_load_fraction"] = repeat([0.7], 12) + + # Assuming Scenario and REoptInputs are defined functions/classes in your code + s = Scenario(data) + inputs = REoptInputs(s) + + dhw_thermal_load_expected = sum(data["DomesticHotWaterLoad"]["monthly_mmbtu"] .* data["DomesticHotWaterLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency + space_thermal_load_expected = sum(data["SpaceHeatingLoad"]["monthly_mmbtu"] .* data["SpaceHeatingLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency + process_thermal_load_expected = sum(data["ProcessHeatLoad"]["monthly_mmbtu"] .* data["ProcessHeatLoad"]["addressable_load_fraction"]) * s.existing_boiler.efficiency + + @test round(sum(s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(dhw_thermal_load_expected) + @test round(sum(s.space_heating_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(space_thermal_load_expected) + @test round(sum(s.process_heat_load.loads_kw) / REopt.KWH_PER_MMBTU) ≈ sum(process_thermal_load_expected) + end - # @testset verbose=true "CHP" begin - # @testset "CHP Sizing" begin - # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - # data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") - # s = Scenario(data_sizing) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # results = run_reopt(m, inputs) - - # @test round(results["CHP"]["size_kw"], digits=0) ≈ 400.0 atol=50.0 - # @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 rtol=1.0e-2 - # end + @testset verbose=true "CHP" begin + @testset "CHP Sizing" begin + # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + data_sizing = JSON.parsefile("./scenarios/chp_sizing.json") + s = Scenario(data_sizing) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt(m, inputs) + + @test round(results["CHP"]["size_kw"], digits=0) ≈ 400.0 atol=50.0 + @test round(results["Financial"]["lcc"], digits=0) ≈ 1.3476e7 rtol=1.0e-2 + end - # @testset "CHP Cost Curve and Min Allowable Size" begin - # # Fixed size CHP with cost curve, no unavailability_periods - # data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") - # data_cost_curve["CHP"] = Dict() - # data_cost_curve["CHP"]["prime_mover"] = "recip_engine" - # data_cost_curve["CHP"]["size_class"] = 1 - # data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 - # data_cost_curve["CHP"]["min_kw"] = 0 - # data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 - # data_cost_curve["CHP"]["max_kw"] = 555.51 - # data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 - # data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] - # data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] - - # data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 - # data_cost_curve["CHP"]["macrs_option_years"] = 0 - # data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 - # data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 - - # expected_x = data_cost_curve["CHP"]["min_allowable_kw"] - # cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] - # cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] - # slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) - # init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope - # lifecycle_capex_chp_expected = init_capex_chp_expected - - # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - # [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) - - # #PV - # data_cost_curve["PV"] = Dict() - # data_cost_curve["PV"]["min_kw"] = 1500 - # data_cost_curve["PV"]["max_kw"] = 1500 - # data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 - # data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 - # data_cost_curve["PV"]["macrs_option_years"] = 0 - # data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 - # data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 - - # init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] - # lifecycle_capex_pv_expected = init_capex_pv_expected - - # REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], - # [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) - - # s = Scenario(data_cost_curve) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) - - # init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected - # lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected - - # init_capex_total = results["Financial"]["initial_capital_costs"] - # lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] - - - # # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect - # @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected - # @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected - - # # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero - # @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 - # end + @testset "CHP Cost Curve and Min Allowable Size" begin + # Fixed size CHP with cost curve, no unavailability_periods + data_cost_curve = JSON.parsefile("./scenarios/chp_sizing.json") + data_cost_curve["CHP"] = Dict() + data_cost_curve["CHP"]["prime_mover"] = "recip_engine" + data_cost_curve["CHP"]["size_class"] = 1 + data_cost_curve["CHP"]["fuel_cost_per_mmbtu"] = 8.0 + data_cost_curve["CHP"]["min_kw"] = 0 + data_cost_curve["CHP"]["min_allowable_kw"] = 555.5 + data_cost_curve["CHP"]["max_kw"] = 555.51 + data_cost_curve["CHP"]["installed_cost_per_kw"] = 1800.0 + data_cost_curve["CHP"]["installed_cost_per_kw"] = [2300.0, 1800.0, 1500.0] + data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] = [100.0, 300.0, 1140.0] + + data_cost_curve["CHP"]["federal_itc_fraction"] = 0.1 + data_cost_curve["CHP"]["macrs_option_years"] = 0 + data_cost_curve["CHP"]["macrs_bonus_fraction"] = 0.0 + data_cost_curve["CHP"]["macrs_itc_reduction"] = 0.0 + + expected_x = data_cost_curve["CHP"]["min_allowable_kw"] + cap_cost_y = data_cost_curve["CHP"]["installed_cost_per_kw"] + cap_cost_x = data_cost_curve["CHP"]["tech_sizes_for_cost_curve"] + slope = (cap_cost_x[3] * cap_cost_y[3] - cap_cost_x[2] * cap_cost_y[2]) / (cap_cost_x[3] - cap_cost_x[2]) + init_capex_chp_expected = cap_cost_x[2] * cap_cost_y[2] + (expected_x - cap_cost_x[2]) * slope + lifecycle_capex_chp_expected = init_capex_chp_expected - + REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + [0, init_capex_chp_expected * data_cost_curve["CHP"]["federal_itc_fraction"]]) + + #PV + data_cost_curve["PV"] = Dict() + data_cost_curve["PV"]["min_kw"] = 1500 + data_cost_curve["PV"]["max_kw"] = 1500 + data_cost_curve["PV"]["installed_cost_per_kw"] = 1600 + data_cost_curve["PV"]["federal_itc_fraction"] = 0.26 + data_cost_curve["PV"]["macrs_option_years"] = 0 + data_cost_curve["PV"]["macrs_bonus_fraction"] = 0.0 + data_cost_curve["PV"]["macrs_itc_reduction"] = 0.0 + + init_capex_pv_expected = data_cost_curve["PV"]["max_kw"] * data_cost_curve["PV"]["installed_cost_per_kw"] + lifecycle_capex_pv_expected = init_capex_pv_expected - + REopt.npv(data_cost_curve["Financial"]["offtaker_discount_rate_fraction"], + [0, init_capex_pv_expected * data_cost_curve["PV"]["federal_itc_fraction"]]) + + s = Scenario(data_cost_curve) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) + + init_capex_total_expected = init_capex_chp_expected + init_capex_pv_expected + lifecycle_capex_total_expected = lifecycle_capex_chp_expected + lifecycle_capex_pv_expected + + init_capex_total = results["Financial"]["initial_capital_costs"] + lifecycle_capex_total = results["Financial"]["initial_capital_costs_after_incentives"] + + + # Check initial CapEx (pre-incentive/tax) and life cycle CapEx (post-incentive/tax) cost with expect + @test init_capex_total_expected ≈ init_capex_total atol=0.0001*init_capex_total_expected + @test lifecycle_capex_total_expected ≈ lifecycle_capex_total atol=0.0001*lifecycle_capex_total_expected + + # Test CHP.min_allowable_kw - the size would otherwise be ~100 kW less by setting min_allowable_kw to zero + @test results["CHP"]["size_kw"] ≈ data_cost_curve["CHP"]["min_allowable_kw"] atol=0.1 + end - # @testset "CHP Unavailability and Outage" begin - # """ - # Validation to ensure that: - # 1) CHP meets load during outage without exporting - # 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) - # 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) - # 4) CHP min_turn_down_fraction is ignored during an outage - # 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage - # 6) Unavailability intervals that intersect with grid-outages get ignored - # 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production - # """ - # # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods - # data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") - - # # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period - # data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), - # ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), - # Dict([("month", 1), ("start_week_of_month", 2), - # ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] - - # # Manually doing the math from the unavailability defined above - # unavail_1_start = 24 + 1 - # unavail_1_end = unavail_1_start + 8 - 1 - # unavail_2_start = 24*3 + 9 - # unavail_2_end = unavail_2_start + 8 - 1 + @testset "CHP Unavailability and Outage" begin + """ + Validation to ensure that: + 1) CHP meets load during outage without exporting + 2) CHP never exports if chp.can_wholesale and chp.can_net_meter inputs are False (default) + 3) CHP does not "curtail", i.e. send power to a load bank when chp.can_curtail is False (default) + 4) CHP min_turn_down_fraction is ignored during an outage + 5) Cooling tech production gets zeroed out during the outage period because we ignore the cooling load balance for outage + 6) Unavailability intervals that intersect with grid-outages get ignored + 7) Unavailability intervals that do not intersect with grid-outages result in no CHP production + """ + # Sizing CHP with non-constant efficiency, no cost curve, no unavailability_periods + data = JSON.parsefile("./scenarios/chp_unavailability_outage.json") + + # Add unavailability periods that 1) intersect (ignored) and 2) don't intersect with outage period + data["CHP"]["unavailability_periods"] = [Dict([("month", 1), ("start_week_of_month", 2), + ("start_day_of_week", 1), ("start_hour", 1), ("duration_hours", 8)]), + Dict([("month", 1), ("start_week_of_month", 2), + ("start_day_of_week", 3), ("start_hour", 9), ("duration_hours", 8)])] + + # Manually doing the math from the unavailability defined above + unavail_1_start = 24 + 1 + unavail_1_end = unavail_1_start + 8 - 1 + unavail_2_start = 24*3 + 9 + unavail_2_end = unavail_2_start + 8 - 1 - # # Specify the CHP.min_turn_down_fraction which is NOT used during an outage - # data["CHP"]["min_turn_down_fraction"] = 0.5 - # # Specify outage period; outage time_steps are 1-indexed - # outage_start = unavail_1_start - # data["ElectricUtility"]["outage_start_time_step"] = outage_start - # outage_end = unavail_1_end - # data["ElectricUtility"]["outage_end_time_step"] = outage_end - # data["ElectricLoad"]["critical_load_fraction"] = 0.25 - - # s = Scenario(data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) - - # tot_elec_load = results["ElectricLoad"]["load_series_kw"] - # chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] - # chp_to_load = results["CHP"]["electric_to_load_series_kw"] - # chp_export = results["CHP"]["electric_to_grid_series_kw"] - # cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] - - # # The values compared to the expected values - # @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 - # critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] - # @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 - # @test sum(chp_export) == 0.0 - # @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) - # @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 - # @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 - # end + # Specify the CHP.min_turn_down_fraction which is NOT used during an outage + data["CHP"]["min_turn_down_fraction"] = 0.5 + # Specify outage period; outage time_steps are 1-indexed + outage_start = unavail_1_start + data["ElectricUtility"]["outage_start_time_step"] = outage_start + outage_end = unavail_1_end + data["ElectricUtility"]["outage_end_time_step"] = outage_end + data["ElectricLoad"]["critical_load_fraction"] = 0.25 + + s = Scenario(data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) + + tot_elec_load = results["ElectricLoad"]["load_series_kw"] + chp_total_elec_prod = results["CHP"]["electric_production_series_kw"] + chp_to_load = results["CHP"]["electric_to_load_series_kw"] + chp_export = results["CHP"]["electric_to_grid_series_kw"] + cooling_elec_consumption = results["ExistingChiller"]["electric_consumption_series_kw"] + + # The values compared to the expected values + @test sum([(chp_to_load[i] - tot_elec_load[i]*data["ElectricLoad"]["critical_load_fraction"]) for i in outage_start:outage_end]) ≈ 0.0 atol=0.001 + critical_load = tot_elec_load[outage_start:outage_end] * data["ElectricLoad"]["critical_load_fraction"] + @test sum(chp_to_load[outage_start:outage_end]) ≈ sum(critical_load) atol=0.1 + @test sum(chp_export) == 0.0 + @test sum(chp_total_elec_prod) ≈ sum(chp_to_load) atol=1.0e-5*sum(chp_total_elec_prod) + @test sum(cooling_elec_consumption[outage_start:outage_end]) == 0.0 + @test sum(chp_total_elec_prod[unavail_2_start:unavail_2_end]) == 0.0 + end - # @testset "CHP Supplementary firing and standby" begin - # """ - # Test to ensure that supplementary firing and standby charges work as intended. The thermal and - # electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a - # similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower - # value than that of the supplementary firing. The test also ensures that demand charges are - # correctly calculated when CHP is and is not allowed to reduce demand charges. - # """ - # data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") - # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 - # data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) - # data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - # data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) - # #part 1: supplementary firing not used when less efficient than the boiler and expensive - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(m1, inputs) - # @test results["CHP"]["size_kw"] == 800 - # @test results["CHP"]["size_supplemental_firing_kw"] == 0 - # @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 - # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 - # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 - # @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] - # @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - # @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] - - # #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP - # data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 - # data["CHP"]["reduces_demand_charges"] = false - # data["ExistingBoiler"]["efficiency"] = 0.85 - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(m2, inputs) - # @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 - # @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 - # @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 - # end - - # @testset "CHP to Waste Heat" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d = JSON.parsefile("./scenarios/chp_waste.json") - # results = run_reopt(m, d) - # @test sum(results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"]) ≈ 4174.455 atol=1e-3 - # end - # end + @testset "CHP Supplementary firing and standby" begin + """ + Test to ensure that supplementary firing and standby charges work as intended. The thermal and + electrical loads are constant, and the CHP system size is fixed; the supplementary firing has a + similar cost to the boiler and is purcahsed and used when the boiler efficiency is set to a lower + value than that of the supplementary firing. The test also ensures that demand charges are + correctly calculated when CHP is and is not allowed to reduce demand charges. + """ + data = JSON.parsefile("./scenarios/chp_supplementary_firing.json") + data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10000 + data["ElectricLoad"]["loads_kw"] = repeat([800.0], 8760) + data["DomesticHotWaterLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + data["SpaceHeatingLoad"]["fuel_loads_mmbtu_per_hour"] = repeat([6.0], 8760) + #part 1: supplementary firing not used when less efficient than the boiler and expensive + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(m1, inputs) + @test results["CHP"]["size_kw"] == 800 + @test results["CHP"]["size_supplemental_firing_kw"] == 0 + @test results["CHP"]["annual_electric_production_kwh"] ≈ 800*8760 rtol=1e-5 + @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 800*(0.4418/0.3573)*8760/293.07107 rtol=1e-5 + @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] == 0 + @test results["HeatingLoad"]["annual_calculated_total_heating_thermal_load_mmbtu"] == 12.0 * 8760 * data["ExistingBoiler"]["efficiency"] + @test results["HeatingLoad"]["annual_calculated_dhw_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + @test results["HeatingLoad"]["annual_calculated_space_heating_thermal_load_mmbtu"] == 6.0 * 8760 * data["ExistingBoiler"]["efficiency"] + + #part 2: supplementary firing used when more efficient than the boiler and low-cost; demand charges not reduced by CHP + data["CHP"]["supplementary_firing_capital_cost_per_kw"] = 10 + data["CHP"]["reduces_demand_charges"] = false + data["ExistingBoiler"]["efficiency"] = 0.85 + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(m2, inputs) + @test results["CHP"]["size_supplemental_firing_kw"] ≈ 321.71 atol=0.1 + @test results["CHP"]["annual_thermal_production_mmbtu"] ≈ 149136.6 rtol=1e-5 + @test results["ElectricTariff"]["lifecycle_demand_cost_after_tax"] ≈ 5212.7 rtol=1e-5 + end + + @testset "CHP to Waste Heat" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d = JSON.parsefile("./scenarios/chp_waste.json") + results = run_reopt(m, d) + @test sum(results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"]) ≈ 4174.455 atol=1e-3 + end + end - # @testset verbose=true "FlexibleHVAC" begin + @testset verbose=true "FlexibleHVAC" begin - # @testset "Single RC Model heating only" begin - # #= - # Single RC model: - # 1 state/control node - # 2 inputs: Ta and Qheat - # A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] - # NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here + @testset "Single RC Model heating only" begin + #= + Single RC model: + 1 state/control node + 2 inputs: Ta and Qheat + A = [1/(RC)], B = [1/(RC) 1/C], u = [Ta; Q] + NOTE exogenous_inputs (u) allows for parasitic heat, but it is input as zeros here - # We start with no technologies except ExistingBoiler and ExistingChiller. - # FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) - # or if there is a time-varying fuel and/or electricity cost - # (and the FlexibleHVAC installed_cost is less than the achievable savings). - # =# + We start with no technologies except ExistingBoiler and ExistingChiller. + FlexibleHVAC is only worth purchasing if its cost is neglible (i.e. below the lcc_bau * MIPTOL) + or if there is a time-varying fuel and/or electricity cost + (and the FlexibleHVAC installed_cost is less than the achievable savings). + =# - # # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC - # pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); - # R = 0.00025 # K/kW - # C = 1e5 # kJ/K - # # the starting scenario has flat fuel and electricty costs - # d = JSON.parsefile("./scenarios/thermal_load.json"); - # A = reshape([-1/(R*C)], 1,1) - # B = [1/(R*C) 1/C] - # u = [tamb zeros(8760)]'; - # d["FlexibleHVAC"] = Dict( - # "control_node" => 1, - # "initial_temperatures" => [21], - # "temperature_upper_bound_degC" => 22.0, - # "temperature_lower_bound_degC" => 19.8, - # "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it - # "system_matrix" => A, - # "input_matrix" => B, - # "exogenous_inputs" => u - # ) + # Austin, TX -> existing_chiller and existing_boiler added with FlexibleHVAC + pf, tamb = REopt.call_pvwatts_api(30.2672, -97.7431); + R = 0.00025 # K/kW + C = 1e5 # kJ/K + # the starting scenario has flat fuel and electricty costs + d = JSON.parsefile("./scenarios/thermal_load.json"); + A = reshape([-1/(R*C)], 1,1) + B = [1/(R*C) 1/C] + u = [tamb zeros(8760)]'; + d["FlexibleHVAC"] = Dict( + "control_node" => 1, + "initial_temperatures" => [21], + "temperature_upper_bound_degC" => 22.0, + "temperature_lower_bound_degC" => 19.8, + "installed_cost" => 300.0, # NOTE cost must be more then the MIPTOL * LCC 5e-5 * 5.79661e6 ≈ 290 to make FlexibleHVAC not worth it + "system_matrix" => A, + "input_matrix" => B, + "exogenous_inputs" => u + ) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # # @test r["Financial"]["npv"] == 0 + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # @test r["Financial"]["npv"] == 0 - # # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical - # # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC - # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # all of the savings are from the ExistingBoiler fuel costs - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true - # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # put in a time varying fuel cost, which should make purchasing the FlexibleHVAC system economical + # with flat ElectricTariff the ExistingChiller does not benefit from FlexibleHVAC + d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = rand(Float64, (8760))*(50-5).+5; + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # all of the savings are from the ExistingBoiler fuel costs + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === true + # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # @test fuel_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased - # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # # @test r["Financial"]["npv"] == 0 + # now increase the FlexibleHVAC installed_cost to the fuel costs savings + 100 and expect that the FlexibleHVAC is not purchased + # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + 100 + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # @test r["Financial"]["npv"] == 0 - # # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently - # d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") + # add TOU ElectricTariff and expect to benefit from using ExistingChiller intelligently + d["ElectricTariff"] = Dict("urdb_label" => "5ed6c1a15457a3367add15ae") - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + - # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - - # # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - - # # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] + # elec_cost_savings = r["ElectricTariff"]["lifecycle_demand_cost_after_tax_bau"] + + # r["ElectricTariff"]["lifecycle_energy_cost_after_tax_bau"] - + # r["ElectricTariff"]["lifecycle_demand_cost_after_tax"] - + # r["ElectricTariff"]["lifecycle_energy_cost_after_tax"] - # # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] - # # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 + # fuel_cost_savings = r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax_bau"] - r["ExistingBoiler"]["lifecycle_fuel_cost_after_tax"] + # @test fuel_cost_savings + elec_cost_savings - d["FlexibleHVAC"]["installed_cost"] ≈ r["Financial"]["npv"] atol=0.1 - # # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings - # # + 100 and expect that the FlexibleHVAC is not purchased - # # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) - # # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false - # # @test r["Financial"]["npv"] == 0 + # now increase the FlexibleHVAC installed_cost to the fuel costs savings + elec_cost_savings + # + 100 and expect that the FlexibleHVAC is not purchased + # d["FlexibleHVAC"]["installed_cost"] = fuel_cost_savings + elec_cost_savings + 100 + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test (occursin("not supported by the solver", string(r["Messages"]["errors"])) || occursin("REopt scenarios solved either with errors or non-optimal solutions", string(r["Messages"]["errors"]))) + # @test Meta.parse(r["FlexibleHVAC"]["purchased"]) === false + # @test r["Financial"]["npv"] == 0 - # end - # end - - # #= - # add a time-of-export rate that is greater than retail rate for the month of January, - # check to make sure that PV does NOT export unless the site load is met first for the month of January. - # =# - # @testset "Do not allow_simultaneous_export_import" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/monthly_rate.json") - - # # create wholesale_rate with compensation in January > retail rate - # jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] - # data["ElectricTariff"]["wholesale_rate"] = - # append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) - # data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) - # data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) - - # s = Scenario(data) - # inputs = REoptInputs(s) - # results = run_reopt(model, inputs) - - # @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) - # if results["PV"]["electric_to_grid_series_kw"][i] > 0) - # end - - # @testset "Solar and ElectricStorage w/BAU and degradation" begin - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # d = JSON.parsefile("scenarios/pv_storage.json"); - # d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) - # results = run_reopt([m1,m2], d) - - # @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 - # @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 - # @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 - # @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 - # @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 - # @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 - # proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - - # results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) - # @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 - - # # compare avg soc with and without degradation, - # # using default augmentation battery maintenance strategy - # avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 - # d["ElectricStorage"]["model_degradation"] = true - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r_degr = run_reopt(m, d) - # avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 - # @test avg_soc_no_degr > avg_soc_degr - - # # test the replacement strategy - # d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # set_optimizer_attribute(m, "mip_rel_gap", 0.01) - # r = run_reopt(m, d) - # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # # #optimal SOH at end of horizon is 80\% to prevent any replacement - # # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 - # # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 - # # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match - # # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 - # # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 - # # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 - - # # test minimum_avg_soc_fraction - # d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # set_optimizer_attribute(m, "mip_rel_gap", 0.01) - # r = run_reopt(m, d) - # @test occursin("not supported by the solver", string(r["Messages"]["errors"])) - # # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 - # end - - # @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # p = REoptInputs("./scenarios/generator.json") - # results = run_reopt([m1,m2], p) - # @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 - # @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + - # sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 - # @test results["ElectricLoad"]["bau_critical_load_met"] == false - # @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 - - # simresults = simulate_outages(results, p) - # @test simresults["resilience_hours_max"] == 11 - # end - - # #candidate for outages - # @testset "Minimize Unserved Load" begin - # d = JSON.parsefile("./scenarios/outage.json") - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # results = run_reopt(m, d) + end + end + + #= + add a time-of-export rate that is greater than retail rate for the month of January, + check to make sure that PV does NOT export unless the site load is met first for the month of January. + =# + @testset "Do not allow_simultaneous_export_import" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/monthly_rate.json") + + # create wholesale_rate with compensation in January > retail rate + jan_rate = data["ElectricTariff"]["monthly_energy_rates"][1] + data["ElectricTariff"]["wholesale_rate"] = + append!(repeat([jan_rate + 0.1], 31 * 24), repeat([0.0], 8760 - 31*24)) + data["ElectricTariff"]["monthly_demand_rates"] = repeat([0], 12) + data["ElectricUtility"] = Dict("allow_simultaneous_export_import" => false) + + s = Scenario(data) + inputs = REoptInputs(s) + results = run_reopt(model, inputs) + + @test all(x == 0.0 for (i,x) in enumerate(results["ElectricUtility"]["electric_to_load_series_kw"][1:744]) + if results["PV"]["electric_to_grid_series_kw"][i] > 0) + end + + @testset "Solar and ElectricStorage w/BAU and degradation" begin + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + d = JSON.parsefile("scenarios/pv_storage.json"); + d["Settings"] = Dict{Any,Any}("add_soc_incentive" => false) + results = run_reopt([m1,m2], d) + + @test results["PV"]["size_kw"] ≈ 216.6667 atol=0.01 + @test results["PV"]["lcoe_per_kwh"] ≈ 0.0468 atol = 0.001 + @test results["Financial"]["lcc"] ≈ 1.239179e7 rtol=1e-5 + @test results["Financial"]["lcc_bau"] ≈ 12766397 rtol=1e-5 + @test results["ElectricStorage"]["size_kw"] ≈ 49.02 atol=0.1 + @test results["ElectricStorage"]["size_kwh"] ≈ 83.3 atol=0.1 + proforma_npv = REopt.npv(results["Financial"]["offtaker_annual_free_cashflows"] - + results["Financial"]["offtaker_annual_free_cashflows_bau"], 0.081) + @test results["Financial"]["npv"] ≈ proforma_npv rtol=0.0001 + + # compare avg soc with and without degradation, + # using default augmentation battery maintenance strategy + avg_soc_no_degr = sum(results["ElectricStorage"]["soc_series_fraction"]) / 8760 + d["ElectricStorage"]["model_degradation"] = true + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r_degr = run_reopt(m, d) + avg_soc_degr = sum(r_degr["ElectricStorage"]["soc_series_fraction"]) / 8760 + @test avg_soc_no_degr > avg_soc_degr + + # test the replacement strategy + d["ElectricStorage"]["degradation"] = Dict("maintenance_strategy" => "replacement") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + set_optimizer_attribute(m, "mip_rel_gap", 0.01) + r = run_reopt(m, d) + @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # #optimal SOH at end of horizon is 80\% to prevent any replacement + # @test sum(value.(m[:bmth_BkWh])) ≈ 0 atol=0.1 + # # @test r["ElectricStorage"]["maintenance_cost"] ≈ 2972.66 atol=0.01 + # # the maintenance_cost comes out to 3004.39 on Actions, so we test the LCC since it should match + # @test r["Financial"]["lcc"] ≈ 1.240096e7 rtol=0.01 + # @test last(value.(m[:SOH])) ≈ 66.633 rtol=0.01 + # @test r["ElectricStorage"]["size_kwh"] ≈ 83.29 rtol=0.01 + + # test minimum_avg_soc_fraction + d["ElectricStorage"]["minimum_avg_soc_fraction"] = 0.72 + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + set_optimizer_attribute(m, "mip_rel_gap", 0.01) + r = run_reopt(m, d) + @test occursin("not supported by the solver", string(r["Messages"]["errors"])) + # @test round(sum(r["ElectricStorage"]["soc_series_fraction"]), digits=2) / 8760 >= 0.7199 + end + + @testset "Outage with Generator, outage simulator, BAU critical load outputs" begin + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + p = REoptInputs("./scenarios/generator.json") + results = run_reopt([m1,m2], p) + @test results["Generator"]["size_kw"] ≈ 9.55 atol=0.01 + @test (sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 1:9) + + sum(results["Generator"]["electric_to_load_series_kw"][i] for i in 13:8760)) == 0 + @test results["ElectricLoad"]["bau_critical_load_met"] == false + @test results["ElectricLoad"]["bau_critical_load_met_time_steps"] == 0 + + simresults = simulate_outages(results, p) + @test simresults["resilience_hours_max"] == 11 + end + + #candidate for outages + @testset "Minimize Unserved Load" begin + d = JSON.parsefile("./scenarios/outage.json") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt(m, d) - # @test results["Outages"]["expected_outage_cost"] ≈ 0 atol=0.1 - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 atol=0.1 - # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - # @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 - # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - # @test value(m[:binMGStorageUsed]) ≈ 1 + @test results["Outages"]["expected_outage_cost"] ≈ 0 atol=0.1 + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 0 atol=0.1 + @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + @test value(m[:binMGTechUsed]["CHP"]) ≈ 1 + @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + @test value(m[:binMGStorageUsed]) ≈ 1 - # # Increase cost of microgrid upgrade and PV Size, PV not used and some load not met - # d["Financial"]["microgrid_upgrade_cost_fraction"] = 0.3 - # d["PV"]["min_kw"] = 200.0 - # d["PV"]["max_kw"] = 200.0 - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # results = run_reopt(m, d) - # @test value(m[:binMGTechUsed]["PV"]) ≈ 0 - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 24.16 atol=0.1 - - # #= - # Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 - # - should meet 168 kWh in each outage such that the total unserved load is 12 kWh - # =# - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 - - # # testing dvUnserved load, which would output 100 kWh for this scenario before output fix - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") - # @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 - # @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) - # @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 - - # # Scenario with generator, PV, electric storage - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") - # @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 - # @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 - - # # Scenario with generator, PV, wind, electric storage - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") - # @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 - # @test value(m[:binMGTechUsed]["PV"]) ≈ 1 - # @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 - # @test results["Outages"]["expected_outage_cost"] ≈ 1.296319791276051e6 atol=1.0 - # @test results["Financial"]["lcc"] ≈ 4.8046446434e6 rtol=0.001 - - # end - - # @testset "Outages with Wind and supply-to-load no greater than critical load" begin - # input_data = JSON.parsefile("./scenarios/wind_outages.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # results = run_reopt([m1,m2], inputs) + # Increase cost of microgrid upgrade and PV Size, PV not used and some load not met + d["Financial"]["microgrid_upgrade_cost_fraction"] = 0.3 + d["PV"]["min_kw"] = 200.0 + d["PV"]["max_kw"] = 200.0 + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt(m, d) + @test value(m[:binMGTechUsed]["PV"]) ≈ 0 + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 24.16 atol=0.1 + + #= + Scenario with $0.001/kWh value_of_lost_load_per_kwh, 12x169 hour outages, 1kW load/hour, and min_resil_time_steps = 168 + - should meet 168 kWh in each outage such that the total unserved load is 12 kWh + =# + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/nogridcost_minresilhours.json") + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 12 + + # testing dvUnserved load, which would output 100 kWh for this scenario before output fix + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/nogridcost_multiscenario.json") + @test sum(results["Outages"]["unserved_load_per_outage_kwh"]) ≈ 60 + @test results["Outages"]["expected_outage_cost"] ≈ 485.43270 atol=1.0e-5 #avg duration (3h) * load per time step (10) * present worth factor (16.18109) + @test results["Outages"]["max_outage_cost_per_outage_duration"][1] ≈ 161.8109 atol=1.0e-5 + + # Scenario with generator, PV, electric storage + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outages_gen_pv_stor.json") + @test results["Outages"]["expected_outage_cost"] ≈ 3.54476923e6 atol=10 + @test results["Financial"]["lcc"] ≈ 8.6413594727e7 rtol=0.001 + + # Scenario with generator, PV, wind, electric storage + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt(m, "./scenarios/outages_gen_pv_wind_stor.json") + @test value(m[:binMGTechUsed]["Generator"]) ≈ 1 + @test value(m[:binMGTechUsed]["PV"]) ≈ 1 + @test value(m[:binMGTechUsed]["Wind"]) ≈ 1 + @test results["Outages"]["expected_outage_cost"] ≈ 1.296319791276051e6 atol=1.0 + @test results["Financial"]["lcc"] ≈ 4.8046446434e6 rtol=0.001 + + end + + @testset "Outages with Wind and supply-to-load no greater than critical load" begin + input_data = JSON.parsefile("./scenarios/wind_outages.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + results = run_reopt([m1,m2], inputs) - # # Check that supply-to-load is equal to critical load during outages, including wind - # supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] - # supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] - # critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] - # check = .≈(supply_to_load, critical_load, atol=0.001) - # @test !(0 in check) - - # # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw - # @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) - # end - - # @testset "Multiple Sites" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # ps = [ - # REoptInputs("./scenarios/pv_storage.json"), - # REoptInputs("./scenarios/monthly_rate.json"), - # ]; - # results = run_reopt(m, ps) - # @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 - # end - - # @testset "MPC" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_mpc(model, "./scenarios/mpc.json") - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 - # @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 - # @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 - # grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] - # # the grid draw limit in the 10th time step is set to 90 - # # without the 90 limit the grid draw is 98 in the 10th time step - # @test grid_draw[10] <= 90 - # end - - # @testset verbose=true "Rate Structures" begin - - # @testset "Tiered Energy" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, "./scenarios/tiered_energy_rate.json") - # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 - # @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 - # end - - # @testset "Lookback Demand Charges" begin - # # 1. Testing rate from URDB - # data = JSON.parsefile("./scenarios/lookback_rate.json") - # # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand - # # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month - # data["ElectricLoad"]["loads_kw"] = ones(8760) - # data["ElectricLoad"]["loads_kw"][8] = 100.0 - # inputs = REoptInputs(Scenario(data)) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, inputs) - # # Expected result is 100 kW demand for January, 35% of that for all other months and - # # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) - - # # 2. Testing custom rate from user with demand_lookback_months - # d = JSON.parsefile("./scenarios/lookback_rate.json") - # d["ElectricTariff"] = Dict() - # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - # d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec - # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, REoptInputs(Scenario(d))) - - # monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June - # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - # # 3. Testing custom rate from user with demand_lookback_range - # d = JSON.parsefile("./scenarios/lookback_rate.json") - # d["ElectricTariff"] = Dict() - # d["ElectricTariff"]["demand_lookback_percent"] = 0.75 - # d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] - # d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak - # d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) - # d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) - # d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak - # d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] - # d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 - # d["ElectricTariff"]["demand_lookback_range"] = 6 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, REoptInputs(Scenario(d))) - - # monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] - # expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) - # @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost - - # end - - # @testset "Blended tariff" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/no_techs.json") - # @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 - # end - - # @testset "Coincident Peak Charges" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, "./scenarios/coincident_peak.json") - # @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 - # end - - # @testset "URDB sell rate" begin - # #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) - # =# - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # p = REoptInputs("./scenarios/URDB_customer_generation.json") - # results = run_reopt(model, p) - # @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] - # end - - # @testset "Custom URDB with Sub-Hourly" begin - # # Avoid excessive JuMP warning messages about += with Expressions - # logger = SimpleLogger() - # with_logger(logger) do - # # Testing a 15-min post with a urdb_response with multiple n_energy_tiers - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) - # p = REoptInputs("./scenarios/subhourly_with_urdb.json") - # results = run_reopt(model, p) - # @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 - # @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw - # end - # end - - # @testset "Multi-tier demand and energy rates" begin - # #This test ensures that when multiple energy or demand regimes are included, that the tier limits load appropriately - # d = JSON.parsefile("./scenarios/no_techs.json") - # d["ElectricTariff"] = Dict() - # d["ElectricTariff"]["urdb_response"] = JSON.parsefile("./scenarios/multi_tier_urdb_response.json") - # s = Scenario(d) - # p = REoptInputs(s) - # @test p.s.electric_tariff.tou_demand_tier_limits[1, 1] ≈ 1.0e8 atol=1.0 - # @test p.s.electric_tariff.tou_demand_tier_limits[1, 2] ≈ 1.0e8 atol=1.0 - # @test p.s.electric_tariff.tou_demand_tier_limits[2, 1] ≈ 100.0 atol=1.0 - # @test p.s.electric_tariff.tou_demand_tier_limits[2, 2] ≈ 1.0e8 atol=1.0 - # @test p.s.electric_tariff.energy_tier_limits[1, 1] ≈ 1.0e10 atol=1.0 - # @test p.s.electric_tariff.energy_tier_limits[1, 2] ≈ 1.0e10 atol=1.0 - # @test p.s.electric_tariff.energy_tier_limits[6, 1] ≈ 20000.0 atol=1.0 - # @test p.s.electric_tariff.energy_tier_limits[6, 2] ≈ 1.0e10 atol=1.0 - # end - - # @testset "Tiered TOU Demand" begin - # data = JSON.parsefile("./scenarios/tiered_tou_demand.json") - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(model, data) - # max_demand = data["ElectricLoad"]["annual_kwh"] / 8760 - # tier1_max = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["max"] - # tier1_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["rate"] - # tier2_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][2]["rate"] - # expected_demand_charges = 12 * (tier1_max * tier1_rate + (max_demand - tier1_max) * tier2_rate) - # @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_charges atol=1 - # end - - # # # tiered monthly demand rate TODO: expected results? - # # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # # data = JSON.parsefile("./scenarios/tiered_energy_rate.json") - # # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" - # # s = Scenario(data) - # # inputs = REoptInputs(s) - # # results = run_reopt(m, inputs) - - # @testset "Non-Standard Units for Energy Rates" begin - # d = JSON.parsefile("./scenarios/no_techs.json") - # d["ElectricTariff"] = Dict( - # "urdb_label" => "6272e4ae7eb76766c247d469" - # ) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, d) - # @test occursin("URDB energy tiers have non-standard units of", string(results["Messages"])) - # end - - # end - - # @testset "EASIUR" begin - # d = JSON.parsefile("./scenarios/pv.json") - # d["Site"]["latitude"] = 30.2672 - # d["Site"]["longitude"] = -97.7431 - # scen = Scenario(d) - # @test scen.financial.NOx_grid_cost_per_tonne ≈ 5510.61 atol=0.1 - # end - - # @testset "Wind" begin - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d = JSON.parsefile("./scenarios/wind.json") - # results = run_reopt(m, d) - # @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 - # @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 - # #= - # 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 - # - both have zero curtailment - # - same energy to grid: 5,839,317 vs 5,839,322 - # - same energy to load: 4,160,683 vs 4,160,677 - # - same city: Boulder - # - same total wind prod factor - - # REopt.jl has: - # - bigger turbine: 3752 vs 3735 - # - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 - - # TODO: will these discrepancies be addressed once NMIL binaries are added? - # =# - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) - # results = run_reopt(m, d) - # @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error - # results = run_reopt(m, d) - # @test "errors" ∈ keys(results["Messages"]) - # @test length(results["Messages"]["errors"]) > 0 - # end - - # @testset "Multiple PVs" begin - # logger = SimpleLogger() - # with_logger(logger) do - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") - - # ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] - # roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] - # roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] - - # @test ground_pv["size_kw"] ≈ 15 atol=0.1 - # @test roof_west["size_kw"] ≈ 7 atol=0.1 - # @test roof_east["size_kw"] ≈ 4 atol=0.1 - # @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - # @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 - # @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 - # @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 - # @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 - # @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 - # @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 - # end - # end - - # #candidate for hot and cold stor - # @testset "Thermal Energy Storage + Absorption Chiller" begin - # model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # data = JSON.parsefile("./scenarios/thermal_storage.json") - # s = Scenario(data) - # p = REoptInputs(s) + # Check that supply-to-load is equal to critical load during outages, including wind + supply_to_load = results["Outages"]["storage_discharge_series_kw"] .+ results["Outages"]["wind_to_load_series_kw"] + supply_to_load = [supply_to_load[:,:,i][1] for i in eachindex(supply_to_load)] + critical_load = results["Outages"]["critical_loads_per_outage_series_kw"][1,1,:] + check = .≈(supply_to_load, critical_load, atol=0.001) + @test !(0 in check) + + # Check that the soc_series_fraction is the same length as the storage_discharge_series_kw + @test size(results["Outages"]["soc_series_fraction"]) == size(results["Outages"]["storage_discharge_series_kw"]) + end + + @testset "Multiple Sites" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + ps = [ + REoptInputs("./scenarios/pv_storage.json"), + REoptInputs("./scenarios/monthly_rate.json"), + ]; + results = run_reopt(m, ps) + @test results[3]["Financial"]["lcc"] + results[10]["Financial"]["lcc"] ≈ 1.2830872235e7 rtol=1e-5 + end + + @testset "MPC" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_mpc(model, "./scenarios/mpc.json") + @test maximum(r["ElectricUtility"]["to_load_series_kw"][1:15]) <= 98.0 + @test maximum(r["ElectricUtility"]["to_load_series_kw"][16:24]) <= 97.0 + @test sum(r["PV"]["to_grid_series_kw"]) ≈ 0 + grid_draw = r["ElectricUtility"]["to_load_series_kw"] .+ r["ElectricUtility"]["to_battery_series_kw"] + # the grid draw limit in the 10th time step is set to 90 + # without the 90 limit the grid draw is 98 in the 10th time step + @test grid_draw[10] <= 90 + end + + @testset verbose=true "Rate Structures" begin + + @testset "Tiered Energy" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, "./scenarios/tiered_energy_rate.json") + @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 2342.88 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 24000.0 atol=0.1 + @test results["ElectricLoad"]["annual_calculated_kwh"] ≈ 24000.0 atol=0.1 + end + + @testset "Lookback Demand Charges" begin + # 1. Testing rate from URDB + data = JSON.parsefile("./scenarios/lookback_rate.json") + # urdb_label used https://apps.openei.org/IURDB/rate/view/539f6a23ec4f024411ec8bf9#2__Demand + # has a demand charge lookback of 35% for all months with 2 different demand charges based on which month + data["ElectricLoad"]["loads_kw"] = ones(8760) + data["ElectricLoad"]["loads_kw"][8] = 100.0 + inputs = REoptInputs(Scenario(data)) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, inputs) + # Expected result is 100 kW demand for January, 35% of that for all other months and + # with 5x other $10.5/kW cold months and 6x $11.5/kW warm months + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 100 * (10.5 + 0.35*10.5*5 + 0.35*11.5*6) + + # 2. Testing custom rate from user with demand_lookback_months + d = JSON.parsefile("./scenarios/lookback_rate.json") + d["ElectricTariff"] = Dict() + d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + d["ElectricTariff"]["demand_lookback_months"] = [1,0,0,1,0,0,0,0,0,0,0,1] # Jan, April, Dec + d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, REoptInputs(Scenario(d))) + + monthly_peaks = [300,300,300,400,300,500,300,300,300,300,300,300] # 300 = 400*0.75. Sets peak in all months excpet April and June + expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + # 3. Testing custom rate from user with demand_lookback_range + d = JSON.parsefile("./scenarios/lookback_rate.json") + d["ElectricTariff"] = Dict() + d["ElectricTariff"]["demand_lookback_percent"] = 0.75 + d["ElectricLoad"]["loads_kw"] = [100 for i in range(1,8760)] + d["ElectricLoad"]["loads_kw"][22] = 200 # Jan peak + d["ElectricLoad"]["loads_kw"][2403] = 400 # April peak (Should set dvPeakDemandLookback) + d["ElectricLoad"]["loads_kw"][4088] = 500 # June peak (not in peak month lookback) + d["ElectricLoad"]["loads_kw"][8333] = 300 # Dec peak + d["ElectricTariff"]["monthly_demand_rates"] = [10,10,20,50,20,10,20,20,20,20,20,5] + d["ElectricTariff"]["blended_annual_energy_rate"] = 0.01 + d["ElectricTariff"]["demand_lookback_range"] = 6 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, REoptInputs(Scenario(d))) + + monthly_peaks = [225, 225, 225, 400, 300, 500, 375, 375, 375, 375, 375, 375] + expected_demand_cost = sum(monthly_peaks.*d["ElectricTariff"]["monthly_demand_rates"]) + @test r["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_cost + + end + + @testset "Blended tariff" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/no_techs.json") + @test results["ElectricTariff"]["year_one_energy_cost_before_tax"] ≈ 1000.0 + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ 136.99 + end + + @testset "Coincident Peak Charges" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, "./scenarios/coincident_peak.json") + @test results["ElectricTariff"]["year_one_coincident_peak_cost_before_tax"] ≈ 15.0 + end + + @testset "URDB sell rate" begin + #= The URDB contains at least one "Customer generation" tariff that only has a "sell" key in the energyratestructure (the tariff tested here) + =# + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + p = REoptInputs("./scenarios/URDB_customer_generation.json") + results = run_reopt(model, p) + @test results["PV"]["size_kw"] ≈ p.max_sizes["PV"] + end + + @testset "Custom URDB with Sub-Hourly" begin + # Avoid excessive JuMP warning messages about += with Expressions + logger = SimpleLogger() + with_logger(logger) do + # Testing a 15-min post with a urdb_response with multiple n_energy_tiers + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.1)) + p = REoptInputs("./scenarios/subhourly_with_urdb.json") + results = run_reopt(model, p) + @test length(p.s.electric_tariff.export_rates[:WHL]) ≈ 8760*4 + @test results["PV"]["size_kw"] ≈ p.s.pvs[1].existing_kw + end + end + + @testset "Multi-tier demand and energy rates" begin + #This test ensures that when multiple energy or demand regimes are included, that the tier limits load appropriately + d = JSON.parsefile("./scenarios/no_techs.json") + d["ElectricTariff"] = Dict() + d["ElectricTariff"]["urdb_response"] = JSON.parsefile("./scenarios/multi_tier_urdb_response.json") + s = Scenario(d) + p = REoptInputs(s) + @test p.s.electric_tariff.tou_demand_tier_limits[1, 1] ≈ 1.0e8 atol=1.0 + @test p.s.electric_tariff.tou_demand_tier_limits[1, 2] ≈ 1.0e8 atol=1.0 + @test p.s.electric_tariff.tou_demand_tier_limits[2, 1] ≈ 100.0 atol=1.0 + @test p.s.electric_tariff.tou_demand_tier_limits[2, 2] ≈ 1.0e8 atol=1.0 + @test p.s.electric_tariff.energy_tier_limits[1, 1] ≈ 1.0e10 atol=1.0 + @test p.s.electric_tariff.energy_tier_limits[1, 2] ≈ 1.0e10 atol=1.0 + @test p.s.electric_tariff.energy_tier_limits[6, 1] ≈ 20000.0 atol=1.0 + @test p.s.electric_tariff.energy_tier_limits[6, 2] ≈ 1.0e10 atol=1.0 + end + + @testset "Tiered TOU Demand" begin + data = JSON.parsefile("./scenarios/tiered_tou_demand.json") + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(model, data) + max_demand = data["ElectricLoad"]["annual_kwh"] / 8760 + tier1_max = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["max"] + tier1_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][1]["rate"] + tier2_rate = data["ElectricTariff"]["urdb_response"]["demandratestructure"][1][2]["rate"] + expected_demand_charges = 12 * (tier1_max * tier1_rate + (max_demand - tier1_max) * tier2_rate) + @test results["ElectricTariff"]["year_one_demand_cost_before_tax"] ≈ expected_demand_charges atol=1 + end + + # # tiered monthly demand rate TODO: expected results? + # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + # data = JSON.parsefile("./scenarios/tiered_energy_rate.json") + # data["ElectricTariff"]["urdb_label"] = "59bc22705457a3372642da67" + # s = Scenario(data) + # inputs = REoptInputs(s) + # results = run_reopt(m, inputs) + + @testset "Non-Standard Units for Energy Rates" begin + d = JSON.parsefile("./scenarios/no_techs.json") + d["ElectricTariff"] = Dict( + "urdb_label" => "6272e4ae7eb76766c247d469" + ) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, d) + @test occursin("URDB energy tiers have non-standard units of", string(results["Messages"])) + end + + end + + @testset "EASIUR" begin + d = JSON.parsefile("./scenarios/pv.json") + d["Site"]["latitude"] = 30.2672 + d["Site"]["longitude"] = -97.7431 + scen = Scenario(d) + @test scen.financial.NOx_grid_cost_per_tonne ≈ 5510.61 atol=0.1 + end + + @testset "Wind" begin + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d = JSON.parsefile("./scenarios/wind.json") + results = run_reopt(m, d) + @test results["Wind"]["size_kw"] ≈ 3752 atol=0.1 + @test results["Financial"]["lcc"] ≈ 8.591017e6 rtol=1e-5 + #= + 0.5% higher LCC in this package as compared to API ? 8,591,017 vs 8,551,172 + - both have zero curtailment + - same energy to grid: 5,839,317 vs 5,839,322 + - same energy to load: 4,160,683 vs 4,160,677 + - same city: Boulder + - same total wind prod factor + + REopt.jl has: + - bigger turbine: 3752 vs 3735 + - net_capital_costs_plus_om: 8,576,590 vs. 8,537,480 + + TODO: will these discrepancies be addressed once NMIL binaries are added? + =# + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d["Site"]["land_acres"] = 60 # = 2 MW (with 0.03 acres/kW) + results = run_reopt(m, d) + @test results["Wind"]["size_kw"] == 2000.0 # Wind should be constrained by land_acres + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + d["Wind"]["min_kw"] = 2001 # min_kw greater than land-constrained max should error + results = run_reopt(m, d) + @test "errors" ∈ keys(results["Messages"]) + @test length(results["Messages"]["errors"]) > 0 + end + + @testset "Multiple PVs" begin + logger = SimpleLogger() + with_logger(logger) do + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], "./scenarios/multiple_pvs.json") + + ground_pv = results["PV"][findfirst(pv -> pv["name"] == "ground", results["PV"])] + roof_west = results["PV"][findfirst(pv -> pv["name"] == "roof_west", results["PV"])] + roof_east = results["PV"][findfirst(pv -> pv["name"] == "roof_east", results["PV"])] + + @test ground_pv["size_kw"] ≈ 15 atol=0.1 + @test roof_west["size_kw"] ≈ 7 atol=0.1 + @test roof_east["size_kw"] ≈ 4 atol=0.1 + @test ground_pv["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + @test roof_west["lifecycle_om_cost_after_tax_bau"] ≈ 782.0 atol=0.1 + @test ground_pv["annual_energy_produced_kwh_bau"] ≈ 8933.09 atol=0.1 + @test roof_west["annual_energy_produced_kwh_bau"] ≈ 7656.11 atol=0.1 + @test ground_pv["annual_energy_produced_kwh"] ≈ 26799.26 atol=0.1 + @test roof_west["annual_energy_produced_kwh"] ≈ 10719.51 atol=0.1 + @test roof_east["annual_energy_produced_kwh"] ≈ 6685.95 atol=0.1 + end + end + + #candidate for hot and cold stor + @testset "Thermal Energy Storage + Absorption Chiller" begin + model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + data = JSON.parsefile("./scenarios/thermal_storage.json") + s = Scenario(data) + p = REoptInputs(s) - # #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. - # htf_defaults_response = get_absorption_chiller_defaults(; - # thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), - # boiler_type=get(data["ExistingBoiler"], "production_type", nothing), - # load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - # ) - - # expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] - # expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] - - # @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - # @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 - # @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 - - # #load test values - # p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR - # p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR - # p.s.absorption_chiller.cop_thermal = 0.7 - - # #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period - # for ts in p.time_steps - # #heating and cooling loads only - # if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge - # p.s.electric_load.loads_kw[ts] = 10 - # p.s.dhw_load.loads_kw[ts] = 5 - # p.s.space_heating_load.loads_kw[ts] = 5 - # p.s.cooling_load.loads_kw_thermal[ts] = 10 - # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 - # for tier in 1:p.s.electric_tariff.n_energy_tiers - # p.s.electric_tariff.energy_rates[ts, tier] = 100 - # end - # else #in odd periods, there is no load and energy is cheaper - storage should charge - # p.s.electric_load.loads_kw[ts] = 0 - # p.s.dhw_load.loads_kw[ts] = 0 - # p.s.space_heating_load.loads_kw[ts] = 0 - # p.s.cooling_load.loads_kw_thermal[ts] = 0 - # p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 - # for tier in 1:p.s.electric_tariff.n_energy_tiers - # p.s.electric_tariff.energy_rates[ts, tier] = 50 - # end - # end - # end - - # r = run_reopt(model, p) - - # #dispatch to load should be 10kW every other period = 4,380 * 10 - # @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 - # @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 - # #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC - # @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 - # @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 - # #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. - # @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 - # @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 - # @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 - # end - - # #condidate for hot/cold stor - # @testset "Heat and cool energy balance" begin - # """ - - # This is an "energy balance" type of test which tests the model formulation/math as opposed - # to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting - - # Validation to ensure that: - # 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage - # 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load - # 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly - - # """ - # input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) - - # # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load - # # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load - # crb_cop = REopt.get_existing_chiller_default_cop(; - # existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, - # max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) - # cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) - # cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR - - # #Test cooling load results - # @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 - - # # Convert fuel input to thermal using user input boiler efficiency - # boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) - # boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency - - # # Cooling outputs - # cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] - # cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] - # cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] - # cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] - # cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) - # cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) - # cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] - # cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) - # tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total - # cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total - # cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses - - # # Absorption Chiller electric consumption addition - # absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series - # absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) - # absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] - # absorpchl_cop_elec = s.absorption_chiller.cop_electric - - # # Check if sum of electric and absorption chillers equals cooling thermal total - # @test tes_effic_with_decay < 0.97 - # @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 - # @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 - - # # Heating outputs - # boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] - # boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] - # boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - # boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] - # chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] - # chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] - # chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] - # absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - # hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - # tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) - # total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) - # tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) - # total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) - # boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency - # total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows - - # @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 - # @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 - - # # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) - # absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU - # absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR - # absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh - - # @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 - # end - - # @testset "Heating and cooling inputs + CHP defaults" begin - # """ - - # This tests the various ways to input heating and cooling loads to make sure they are processed correctly. - # There are no "new" technologies in this test, so heating is served by ExistingBoiler, and - # cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. - - # """ - # input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # # Heating load is input as **fuel**, not thermal - # # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal - # expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] - # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 - # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 - # # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal - # input_data["ExistingBoiler"]["efficiency"] = 0.72 - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU - # @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 - # total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency - # @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 - - # # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input - # # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile - # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop - # @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** - - # #Test CHP defaults use average fuel load, size class 2 for recip_engine - # @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 - # @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 - - # delete!(input_data, "SpaceHeatingLoad") - # delete!(input_data, "DomesticHotWaterLoad") - # annual_fraction_of_electric_load_input = 0.5 - # input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input - # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - # @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 - # @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 - - # # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load - # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 - - # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) - # input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # #Test CHP defaults use average fuel load, size class changes to 3 - # @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 - # @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 - # #Update CHP prime_mover and test new defaults - # input_data["CHP"]["prime_mover"] = "combustion_turbine" - # input_data["CHP"]["size_class"] = 1 - # # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw - # input_data["CHP"]["max_kw"] = 2500.0 - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 - # @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 - - # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 - # total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop - # @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 - - # input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) - # input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + - # sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU - # @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 - # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 - - # # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied - # input_data["SpaceHeatingLoad"] = Dict{Any, Any}() - # input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() - # annual_tonhour = 25000.0 - # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", - # "annual_tonhour" => annual_tonhour) - # input_data["ExistingChiller"] = Dict{Any, Any}() - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 - - # # Test for prime generator CHP inputs (electric only) - # # First get CHP cost to compare later with prime generator - # input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", - # "annual_kwh" => 876000) - # input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, - # "blended_annual_demand_rate" => 0.0 ) - # s_chp = Scenario(input_data) - # inputs_chp = REoptInputs(s) - # installed_cost_chp = s_chp.chp.installed_cost_per_kw - - # # Now get prime generator (electric only) - # input_data["CHP"]["is_electric_only"] = true - # delete!(input_data["CHP"], "max_kw") - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # # Costs are 75% of CHP - # @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 - # @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 - # @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 - # # Thermal efficiency set to zero - # @test inputs.s.chp.thermal_efficiency_full_load == 0 - # @test inputs.s.chp.thermal_efficiency_half_load == 0 - # # Max size based on electric load, not heating load - # @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 - # end - - # @testset "Hybrid/blended heating and cooling loads" begin - # """ - - # This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names - # and blended_doe_reference_percents are given and blended to create an aggregate load profile - - # """ - # input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") - - # hospital_fraction = 0.75 - # hotel_fraction = 1.0 - hospital_fraction - - # # Hospital only - # input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 - # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 - # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" - # input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # elec_hospital = inputs.s.electric_load.loads_kw - # space_hospital = inputs.s.space_heating_load.loads_kw # thermal - # dhw_hospital = inputs.s.dhw_load.loads_kw # thermal - # cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal - # cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital - - # # Hotel only - # input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 - # input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 - # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" - # input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # elec_hotel = inputs.s.electric_load.loads_kw - # space_hotel = inputs.s.space_heating_load.loads_kw # thermal - # dhw_hotel = inputs.s.dhw_load.loads_kw # thermal - # cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal - # cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel - - # # Hybrid mix of hospital and hotel - # # Remove previous assignment of doe_reference_name - # for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] - # delete!(input_data[load], "doe_reference_name") - # end - # annual_energy = (hospital_fraction + hotel_fraction) * 100 - # building_list = ["Hospital", "LargeHotel"] - # percent_share_list = [hospital_fraction, hotel_fraction] - # input_data["ElectricLoad"]["annual_kwh"] = annual_energy - # input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list - # input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list - - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy - # input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list - # input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy - # input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list - # input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list - - # # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided - # input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list - # input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - - # elec_hybrid = inputs.s.electric_load.loads_kw - # space_hybrid = inputs.s.space_heating_load.loads_kw # thermal - # dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal - # cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal - # cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric - # cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid - - # # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step - - # @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 - # @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 - # @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 - # # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles - # cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ - # cooling_elec_frac_of_total_hotel * hotel_fraction) - # @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 - # end - - # @testset "Boiler (new) test" begin - # input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], inputs) - - # # BAU boiler loads - # load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU - # existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - # boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) - - # # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only - # # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year - # @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 - # @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 - # end - - # @testset "OffGrid" begin - # ## Scenario 1: Solar, Storage, Fixed Generator - # post_name = "off_grid.json" - # post = JSON.parsefile("./scenarios/$post_name") - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # scen = Scenario(post) - # r = run_reopt(m, scen) - - # # Test default values - # @test scen.electric_utility.outage_start_time_step ≈ 1 - # @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour - # @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 - # @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false - # @test scen.generator.fuel_avail_gal ≈ 1.0e9 - # @test scen.generator.min_turn_down_fraction ≈ 0.15 - # @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw - # @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 - - # # Test outputs - # @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid - # @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc - # @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required - # @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - # @test r["PV"]["size_kw"] ≈ 5050.0 - # f = r["Financial"] - # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - - # ## Scenario 2: Fixed Generator only - # post["ElectricLoad"]["annual_kwh"] = 100.0 - # post["PV"]["max_kw"] = 0.0 - # post["ElectricStorage"]["max_kw"] = 0.0 - # post["Generator"]["min_turn_down_fraction"] = 0.0 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, post) - - # # Test generator outputs - # @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh - # @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 - # @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 - # @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 - # @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) - # @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible - # @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 - # @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 - # @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 - - # ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement - # ## This test ensures the load operating reserve requirement is being enforced - # post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" - # post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen - # post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity - # post["PV"]["max_kw"] = 0.0 - # post["ElectricStorage"]["max_kw"] = 0.0 - # post["Generator"]["min_turn_down_fraction"] = 0.0 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, post) - - # # Test generator outputs - # @test typeof(r) == Model # this is true when the model is infeasible - - # ### Scenario 3: Indonesia. Wind (custom prod) and Generator only - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) - # post_name = "wind_intl_offgrid.json" - # post = JSON.parsefile("./scenarios/$post_name") - # post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] - # scen = Scenario(post) - # post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) - - # results = run_reopt(m, post) - - # @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction - # f = results["Financial"] - # @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + - # f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + - # f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + - # f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - - # f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 - - # windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) - # loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) - # @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 - - # end - - # @testset "GHP" begin - # """ - - # This tests multiple unique aspects of GHP: - # 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one - # 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load - # 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW - # 4. GHP serves all the Cooling load - # 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly - # 6. Hybrid GHP capability functions as expected - - # """ - # # Load base inputs - # input_data = JSON.parsefile("scenarios/ghp_inputs.json") - - # # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl - # # Heat pump performance maps - # cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) - # data = cop_map_mat_header[1] - # headers = cop_map_mat_header[2] - # # Generate a "records" style dictionary from the - # cop_map_list = [] - # for i in axes(data,1) - # dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) - # push!(cop_map_list, dict_record) - # end - # input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list - - # # Due to GhpGhx not being a registered package (no OSI-approved license), - # # the registered REopt package cannot have GhpGhx as a "normal" dependency; - # # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an - # # input to REopt to avoid GhpGhx module calls - # response_1 = JSON.parsefile("scenarios/ghpghx_response.json") - # response_2 = deepcopy(response_1) - # # Reduce the electric consumption of response 2 which should then be the chosen system - # response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 - # input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] - - # # Heating load - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - # input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) - # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 - # input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 - - # # Call REopt - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt([m1,m2], inputs) - - # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - # @test ghp_option_chosen == 2 - - # # Test GHP heating and cooling load reduced - # hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) - # cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) - # @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 - # @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 - - # # Test GHP serving space heating with VAV thermal efficiency improvements - # heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) - # expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) - # @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 - - # # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit - # boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) - # expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) - # @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 - - # # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production - # bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) - # ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) - # @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 - - # # Custom heat pump COP map is used properly - # ghp_option_chosen = results["GHP"]["ghp_option_chosen"] - # heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] - # cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] - # # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map - # @test heating_cop_avg <= 4.0 - # @test cooling_cop_avg <= 8.0 - # end - - # @testset "Hybrid GHX and GHP calculated costs validation" begin - # ## Hybrid GHP validation. - # # Load base inputs - # input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") - - # inputs = REoptInputs(input_data) - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt([m1,m2], inputs) - - # calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* - # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* - # inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + - # (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* - # input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* - # inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + - # (inputs.s.ghp_option_list[1].building_sqft* - # inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) - - # @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 - - # calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* - # inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om - - # @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 - - # calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) - # @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 - - # @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 - - # @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 - - # @test abs(results["Financial"]["npv"] - 840621) < 1.0 - # @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 - # @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 - - # @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") - - # ## Hybrid - # input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] - # input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 - # input_data["GHP"]["ghx_useful_life_years"] = 35 - - # inputs = REoptInputs(input_data) - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt([m1,m2], inputs) - - # pop!(input_data["GHP"], "ghpghx_inputs", nothing) - # pop!(input_data["GHP"], "ghpghx_responses", nothing) - # ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) - - # calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* - # ( - # (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years - # )/( - # (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years - # ) - - # @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 - # @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true - - # # Test centralized GHP cost calculations - # input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") - # response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") - # input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] - - # s_wwhp = Scenario(input_data_wwhp) - # inputs_wwhp = REoptInputs(s_wwhp) - # m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results_wwhp = run_reopt(m3, inputs_wwhp) - - - # heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * - # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] - - # cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * - # input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] - - # ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * - # results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] - - # # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount - # capex_reduction_factor = 0.455005797 - - # calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) - - # reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] - # @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 - # end - - # @testset "Cambium Emissions" begin - # """ - # 1) Location in contiguous US - # - Correct data from Cambium (returned location and values) - # - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) - # - co2 pct increase should be zero - # 2) HI and AK locations - # - Should use AVERT data and give an "info" message - # - Adjust for load year vs. AVERT year - # - co2 pct increase should be the default value unless user provided value - # 3) International - # - all emissions should be zero unless provided - # """ - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + #test for get_absorption_chiller_defaults consistency with inputs data and Scenario s. + htf_defaults_response = get_absorption_chiller_defaults(; + thermal_consumption_hot_water_or_steam=get(data["AbsorptionChiller"], "thermal_consumption_hot_water_or_steam", nothing), + boiler_type=get(data["ExistingBoiler"], "production_type", nothing), + load_max_tons=maximum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + ) + + expected_installed_cost_per_ton = htf_defaults_response["default_inputs"]["installed_cost_per_ton"] + expected_om_cost_per_ton = htf_defaults_response["default_inputs"]["om_cost_per_ton"] + + @test p.s.absorption_chiller.installed_cost_per_kw ≈ expected_installed_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + @test p.s.absorption_chiller.om_cost_per_kw ≈ expected_om_cost_per_ton / REopt.KWH_THERMAL_PER_TONHOUR atol=0.001 + @test p.s.absorption_chiller.cop_thermal ≈ htf_defaults_response["default_inputs"]["cop_thermal"] atol=0.001 + + #load test values + p.s.absorption_chiller.installed_cost_per_kw = 500.0 / REopt.KWH_THERMAL_PER_TONHOUR + p.s.absorption_chiller.om_cost_per_kw = 0.5 / REopt.KWH_THERMAL_PER_TONHOUR + p.s.absorption_chiller.cop_thermal = 0.7 + + #Make every other hour zero fuel and electric cost; storage should charge and discharge in each period + for ts in p.time_steps + #heating and cooling loads only + if ts % 2 == 0 #in even periods, there is a nonzero load and energy is higher cost, and storage should discharge + p.s.electric_load.loads_kw[ts] = 10 + p.s.dhw_load.loads_kw[ts] = 5 + p.s.space_heating_load.loads_kw[ts] = 5 + p.s.cooling_load.loads_kw_thermal[ts] = 10 + p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 100 + for tier in 1:p.s.electric_tariff.n_energy_tiers + p.s.electric_tariff.energy_rates[ts, tier] = 100 + end + else #in odd periods, there is no load and energy is cheaper - storage should charge + p.s.electric_load.loads_kw[ts] = 0 + p.s.dhw_load.loads_kw[ts] = 0 + p.s.space_heating_load.loads_kw[ts] = 0 + p.s.cooling_load.loads_kw_thermal[ts] = 0 + p.fuel_cost_per_kwh["ExistingBoiler"][ts] = 1 + for tier in 1:p.s.electric_tariff.n_energy_tiers + p.s.electric_tariff.energy_rates[ts, tier] = 50 + end + end + end + + r = run_reopt(model, p) + + #dispatch to load should be 10kW every other period = 4,380 * 10 + @test sum(r["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"]) ≈ 149.45 atol=0.1 + @test sum(r["ColdThermalStorage"]["storage_to_load_series_ton"]) ≈ 12454.33 atol=0.1 + #size should be just over 10kW in gallons, accounting for efficiency losses and min SOC + @test r["HotThermalStorage"]["size_gal"] ≈ 233.0 atol=0.1 + @test r["ColdThermalStorage"]["size_gal"] ≈ 378.0 atol=0.1 + #No production from existing chiller, only absorption chiller, which is sized at ~5kW to manage electric demand charge & capital cost. + @test r["ExistingChiller"]["annual_thermal_production_tonhour"] ≈ 0.0 atol=0.1 + @test r["AbsorptionChiller"]["annual_thermal_production_tonhour"] ≈ 12464.15 atol=0.1 + @test r["AbsorptionChiller"]["size_ton"] ≈ 2.846 atol=0.01 + end + + #condidate for hot/cold stor + @testset "Heat and cool energy balance" begin + """ + + This is an "energy balance" type of test which tests the model formulation/math as opposed + to a specific scenario. This test is robust to changes in the model "MIPRELSTOP" or "MAXTIME" setting + + Validation to ensure that: + 1) The electric and absorption chillers are supplying 100% of the cooling thermal load plus losses from ColdThermalStorage + 2) The boiler and CHP are supplying the heating load plus additional absorption chiller thermal load + 3) The Cold and Hot TES efficiency (charge loss and thermal decay) are being tracked properly + + """ + input_data = JSON.parsefile("./scenarios/heat_cool_energy_balance_inputs.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) + + # Annual cooling **thermal** energy load of CRB is based on annual cooling electric energy (from CRB models) and a conditional COP depending on the peak cooling thermal load + # When the user specifies inputs["ExistingChiller"]["cop"], this changes the **electric** consumption of the chiller to meet that cooling thermal load + crb_cop = REopt.get_existing_chiller_default_cop(; + existing_chiller_max_thermal_factor_on_peak_load=s.existing_chiller.max_thermal_factor_on_peak_load, + max_load_kw_thermal=maximum(s.cooling_load.loads_kw_thermal)) + cooling_thermal_load_tonhour_total = 1427329.0 * crb_cop / REopt.KWH_THERMAL_PER_TONHOUR # From CRB models, in heating_cooling_loads.jl, BuiltInCoolingLoad data for location (SanFrancisco Hospital) + cooling_electric_load_total_mod_cop_kwh = cooling_thermal_load_tonhour_total / inputs.s.existing_chiller.cop * REopt.KWH_THERMAL_PER_TONHOUR + + #Test cooling load results + @test round(cooling_thermal_load_tonhour_total, digits=1) ≈ results["CoolingLoad"]["annual_calculated_tonhour"] atol=1.0 + + # Convert fuel input to thermal using user input boiler efficiency + boiler_thermal_load_mmbtu_total = (671.40531 + 11570.9155) * input_data["ExistingBoiler"]["efficiency"] # From CRB models, in heating_cooling_loads.jl, BuiltInDomesticHotWaterLoad + BuiltInSpaceHeatingLoad data for location (SanFrancisco Hospital) + boiler_fuel_consumption_total_mod_efficiency = boiler_thermal_load_mmbtu_total / inputs.s.existing_boiler.efficiency + + # Cooling outputs + cooling_elecchl_tons_to_load_series = results["ExistingChiller"]["thermal_to_load_series_ton"] + cooling_elecchl_tons_to_tes_series = results["ExistingChiller"]["thermal_to_storage_series_ton"] + cooling_absorpchl_tons_to_load_series = results["AbsorptionChiller"]["thermal_to_load_series_ton"] + cooling_absorpchl_tons_to_tes_series = results["AbsorptionChiller"]["thermal_to_storage_series_ton"] + cooling_tonhour_to_load_tech_total = sum(cooling_elecchl_tons_to_load_series) + sum(cooling_absorpchl_tons_to_load_series) + cooling_tonhour_to_tes_total = sum(cooling_elecchl_tons_to_tes_series) + sum(cooling_absorpchl_tons_to_tes_series) + cooling_tes_tons_to_load_series = results["ColdThermalStorage"]["storage_to_load_series_ton"] + cooling_extra_from_tes_losses = cooling_tonhour_to_tes_total - sum(cooling_tes_tons_to_load_series) + tes_effic_with_decay = sum(cooling_tes_tons_to_load_series) / cooling_tonhour_to_tes_total + cooling_total_prod_from_techs = cooling_tonhour_to_load_tech_total + cooling_tonhour_to_tes_total + cooling_load_plus_tes_losses = cooling_thermal_load_tonhour_total + cooling_extra_from_tes_losses + + # Absorption Chiller electric consumption addition + absorpchl_total_cooling_produced_series_ton = cooling_absorpchl_tons_to_load_series .+ cooling_absorpchl_tons_to_tes_series + absorpchl_total_cooling_produced_ton_hour = sum(absorpchl_total_cooling_produced_series_ton) + absorpchl_electric_consumption_total_kwh = results["AbsorptionChiller"]["annual_electric_consumption_kwh"] + absorpchl_cop_elec = s.absorption_chiller.cop_electric + + # Check if sum of electric and absorption chillers equals cooling thermal total + @test tes_effic_with_decay < 0.97 + @test round(cooling_total_prod_from_techs, digits=0) ≈ cooling_load_plus_tes_losses atol=5.0 + @test round(absorpchl_electric_consumption_total_kwh, digits=0) ≈ absorpchl_total_cooling_produced_ton_hour * REopt.KWH_THERMAL_PER_TONHOUR / absorpchl_cop_elec atol=1.0 + + # Heating outputs + boiler_fuel_consumption_calculated = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + boiler_thermal_series = results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"] + boiler_to_load_series = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + boiler_thermal_to_tes_series = results["ExistingBoiler"]["thermal_to_storage_series_mmbtu_per_hour"] + chp_thermal_to_load_series = results["CHP"]["thermal_to_load_series_mmbtu_per_hour"] + chp_thermal_to_tes_series = results["CHP"]["thermal_to_storage_series_mmbtu_per_hour"] + chp_thermal_to_waste_series = results["CHP"]["thermal_curtailed_series_mmbtu_per_hour"] + absorpchl_thermal_series = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + hot_tes_mmbtu_per_hour_to_load_series = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + tes_inflows = sum(chp_thermal_to_tes_series) + sum(boiler_thermal_to_tes_series) + total_chp_production = sum(chp_thermal_to_load_series) + sum(chp_thermal_to_waste_series) + sum(chp_thermal_to_tes_series) + tes_outflows = sum(hot_tes_mmbtu_per_hour_to_load_series) + total_thermal_expected = boiler_thermal_load_mmbtu_total + sum(chp_thermal_to_waste_series) + tes_inflows + sum(absorpchl_thermal_series) + boiler_fuel_expected = (total_thermal_expected - total_chp_production - tes_outflows) / inputs.s.existing_boiler.efficiency + total_thermal_mmbtu_calculated = sum(boiler_thermal_series) + total_chp_production + tes_outflows + + @test round(boiler_fuel_consumption_calculated, digits=0) ≈ boiler_fuel_expected atol=8.0 + @test round(total_thermal_mmbtu_calculated, digits=0) ≈ total_thermal_expected atol=8.0 + + # Test CHP["cooling_thermal_factor"] = 0.8, AbsorptionChiller["cop_thermal"] = 0.7 (from inputs .json) + absorpchl_heat_in_kwh = results["AbsorptionChiller"]["annual_thermal_consumption_mmbtu"] * REopt.KWH_PER_MMBTU + absorpchl_cool_out_kwh = results["AbsorptionChiller"]["annual_thermal_production_tonhour"] * REopt.KWH_THERMAL_PER_TONHOUR + absorpchl_cop = absorpchl_cool_out_kwh / absorpchl_heat_in_kwh + + @test round(absorpchl_cop, digits=5) ≈ 0.8*0.7 rtol=1e-4 + end + + @testset "Heating and cooling inputs + CHP defaults" begin + """ + + This tests the various ways to input heating and cooling loads to make sure they are processed correctly. + There are no "new" technologies in this test, so heating is served by ExistingBoiler, and + cooling is served by ExistingCooler. Since this is just inputs processing tests, no optimization is needed. + + """ + input_data = JSON.parsefile("./scenarios/heating_cooling_load_inputs.json") + s = Scenario(input_data) + inputs = REoptInputs(s) + + # Heating load is input as **fuel**, not thermal + # If boiler efficiency is not input, we use REopt.EXISTING_BOILER_EFFICIENCY to convert fuel to thermal + expected_fuel = input_data["SpaceHeatingLoad"]["annual_mmbtu"] + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] + total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY atol=1.0 + total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * REopt.EXISTING_BOILER_EFFICIENCY / inputs.s.existing_boiler.efficiency atol=1.0 + # If boiler efficiency is input, use that with annual or monthly mmbtu input to convert fuel to thermal + input_data["ExistingBoiler"]["efficiency"] = 0.72 + s = Scenario(input_data) + inputs = REoptInputs(s) + total_boiler_heating_thermal_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + sum(inputs.s.dhw_load.loads_kw)) / REopt.KWH_PER_MMBTU + @test round(total_boiler_heating_thermal_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] atol=1.0 + total_boiler_heating_fuel_load_mmbtu = total_boiler_heating_thermal_load_mmbtu / inputs.s.existing_boiler.efficiency + @test round(total_boiler_heating_fuel_load_mmbtu, digits=0) ≈ expected_fuel * input_data["ExistingBoiler"]["efficiency"] / inputs.s.existing_boiler.efficiency atol=1.0 + + # The expected cooling load is based on the default **fraction of total electric** profile for the doe_reference_name when annual_tonhour is NOT input + # the 320540.0 kWh number is from the default LargeOffice fraction of total electric profile applied to the Hospital default total electric profile + total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.existing_chiller.cop + @test round(total_chiller_electric_consumption, digits=0) ≈ 320544.0 atol=1.0 # loads_kw is **electric**, loads_kw_thermal is **thermal** + + #Test CHP defaults use average fuel load, size class 2 for recip_engine + @test inputs.s.chp.min_allowable_kw ≈ 50.0 atol=0.01 + @test inputs.s.chp.om_cost_per_kwh ≈ 0.0235 atol=0.0001 + + delete!(input_data, "SpaceHeatingLoad") + delete!(input_data, "DomesticHotWaterLoad") + annual_fraction_of_electric_load_input = 0.5 + input_data["CoolingLoad"] = Dict{Any, Any}("annual_fraction_of_electric_load" => annual_fraction_of_electric_load_input) + + s = Scenario(input_data) + inputs = REoptInputs(s) + + expected_cooling_electricity = sum(inputs.s.electric_load.loads_kw) * annual_fraction_of_electric_load_input + total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + @test round(total_chiller_electric_consumption, digits=0) ≈ round(expected_cooling_electricity) atol=1.0 + @test round(total_chiller_electric_consumption, digits=0) ≈ 3876410 atol=1.0 + + # Check that without heating load or max_kw input, CHP.max_kw gets set based on peak electric load + @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.01 + + input_data["SpaceHeatingLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("monthly_mmbtu" => repeat([1000.0], 12)) + input_data["CoolingLoad"] = Dict{Any, Any}("monthly_fractions_of_electric_load" => repeat([0.1], 12)) + + s = Scenario(input_data) + inputs = REoptInputs(s) + + #Test CHP defaults use average fuel load, size class changes to 3 + @test inputs.s.chp.min_allowable_kw ≈ 125.0 atol=0.1 + @test inputs.s.chp.om_cost_per_kwh ≈ 0.021 atol=0.0001 + #Update CHP prime_mover and test new defaults + input_data["CHP"]["prime_mover"] = "combustion_turbine" + input_data["CHP"]["size_class"] = 1 + # Set max_kw higher than peak electric load so min_allowable_kw doesn't get assigned to max_kw + input_data["CHP"]["max_kw"] = 2500.0 + + s = Scenario(input_data) + inputs = REoptInputs(s) + + @test inputs.s.chp.min_allowable_kw ≈ 2000.0 atol=0.1 + @test inputs.s.chp.om_cost_per_kwh ≈ 0.014499999999999999 atol=0.0001 + + total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 24000 atol=1.0 + total_chiller_electric_consumption = sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop + @test round(total_chiller_electric_consumption, digits=0) ≈ 775282 atol=1.0 + + input_data["SpaceHeatingLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + input_data["DomesticHotWaterLoad"] = Dict{Any, Any}("fuel_loads_mmbtu_per_hour" => repeat([0.5], 8760)) + input_data["CoolingLoad"] = Dict{Any, Any}("per_time_step_fractions_of_electric_load" => repeat([0.01], 8760)) + + s = Scenario(input_data) + inputs = REoptInputs(s) + + total_heating_fuel_load_mmbtu = (sum(inputs.s.space_heating_load.loads_kw) + + sum(inputs.s.dhw_load.loads_kw)) / input_data["ExistingBoiler"]["efficiency"] / REopt.KWH_PER_MMBTU + @test round(total_heating_fuel_load_mmbtu, digits=0) ≈ 8760 atol=0.1 + @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / inputs.s.cooling_load.existing_chiller_cop, digits=0) ≈ 77528.0 atol=1.0 + + # Make sure annual_tonhour is preserved with conditional existing_chiller_default logic, where guess-and-correct method is applied + input_data["SpaceHeatingLoad"] = Dict{Any, Any}() + input_data["DomesticHotWaterLoad"] = Dict{Any, Any}() + annual_tonhour = 25000.0 + input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital", + "annual_tonhour" => annual_tonhour) + input_data["ExistingChiller"] = Dict{Any, Any}() + + s = Scenario(input_data) + inputs = REoptInputs(s) + + @test round(sum(inputs.s.cooling_load.loads_kw_thermal) / REopt.KWH_THERMAL_PER_TONHOUR, digits=0) ≈ annual_tonhour atol=1.0 + + # Test for prime generator CHP inputs (electric only) + # First get CHP cost to compare later with prime generator + input_data["ElectricLoad"] = Dict("doe_reference_name" => "FlatLoad", + "annual_kwh" => 876000) + input_data["ElectricTariff"] = Dict("blended_annual_energy_rate" => 0.06, + "blended_annual_demand_rate" => 0.0 ) + s_chp = Scenario(input_data) + inputs_chp = REoptInputs(s) + installed_cost_chp = s_chp.chp.installed_cost_per_kw + + # Now get prime generator (electric only) + input_data["CHP"]["is_electric_only"] = true + delete!(input_data["CHP"], "max_kw") + s = Scenario(input_data) + inputs = REoptInputs(s) + # Costs are 75% of CHP + @test inputs.s.chp.installed_cost_per_kw ≈ (0.75*installed_cost_chp) atol=1.0 + @test inputs.s.chp.om_cost_per_kwh ≈ (0.75*0.0145) atol=0.0001 + @test inputs.s.chp.federal_itc_fraction ≈ 0.0 atol=0.0001 + # Thermal efficiency set to zero + @test inputs.s.chp.thermal_efficiency_full_load == 0 + @test inputs.s.chp.thermal_efficiency_half_load == 0 + # Max size based on electric load, not heating load + @test inputs.s.chp.max_kw ≈ maximum(inputs.s.electric_load.loads_kw) atol=0.001 + end + + @testset "Hybrid/blended heating and cooling loads" begin + """ + + This tests the hybrid/campus loads for heating and cooling, where a blended_doe_reference_names + and blended_doe_reference_percents are given and blended to create an aggregate load profile + + """ + input_data = JSON.parsefile("./scenarios/hybrid_loads_heating_cooling_inputs.json") + + hospital_fraction = 0.75 + hotel_fraction = 1.0 - hospital_fraction + + # Hospital only + input_data["ElectricLoad"]["annual_kwh"] = hospital_fraction * 100 + input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hospital_fraction * 100 + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hospital_fraction * 100 + input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "Hospital" + input_data["CoolingLoad"]["doe_reference_name"] = "Hospital" + + s = Scenario(input_data) + inputs = REoptInputs(s) + + elec_hospital = inputs.s.electric_load.loads_kw + space_hospital = inputs.s.space_heating_load.loads_kw # thermal + dhw_hospital = inputs.s.dhw_load.loads_kw # thermal + cooling_hospital = inputs.s.cooling_load.loads_kw_thermal # thermal + cooling_elec_frac_of_total_hospital = cooling_hospital / inputs.s.cooling_load.existing_chiller_cop ./ elec_hospital + + # Hotel only + input_data["ElectricLoad"]["annual_kwh"] = hotel_fraction * 100 + input_data["ElectricLoad"]["doe_reference_name"] = "LargeHotel" + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = hotel_fraction * 100 + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "LargeHotel" + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = hotel_fraction * 100 + input_data["DomesticHotWaterLoad"]["doe_reference_name"] = "LargeHotel" + input_data["CoolingLoad"]["doe_reference_name"] = "LargeHotel" + + s = Scenario(input_data) + inputs = REoptInputs(s) + + elec_hotel = inputs.s.electric_load.loads_kw + space_hotel = inputs.s.space_heating_load.loads_kw # thermal + dhw_hotel = inputs.s.dhw_load.loads_kw # thermal + cooling_hotel = inputs.s.cooling_load.loads_kw_thermal # thermal + cooling_elec_frac_of_total_hotel = cooling_hotel / inputs.s.cooling_load.existing_chiller_cop ./ elec_hotel + + # Hybrid mix of hospital and hotel + # Remove previous assignment of doe_reference_name + for load in ["ElectricLoad", "SpaceHeatingLoad", "DomesticHotWaterLoad", "CoolingLoad"] + delete!(input_data[load], "doe_reference_name") + end + annual_energy = (hospital_fraction + hotel_fraction) * 100 + building_list = ["Hospital", "LargeHotel"] + percent_share_list = [hospital_fraction, hotel_fraction] + input_data["ElectricLoad"]["annual_kwh"] = annual_energy + input_data["ElectricLoad"]["blended_doe_reference_names"] = building_list + input_data["ElectricLoad"]["blended_doe_reference_percents"] = percent_share_list + + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = annual_energy + input_data["SpaceHeatingLoad"]["blended_doe_reference_names"] = building_list + input_data["SpaceHeatingLoad"]["blended_doe_reference_percents"] = percent_share_list + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = annual_energy + input_data["DomesticHotWaterLoad"]["blended_doe_reference_names"] = building_list + input_data["DomesticHotWaterLoad"]["blended_doe_reference_percents"] = percent_share_list + + # CoolingLoad now use a weighted fraction of total electric profile if no annual_tonhour is provided + input_data["CoolingLoad"]["blended_doe_reference_names"] = building_list + input_data["CoolingLoad"]["blended_doe_reference_percents"] = percent_share_list + + s = Scenario(input_data) + inputs = REoptInputs(s) + + elec_hybrid = inputs.s.electric_load.loads_kw + space_hybrid = inputs.s.space_heating_load.loads_kw # thermal + dhw_hybrid = inputs.s.dhw_load.loads_kw # thermal + cooling_hybrid = inputs.s.cooling_load.loads_kw_thermal # thermal + cooling_elec_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop # electric + cooling_elec_frac_of_total_hybrid = cooling_hybrid / inputs.s.cooling_load.existing_chiller_cop ./ elec_hybrid + + # Check that the combined/hybrid load is the same as the sum of the individual loads in each time_step + + @test round(sum(elec_hybrid .- (elec_hospital .+ elec_hotel)), digits=1) ≈ 0.0 atol=0.1 + @test round(sum(space_hybrid .- (space_hospital .+ space_hotel)), digits=1) ≈ 0.0 atol=0.1 + @test round(sum(dhw_hybrid .- (dhw_hospital .+ dhw_hotel)), digits=1) ≈ 0.0 atol=0.1 + # Check that the cooling load is the weighted average of the default CRB fraction of total electric profiles + cooling_electric_hybrid_expected = elec_hybrid .* (cooling_elec_frac_of_total_hospital * hospital_fraction .+ + cooling_elec_frac_of_total_hotel * hotel_fraction) + @test round(sum(cooling_electric_hybrid_expected .- cooling_elec_hybrid), digits=1) ≈ 0.0 atol=0.1 + end + + @testset "Boiler (new) test" begin + input_data = JSON.parsefile("scenarios/boiler_new_inputs.json") + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = 0.5 * 8760 + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.5 * 8760 + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], inputs) + + # BAU boiler loads + load_thermal_mmbtu_bau = sum(s.space_heating_load.loads_kw + s.dhw_load.loads_kw) / REopt.KWH_PER_MMBTU + existing_boiler_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + boiler_thermal_mmbtu = sum(results["Boiler"]["thermal_production_series_mmbtu_per_hour"]) + + # Used monthly fuel cost for ExistingBoiler and Boiler, where ExistingBoiler has lower fuel cost only + # in February (28 days), so expect ExistingBoiler to serve the flat/constant load 28 days of the year + @test existing_boiler_mmbtu ≈ load_thermal_mmbtu_bau * 28 / 365 atol=0.00001 + @test boiler_thermal_mmbtu ≈ load_thermal_mmbtu_bau - existing_boiler_mmbtu atol=0.00001 + end + + @testset "OffGrid" begin + ## Scenario 1: Solar, Storage, Fixed Generator + post_name = "off_grid.json" + post = JSON.parsefile("./scenarios/$post_name") + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + scen = Scenario(post) + r = run_reopt(m, scen) + + # Test default values + @test scen.electric_utility.outage_start_time_step ≈ 1 + @test scen.electric_utility.outage_end_time_step ≈ 8760 * scen.settings.time_steps_per_hour + @test scen.storage.attr["ElectricStorage"].soc_init_fraction ≈ 1 + @test scen.storage.attr["ElectricStorage"].can_grid_charge ≈ false + @test scen.generator.fuel_avail_gal ≈ 1.0e9 + @test scen.generator.min_turn_down_fraction ≈ 0.15 + @test sum(scen.electric_load.loads_kw) - sum(scen.electric_load.critical_loads_kw) ≈ 0 # critical loads should equal loads_kw + @test scen.financial.microgrid_upgrade_cost_fraction ≈ 0 + + # Test outputs + @test r["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0 # no interaction with grid + @test r["Financial"]["lifecycle_offgrid_other_capital_costs"] ≈ 2617.092 atol=0.01 # Check straight line depreciation calc + @test sum(r["ElectricLoad"]["offgrid_annual_oper_res_provided_series_kwh"]) >= sum(r["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) # OR provided >= required + @test r["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + @test r["PV"]["size_kw"] ≈ 5050.0 + f = r["Financial"] + @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + + ## Scenario 2: Fixed Generator only + post["ElectricLoad"]["annual_kwh"] = 100.0 + post["PV"]["max_kw"] = 0.0 + post["ElectricStorage"]["max_kw"] = 0.0 + post["Generator"]["min_turn_down_fraction"] = 0.0 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, post) + + # Test generator outputs + @test r["Generator"]["annual_fuel_consumption_gal"] ≈ 7.52 # 99 kWh * 0.076 gal/kWh + @test r["Generator"]["annual_energy_produced_kwh"] ≈ 99.0 + @test r["Generator"]["year_one_fuel_cost_before_tax"] ≈ 22.57 + @test r["Generator"]["lifecycle_fuel_cost_after_tax"] ≈ 205.35 + @test r["Financial"]["initial_capital_costs"] ≈ 100*(700) + @test r["Financial"]["lifecycle_capital_costs"] ≈ 100*(700+324.235442*(1-0.26)) atol=0.1 # replacement in yr 10 is considered tax deductible + @test r["Financial"]["initial_capital_costs_after_incentives"] ≈ 700*100 atol=0.1 + @test r["Financial"]["replacements_future_cost_after_tax"] ≈ 700*100 + @test r["Financial"]["replacements_present_cost_after_tax"] ≈ 100*(324.235442*(1-0.26)) atol=0.1 + + ## Scenario 3: Fixed Generator that can meet load, but cannot meet load operating reserve requirement + ## This test ensures the load operating reserve requirement is being enforced + post["ElectricLoad"]["doe_reference_name"] = "FlatLoad" + post["ElectricLoad"]["annual_kwh"] = 876000.0 # requires 100 kW gen + post["ElectricLoad"]["min_load_met_annual_fraction"] = 1.0 # requires additional generator capacity + post["PV"]["max_kw"] = 0.0 + post["ElectricStorage"]["max_kw"] = 0.0 + post["Generator"]["min_turn_down_fraction"] = 0.0 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, post) + + # Test generator outputs + @test typeof(r) == Model # this is true when the model is infeasible + + ### Scenario 3: Indonesia. Wind (custom prod) and Generator only + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01, "presolve" => "on")) + post_name = "wind_intl_offgrid.json" + post = JSON.parsefile("./scenarios/$post_name") + post["ElectricLoad"]["loads_kw"] = [10.0 for i in range(1,8760)] + scen = Scenario(post) + post["Wind"]["production_factor_series"] = reduce(vcat, readdlm("./data/example_wind_prod_factor_kw.csv", '\n', header=true)[1]) + + results = run_reopt(m, post) + + @test results["ElectricLoad"]["offgrid_load_met_fraction"] >= scen.electric_load.min_load_met_annual_fraction + f = results["Financial"] + @test f["lifecycle_generation_tech_capital_costs"] + f["lifecycle_storage_capital_costs"] + f["lifecycle_om_costs_after_tax"] + + f["lifecycle_fuel_costs_after_tax"] + f["lifecycle_chp_standby_cost_after_tax"] + f["lifecycle_elecbill_after_tax"] + + f["lifecycle_offgrid_other_annual_costs_after_tax"] + f["lifecycle_offgrid_other_capital_costs"] + + f["lifecycle_outage_cost"] + f["lifecycle_MG_upgrade_and_fuel_cost"] - + f["lifecycle_production_incentive_after_tax"] ≈ f["lcc"] atol=1.0 + + windOR = sum(results["Wind"]["electric_to_load_series_kw"] * post["Wind"]["operating_reserve_required_fraction"]) + loadOR = sum(post["ElectricLoad"]["loads_kw"] * scen.electric_load.operating_reserve_required_fraction) + @test sum(results["ElectricLoad"]["offgrid_annual_oper_res_required_series_kwh"]) ≈ loadOR + windOR atol=1.0 + + end + + @testset "GHP" begin + """ + + This tests multiple unique aspects of GHP: + 1. REopt takes the output data of GhpGhx, creates multiple GHP options, and chooses the expected one + 2. GHP with heating and cooling "..efficiency_thermal_factors" reduces the net thermal load + 3. GHP serves only the SpaceHeatingLoad by default unless it is allowed to serve DHW + 4. GHP serves all the Cooling load + 5. Input of a custom COP map for GHP and check the GHP performance to make sure it's using it correctly + 6. Hybrid GHP capability functions as expected + + """ + # Load base inputs + input_data = JSON.parsefile("scenarios/ghp_inputs.json") + + # Modify ["GHP"]["ghpghx_inputs"] for running GhpGhx.jl + # Heat pump performance maps + cop_map_mat_header = readdlm("scenarios/ghp_cop_map_custom.csv", ',', header=true) + data = cop_map_mat_header[1] + headers = cop_map_mat_header[2] + # Generate a "records" style dictionary from the + cop_map_list = [] + for i in axes(data,1) + dict_record = Dict(name=>data[i, col] for (col, name) in enumerate(headers)) + push!(cop_map_list, dict_record) + end + input_data["GHP"]["ghpghx_inputs"][1]["cop_map_eft_heating_cooling"] = cop_map_list + + # Due to GhpGhx not being a registered package (no OSI-approved license), + # the registered REopt package cannot have GhpGhx as a "normal" dependency; + # Therefore, we only use a "ghpghx_response" (the output of GhpGhx) as an + # input to REopt to avoid GhpGhx module calls + response_1 = JSON.parsefile("scenarios/ghpghx_response.json") + response_2 = deepcopy(response_1) + # Reduce the electric consumption of response 2 which should then be the chosen system + response_2["outputs"]["yearly_total_electric_consumption_series_kw"] *= 0.5 + input_data["GHP"]["ghpghx_responses"] = [response_1, response_2] + + # Heating load + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + input_data["SpaceHeatingLoad"]["monthly_mmbtu"] = fill(1000.0, 12) + input_data["SpaceHeatingLoad"]["monthly_mmbtu"][1] = 500.0 + input_data["SpaceHeatingLoad"]["monthly_mmbtu"][end] = 1500.0 + + # Call REopt + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt([m1,m2], inputs) + + ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + @test ghp_option_chosen == 2 + + # Test GHP heating and cooling load reduced + hot_load_reduced_mmbtu = sum(results["GHP"]["space_heating_thermal_load_reduction_with_ghp_mmbtu_per_hour"]) + cold_load_reduced_tonhour = sum(results["GHP"]["cooling_thermal_load_reduction_with_ghp_ton"]) + @test hot_load_reduced_mmbtu ≈ 1440.00 atol=0.1 + @test cold_load_reduced_tonhour ≈ 761382.78 atol=0.1 + + # Test GHP serving space heating with VAV thermal efficiency improvements + heating_served_mmbtu = sum(s.ghp_option_list[ghp_option_chosen].heating_thermal_kw / REopt.KWH_PER_MMBTU) + expected_heating_served_mmbtu = 12000 * 0.8 * 0.85 # (fuel_mmbtu * boiler_effic * space_heating_efficiency_thermal_factor) + @test round(heating_served_mmbtu, digits=1) ≈ expected_heating_served_mmbtu atol=1.0 + + # Boiler serves all of the DHW load, no DHW thermal reduction due to GHP retrofit + boiler_served_mmbtu = sum(results["ExistingBoiler"]["thermal_production_series_mmbtu_per_hour"]) + expected_boiler_served_mmbtu = 3000 * 0.8 # (fuel_mmbtu * boiler_effic) + @test round(boiler_served_mmbtu, digits=1) ≈ expected_boiler_served_mmbtu atol=1.0 + + # LoadProfileChillerThermal cooling thermal is 1/cooling_efficiency_thermal_factor of GHP cooling thermal production + bau_chiller_thermal_tonhour = sum(s.cooling_load.loads_kw_thermal / REopt.KWH_THERMAL_PER_TONHOUR) + ghp_cooling_thermal_tonhour = sum(inputs.ghp_cooling_thermal_load_served_kw[1,:] / REopt.KWH_THERMAL_PER_TONHOUR) + @test round(bau_chiller_thermal_tonhour) ≈ ghp_cooling_thermal_tonhour/0.6 atol=1.0 + + # Custom heat pump COP map is used properly + ghp_option_chosen = results["GHP"]["ghp_option_chosen"] + heating_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["heating_cop_avg"] + cooling_cop_avg = s.ghp_option_list[ghp_option_chosen].ghpghx_response["outputs"]["cooling_cop_avg"] + # Average COP which includes pump power should be lower than Heat Pump only COP specified by the map + @test heating_cop_avg <= 4.0 + @test cooling_cop_avg <= 8.0 + end + + @testset "Hybrid GHX and GHP calculated costs validation" begin + ## Hybrid GHP validation. + # Load base inputs + input_data = JSON.parsefile("scenarios/ghp_financial_hybrid.json") + + inputs = REoptInputs(input_data) + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt([m1,m2], inputs) + + calculated_ghp_capital_costs = ((input_data["GHP"]["ghpghx_responses"][1]["outputs"]["number_of_boreholes"]* + input_data["GHP"]["ghpghx_responses"][1]["outputs"]["length_boreholes_ft"]* + inputs.s.ghp_option_list[1].installed_cost_ghx_per_ft) + + (inputs.s.ghp_option_list[1].installed_cost_heatpump_per_ton* + input_data["GHP"]["ghpghx_responses"][1]["outputs"]["peak_combined_heatpump_thermal_ton"]* + inputs.s.ghp_option_list[1].heatpump_capacity_sizing_factor_on_peak_load) + + (inputs.s.ghp_option_list[1].building_sqft* + inputs.s.ghp_option_list[1].installed_cost_building_hydronic_loop_per_sqft)) + + @test results["Financial"]["initial_capital_costs"] ≈ calculated_ghp_capital_costs atol=0.1 + + calculated_om_costs = inputs.s.ghp_option_list[1].building_sqft* + inputs.s.ghp_option_list[1].om_cost_per_sqft_year * inputs.third_party_factor * inputs.pwf_om + + @test results["Financial"]["lifecycle_om_costs_before_tax"] ≈ calculated_om_costs atol=0.1 + + calc_om_cost_after_tax = calculated_om_costs*(1-inputs.s.financial.owner_tax_rate_fraction) + @test results["Financial"]["lifecycle_om_costs_after_tax"] - calc_om_cost_after_tax < 0.0001 + + @test abs(results["Financial"]["lifecycle_capital_costs_plus_om_after_tax"] - (calc_om_cost_after_tax + 0.7*results["Financial"]["initial_capital_costs"])) < 150.0 + + @test abs(results["Financial"]["lifecycle_capital_costs"] - 0.7*results["Financial"]["initial_capital_costs"]) < 150.0 + + @test abs(results["Financial"]["npv"] - 840621) < 1.0 + @test results["Financial"]["simple_payback_years"] - 5.09 < 0.1 + @test results["Financial"]["internal_rate_of_return"] - 0.18 < 0.01 + + @test haskey(results["ExistingBoiler"], "year_one_fuel_cost_before_tax_bau") + + ## Hybrid + input_data["GHP"]["ghpghx_responses"] = [JSON.parsefile("scenarios/ghpghx_hybrid_results.json")] + input_data["GHP"]["avoided_capex_by_ghp_present_value"] = 1.0e6 + input_data["GHP"]["ghx_useful_life_years"] = 35 + + inputs = REoptInputs(input_data) + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt([m1,m2], inputs) + + pop!(input_data["GHP"], "ghpghx_inputs", nothing) + pop!(input_data["GHP"], "ghpghx_responses", nothing) + ghp_obj = REopt.GHP(JSON.parsefile("scenarios/ghpghx_hybrid_results.json"), input_data["GHP"]) + + calculated_ghx_residual_value = ghp_obj.ghx_only_capital_cost* + ( + (ghp_obj.ghx_useful_life_years - inputs.s.financial.analysis_years)/ghp_obj.ghx_useful_life_years + )/( + (1 + inputs.s.financial.offtaker_discount_rate_fraction)^inputs.s.financial.analysis_years + ) + + @test results["GHP"]["ghx_residual_value_present_value"] ≈ calculated_ghx_residual_value atol=0.1 + @test inputs.s.ghp_option_list[1].is_ghx_hybrid = true + + # Test centralized GHP cost calculations + input_data_wwhp = JSON.parsefile("scenarios/ghp_inputs_wwhp.json") + response_wwhp = JSON.parsefile("scenarios/ghpghx_response_wwhp.json") + input_data_wwhp["GHP"]["ghpghx_responses"] = [response_wwhp] + + s_wwhp = Scenario(input_data_wwhp) + inputs_wwhp = REoptInputs(s_wwhp) + m3 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results_wwhp = run_reopt(m3, inputs_wwhp) + + + heating_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_heating_pump_per_ton"] * + input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_heating_heatpump_thermal_ton"] + + cooling_hp_cost = input_data_wwhp["GHP"]["installed_cost_wwhp_cooling_pump_per_ton"] * + input_data_wwhp["GHP"]["heatpump_capacity_sizing_factor_on_peak_load"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["peak_cooling_heatpump_thermal_ton"] + + ghx_cost = input_data_wwhp["GHP"]["installed_cost_ghx_per_ft"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["number_of_boreholes"] * + results_wwhp["GHP"]["ghpghx_chosen_outputs"]["length_boreholes_ft"] + + # CAPEX reduction factor for 30% ITC, 5-year MACRS, assuming 26% tax rate and 8.3% discount + capex_reduction_factor = 0.455005797 + + calculated_ghp_capex = (heating_hp_cost + cooling_hp_cost + ghx_cost) * (1 - capex_reduction_factor) + + reopt_ghp_capex = results_wwhp["Financial"]["lifecycle_capital_costs"] + @test calculated_ghp_capex ≈ reopt_ghp_capex atol=300 + end + + @testset "Cambium Emissions" begin + """ + 1) Location in contiguous US + - Correct data from Cambium (returned location and values) + - Adjusted for load year vs. Cambium year (which starts on Sunday) vs. AVERT year (2022 currently) + - co2 pct increase should be zero + 2) HI and AK locations + - Should use AVERT data and give an "info" message + - Adjust for load year vs. AVERT year + - co2 pct increase should be the default value unless user provided value + 3) International + - all emissions should be zero unless provided + """ + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # post_name = "cambium.json" - # post = JSON.parsefile("./scenarios/$post_name") + post_name = "cambium.json" + post = JSON.parsefile("./scenarios/$post_name") - # cities = Dict( - # "Denver" => (39.7413753050447, -104.99965032911328), - # "Fairbanks" => (64.84053664406181, -147.71913656313163), - # "Santiago" => (-33.44485437650408, -70.69031905547853) - # ) + cities = Dict( + "Denver" => (39.7413753050447, -104.99965032911328), + "Fairbanks" => (64.84053664406181, -147.71913656313163), + "Santiago" => (-33.44485437650408, -70.69031905547853) + ) - # # 1) Location in contiguous US - # city = "Denver" - # post["Site"]["latitude"] = cities[city][1] - # post["Site"]["longitude"] = cities[city][2] - # post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] - # post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri - # scen = Scenario(post) + # 1) Location in contiguous US + city = "Denver" + post["Site"]["latitude"] = cities[city][1] + post["Site"]["longitude"] = cities[city][2] + post["ElectricLoad"]["loads_kw"] = [20 for i in range(1,8760)] + post["ElectricLoad"]["year"] = 2021 # 2021 First day is Fri + scen = Scenario(post) - # @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" - # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - # @test scen.electric_utility.cambium_emissions_region == "RMPAc" - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 - # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday - # @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday - # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region - # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data - # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # # 2) AK location - # city = "Fairbanks" - # post["Site"]["latitude"] = cities[city][1] - # post["Site"]["longitude"] = cities[city][2] - # scen = Scenario(post) + @test scen.electric_utility.avert_emissions_region == "Rocky Mountains" + @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + @test scen.electric_utility.cambium_emissions_region == "RMPAc" + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 0.394608 rtol=1e-3 + @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[1] ≈ 0.677942 rtol=1e-4 # Should start on Friday + @test scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh[8760] ≈ 0.6598207198 rtol=1e-5 # Should end on Friday + @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) / 8760 ≈ 0.00061165 rtol=1e-5 # check avg from AVERT data for RM region + @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ 0 atol=1e-5 # should be 0 with Cambium data + @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # 2) AK location + city = "Fairbanks" + post["Site"]["latitude"] = cities[city][1] + post["Site"]["longitude"] = cities[city][2] + scen = Scenario(post) - # @test scen.electric_utility.avert_emissions_region == "Alaska" - # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 - # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used - # @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value - # @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data - # @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] - # @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] - - # # 3) International location - # city = "Santiago" - # post["Site"]["latitude"] = cities[city][1] - # post["Site"]["longitude"] = cities[city][2] - # scen = Scenario(post) - - # @test scen.electric_utility.avert_emissions_region == "" - # @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 - # @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" - # @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 - # @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 + @test scen.electric_utility.avert_emissions_region == "Alaska" + @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 0 atol=1e-5 + @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) / 8760 ≈ 1.29199999 rtol=1e-3 # check that data from eGRID (AVERT data file) is used + @test scen.electric_utility.emissions_factor_CO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["CO2e"] # should get updated to this value + @test scen.electric_utility.emissions_factor_SO2_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["SO2"] # should be 2.163% for AVERT data + @test scen.electric_utility.emissions_factor_NOx_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["NOx"] + @test scen.electric_utility.emissions_factor_PM25_decrease_fraction ≈ REopt.EMISSIONS_DECREASE_DEFAULTS["PM25"] + + # 3) International location + city = "Santiago" + post["Site"]["latitude"] = cities[city][1] + post["Site"]["longitude"] = cities[city][2] + scen = Scenario(post) + + @test scen.electric_utility.avert_emissions_region == "" + @test scen.electric_utility.distance_to_avert_emissions_region_meters ≈ 5.521032136418236e6 atol=1.0 + @test scen.electric_utility.cambium_emissions_region == "NA - Cambium data not used for climate emissions" + @test sum(scen.electric_utility.emissions_factor_series_lb_CO2_per_kwh) ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_NOx_per_kwh) ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_SO2_per_kwh) ≈ 0 + @test sum(scen.electric_utility.emissions_factor_series_lb_PM25_per_kwh) ≈ 0 - # end - - # @testset "Emissions and Renewable Energy Percent" begin - # #renewable energy and emissions reduction targets - # include_exported_RE_in_total = [true,false,true] - # include_exported_ER_in_total = [true,false,true] - # RE_target = [0.8,nothing,nothing] - # ER_target = [nothing,0.8,nothing] - # with_outage = [true,false,false] - - # for i in range(1, stop=3) - # if i == 3 - # inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") - # else - # inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") - # end - # if i == 1 - # inputs["Site"]["latitude"] = 37.746 - # inputs["Site"]["longitude"] = -122.448 - # # inputs["ElectricUtility"]["emissions_region"] = "California" - # end - # inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] - # inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] - # inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end - # inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] - # inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] - # inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] - # if with_outage[i] - # outage_start_hour = 4032 - # outage_duration = 2000 #hrs - # inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 - # inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration - # inputs["Generator"]["max_kw"] = 20 - # inputs["Generator"]["existing_kw"] = 2 - # inputs["Generator"]["fuel_avail_gal"] = 1000 - # end - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt([m1, m2], inputs) - - # if !isnothing(ER_target[i]) - # ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] - # @test ER_target[i] ≈ ER_fraction_out atol=1e-3 - # lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] - # lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] - # ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out - # ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) - # @test ER_fraction_diff ≈ 0.0 atol=1e-2 - # end - - # annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] - # yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] - # yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] - # yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out - # @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 - # if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 - # end + end + + @testset "Emissions and Renewable Energy Percent" begin + #renewable energy and emissions reduction targets + include_exported_RE_in_total = [true,false,true] + include_exported_ER_in_total = [true,false,true] + RE_target = [0.8,nothing,nothing] + ER_target = [nothing,0.8,nothing] + with_outage = [true,false,false] + + for i in range(1, stop=3) + if i == 3 + inputs = JSON.parsefile("./scenarios/re_emissions_with_thermal.json") + else + inputs = JSON.parsefile("./scenarios/re_emissions_elec_only.json") + end + if i == 1 + inputs["Site"]["latitude"] = 37.746 + inputs["Site"]["longitude"] = -122.448 + # inputs["ElectricUtility"]["emissions_region"] = "California" + end + inputs["Site"]["include_exported_renewable_electricity_in_total"] = include_exported_RE_in_total[i] + inputs["Site"]["include_exported_elec_emissions_in_total"] = include_exported_ER_in_total[i] + inputs["Site"]["renewable_electricity_min_fraction"] = if isnothing(RE_target[i]) 0.0 else RE_target[i] end + inputs["Site"]["renewable_electricity_max_fraction"] = RE_target[i] + inputs["Site"]["CO2_emissions_reduction_min_fraction"] = ER_target[i] + inputs["Site"]["CO2_emissions_reduction_max_fraction"] = ER_target[i] + if with_outage[i] + outage_start_hour = 4032 + outage_duration = 2000 #hrs + inputs["ElectricUtility"]["outage_start_time_step"] = outage_start_hour + 1 + inputs["ElectricUtility"]["outage_end_time_step"] = outage_start_hour + 1 + outage_duration + inputs["Generator"]["max_kw"] = 20 + inputs["Generator"]["existing_kw"] = 2 + inputs["Generator"]["fuel_avail_gal"] = 1000 + end + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt([m1, m2], inputs) + + if !isnothing(ER_target[i]) + ER_fraction_out = results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] + @test ER_target[i] ≈ ER_fraction_out atol=1e-3 + lifecycle_emissions_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2"] + lifecycle_emissions_bau_tonnes_CO2_out = results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] + ER_fraction_calced_out = (lifecycle_emissions_bau_tonnes_CO2_out-lifecycle_emissions_tonnes_CO2_out)/lifecycle_emissions_bau_tonnes_CO2_out + ER_fraction_diff = abs(ER_fraction_calced_out-ER_fraction_out) + @test ER_fraction_diff ≈ 0.0 atol=1e-2 + end + + annual_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_tonnes_CO2"] + yr1_fuel_emissions_tonnes_CO2_out = results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] + yr1_grid_emissions_tonnes_CO2_out = results["ElectricUtility"]["annual_emissions_tonnes_CO2"] + yr1_total_emissions_calced_tonnes_CO2 = yr1_fuel_emissions_tonnes_CO2_out + yr1_grid_emissions_tonnes_CO2_out + @test annual_emissions_tonnes_CO2_out ≈ yr1_total_emissions_calced_tonnes_CO2 atol=1e-1 + if haskey(results["Financial"],"breakeven_cost_of_emissions_reduction_per_tonne_CO2") + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] >= 0.0 + end - # if i == 1 - # @test results["PV"]["size_kw"] ≈ 59.7222 atol=1e-1 - # @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 - # @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 - # @test results["Generator"]["size_kw"] ≈ 9.13 atol=1e-1 - # @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 - # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.148375 atol=1e-4 - # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.57403012 atol=1e-4 - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 332.4 atol=1 - # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.85 atol=1e-2 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.427 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8459.45 atol=1 - # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 236.95 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 148.54 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 - # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 27.813 atol=1e-1 - # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 556.26 - # elseif i == 2 - # #commented out values are results using same levelization factor as API - # @test results["PV"]["size_kw"] ≈ 106.13 atol=1 - # @test results["ElectricStorage"]["size_kw"] ≈ 20.09 atol=1 # 20.29 - # @test results["ElectricStorage"]["size_kwh"] ≈ 170.94 atol=1 - # @test !haskey(results, "Generator") - # # Renewable energy - # @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 - # @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 - # @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13308.5 atol=10 # 13542.62 atol=10 - # @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 - # # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven - # @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 491.5 atol=1e-1 - # @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 - # @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - # @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8397.85 atol=1 - # @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 - # @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 - # @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 - # @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 + if i == 1 + @test results["PV"]["size_kw"] ≈ 59.7222 atol=1e-1 + @test results["ElectricStorage"]["size_kw"] ≈ 0.0 atol=1e-1 + @test results["ElectricStorage"]["size_kwh"] ≈ 0.0 atol=1e-1 + @test results["Generator"]["size_kw"] ≈ 9.13 atol=1e-1 + @test results["Site"]["total_renewable_energy_fraction"] ≈ 0.8 + @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.148375 atol=1e-4 + @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.57403012 atol=1e-4 + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 332.4 atol=1 + @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.85 atol=1e-2 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 7.427 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8459.45 atol=1 + @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ 236.95 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 148.54 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 + @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 27.813 atol=1e-1 + @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 556.26 + elseif i == 2 + #commented out values are results using same levelization factor as API + @test results["PV"]["size_kw"] ≈ 106.13 atol=1 + @test results["ElectricStorage"]["size_kw"] ≈ 20.09 atol=1 # 20.29 + @test results["ElectricStorage"]["size_kwh"] ≈ 170.94 atol=1 + @test !haskey(results, "Generator") + # Renewable energy + @test results["Site"]["renewable_electricity_fraction"] ≈ 0.78586 atol=1e-3 + @test results["Site"]["renewable_electricity_fraction_bau"] ≈ 0.132118 atol=1e-3 #0.1354 atol=1e-3 + @test results["Site"]["annual_renewable_electricity_kwh_bau"] ≈ 13308.5 atol=10 # 13542.62 atol=10 + @test results["Site"]["total_renewable_energy_fraction_bau"] ≈ 0.132118 atol=1e-3 # 0.1354 atol=1e-3 + # CO2 emissions - totals ≈ from grid, from fuelburn, ER, $/tCO2 breakeven + @test results["Site"]["lifecycle_emissions_reduction_CO2_fraction"] ≈ 0.8 atol=1e-3 # 0.8 + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ 491.5 atol=1e-1 + @test results["Site"]["annual_emissions_tonnes_CO2"] ≈ 11.662 atol=1 + @test results["Site"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + @test results["Financial"]["lifecycle_emissions_cost_climate"] ≈ 8397.85 atol=1 + @test results["Site"]["lifecycle_emissions_tonnes_CO2_bau"] ≈ 1166.19 atol=1 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] ≈ 0.0 atol=1 # 0.0 + @test results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2_bau"] ≈ 0.0 atol=1 # 0.0 + @test results["ElectricUtility"]["annual_emissions_tonnes_CO2_bau"] ≈ 58.3095 atol=1 + @test results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] ≈ 233.24 atol=1 - # #also test CO2 breakeven cost - # inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - # inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] - # inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] - # inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] - # inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] - # inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] - # inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] - # inputs["Settings"]["include_climate_in_objective"] = true - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) - # results = run_reopt([m1, m2], inputs) - # @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 - # elseif i == 3 - # @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 - # @test !haskey(results, "Wind") - # @test !haskey(results, "ElectricStorage") - # @test !haskey(results, "Generator") - # @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 - # @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 - # @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 - # @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 - # yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] - # nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) - # TONNE_PER_LB = 1/2204.62 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - # @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 - # @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 - # @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 - # @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 - # @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 - # @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 - # @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 - # KWH_PER_MMBTU = 293.07107 - # annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] - # annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU - # @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 - # end - # end - # end - - # @testset "Back pressure steam turbine" begin - # """ - # Validation to ensure that: - # 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application - # 2) SteamTurbine serves the heating load with the condensing steam - - # """ - # # Setup inputs, make heating load large to entice SteamTurbine - # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - # latitude = input_data["Site"]["latitude"] - # longitude = input_data["Site"]["longitude"] - # building = "Hospital" - # elec_load_multiplier = 5.0 - # heat_load_multiplier = 100.0 - # input_data["ElectricLoad"]["doe_reference_name"] = building - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = building - # input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building - # elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) - # input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) - # space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - # input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - # dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) - # input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], inputs) - - # # The expected values below were directly copied from the REopt_API V2 expected values - # @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 - # @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 - # @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 - # @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 - # @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 - # @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 - - # # BAU boiler loads - # load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency - # load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency - - # # ExistingBoiler and SteamTurbine production - # boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] - # boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] - # boiler_total = boiler_to_load + boiler_to_st - # st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] - - # # Fuel/thermal **consumption** - # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - - # # Check that all thermal supply to load meets the BAU load - # thermal_to_load = sum(boiler_to_load) + sum(st_to_load) - # @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 - - # # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet - # steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] - # net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) - # @test net_electric_efficiency ≈ 0.052 atol=0.005 - - # # Check that the max production of the boiler is still less than peak heating load times thermal factor - # factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] - # boiler_capacity = maximum(load_boiler_thermal) * factor - # @test maximum(boiler_total) <= boiler_capacity - # end - - # @testset "All heating supply/demand/storage energy balance" begin - # """ - # Validation to ensure that: - # 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included - # 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption - # 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False - - # :return: - # """ - - # # Start with steam turbine inputs, but adding a bunch below - # input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") - # input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" - # # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks - # input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" - # delete!(input_data, "DomesticHotWaterLoad") - - # # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances - # input_data["SteamTurbine"]["min_kw"] = 30.0 - # input_data["SteamTurbine"]["max_kw"] = 30.0 - - # # Add CHP - # input_data["CHP"] = Dict{Any, Any}([ - # ("prime_mover", "recip_engine"), - # ("size_class", 4), - # ("min_kw", 250.0), - # ("min_allowable_kw", 0.0), - # ("max_kw", 250.0), - # ("can_supply_steam_turbine", false), - # ("fuel_cost_per_mmbtu", 8.0), - # ("cooling_thermal_factor", 1.0) - # ]) - - # input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 - - # # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) - # input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") - # input_data["AbsorptionChiller"] = Dict{Any, Any}([ - # ("min_ton", 600.0), - # ("max_ton", 600.0), - # ("cop_thermal", 0.7), - # ("installed_cost_per_ton", 500.0), - # ("om_cost_per_ton", 0.5), - # ("heating_load_input", "SpaceHeating") - # ]) - - # # Add Hot TES - # input_data["HotThermalStorage"] = Dict{Any, Any}([ - # ("min_gal", 50000.0), - # ("max_gal", 50000.0) - # ]) - - # s = Scenario(input_data) - # inputs = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) - # results = run_reopt(m, inputs) - - # thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] - # thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech - # tech_to_thermal_load = Dict{Any, Any}() - # for tech in thermal_techs - # tech_to_thermal_load[tech] = Dict{Any, Any}() - # for load in thermal_loads - # if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") - # tech_to_thermal_load[tech][load] = [0.0] * 8760 - # else - # if load == "waste" - # tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] - # else - # tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] - # end - # end - # end - # end - # # Hot TES is the other thermal supply - # hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] - - # # BAU boiler loads - # load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU - # load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] - - # # Fuel/thermal **consumption** - # boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] - # chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] - # steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] - # absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] - - # # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked - # alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) - # thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) - # @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 - - # # Check that all thermal to steam turbine is equal to steam turbine thermal consumption - # alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) - # @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 - - # # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False - # for tech in ["ExistingBoiler", "CHP"] - # if !(tech in inputs.techs.can_supply_steam_turbine) - # @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 - # end - # end - # end - - # @testset "Electric Heater" begin - # d = JSON.parsefile("./scenarios/electric_heater.json") - # d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.4 * 8760 - # d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.4 * 8760 - # d["ProcessHeatLoad"]["annual_mmbtu"] = 0.2 * 8760 - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - - # #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased - # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 - # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 - - # d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 - # d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 - # d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - - # annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour - # annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP - # annual_energy_supplied = 87600 + annual_electric_heater_consumption - - # #Second run: ElectricHeater produces the required heat with free electricity - # @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 - # @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 - # @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 - - # end - - # @testset "Process Heat Load" begin - # d = JSON.parsefile("./scenarios/process_heat.json") + #also test CO2 breakeven cost + inputs["PV"]["min_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + inputs["PV"]["max_kw"] = results["PV"]["size_kw"] - inputs["PV"]["existing_kw"] + inputs["ElectricStorage"]["min_kw"] = results["ElectricStorage"]["size_kw"] + inputs["ElectricStorage"]["max_kw"] = results["ElectricStorage"]["size_kw"] + inputs["ElectricStorage"]["min_kwh"] = results["ElectricStorage"]["size_kwh"] + inputs["ElectricStorage"]["max_kwh"] = results["ElectricStorage"]["size_kwh"] + inputs["Financial"]["CO2_cost_per_tonne"] = results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] + inputs["Settings"]["include_climate_in_objective"] = true + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "presolve" => "on")) + results = run_reopt([m1, m2], inputs) + @test results["Financial"]["breakeven_cost_of_emissions_reduction_per_tonne_CO2"] ≈ inputs["Financial"]["CO2_cost_per_tonne"] atol=1e-1 + elseif i == 3 + @test results["PV"]["size_kw"] ≈ 20.0 atol=1e-1 + @test !haskey(results, "Wind") + @test !haskey(results, "ElectricStorage") + @test !haskey(results, "Generator") + @test results["CHP"]["size_kw"] ≈ 200.0 atol=1e-1 + @test results["AbsorptionChiller"]["size_ton"] ≈ 400.0 atol=1e-1 + @test results["HotThermalStorage"]["size_gal"] ≈ 50000 atol=1e1 + @test results["ColdThermalStorage"]["size_gal"] ≈ 30000 atol=1e1 + yr1_nat_gas_mmbtu = results["ExistingBoiler"]["annual_fuel_consumption_mmbtu"] + results["CHP"]["annual_fuel_consumption_mmbtu"] + nat_gas_emissions_lb_per_mmbtu = Dict("CO2"=>117.03, "NOx"=>0.09139, "SO2"=>0.000578592, "PM25"=>0.007328833) + TONNE_PER_LB = 1/2204.62 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_CO2"] ≈ nat_gas_emissions_lb_per_mmbtu["CO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_NOx"] ≈ nat_gas_emissions_lb_per_mmbtu["NOx"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_SO2"] ≈ nat_gas_emissions_lb_per_mmbtu["SO2"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + @test results["Site"]["annual_emissions_from_fuelburn_tonnes_PM25"] ≈ nat_gas_emissions_lb_per_mmbtu["PM25"] * yr1_nat_gas_mmbtu * TONNE_PER_LB atol=1e-2 + @test results["Site"]["lifecycle_emissions_tonnes_CO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_CO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_CO2"] atol=1 + @test results["Site"]["lifecycle_emissions_tonnes_NOx"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_NOx"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_NOx"] atol=0.1 + @test results["Site"]["lifecycle_emissions_tonnes_SO2"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_SO2"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_SO2"] atol=1e-2 + @test results["Site"]["lifecycle_emissions_tonnes_PM25"] ≈ results["Site"]["lifecycle_emissions_from_fuelburn_tonnes_PM25"] + results["ElectricUtility"]["lifecycle_emissions_tonnes_PM25"] atol=1.5e-2 + @test results["Site"]["annual_renewable_electricity_kwh"] ≈ results["PV"]["annual_energy_produced_kwh"] + inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_electric_production_kwh"] atol=1 + @test results["Site"]["renewable_electricity_fraction"] ≈ results["Site"]["annual_renewable_electricity_kwh"] / results["ElectricLoad"]["annual_calculated_kwh"] atol=1e-6#0.044285 atol=1e-4 + KWH_PER_MMBTU = 293.07107 + annual_RE_kwh = inputs["CHP"]["fuel_renewable_energy_fraction"] * results["CHP"]["annual_thermal_production_mmbtu"] * KWH_PER_MMBTU + results["Site"]["annual_renewable_electricity_kwh"] + annual_heat_kwh = (results["CHP"]["annual_thermal_production_mmbtu"] + results["ExistingBoiler"]["annual_thermal_production_mmbtu"]) * KWH_PER_MMBTU + @test results["Site"]["total_renewable_energy_fraction"] ≈ annual_RE_kwh / (annual_heat_kwh + results["ElectricLoad"]["annual_calculated_kwh"]) atol=1e-6 + end + end + end + + @testset "Back pressure steam turbine" begin + """ + Validation to ensure that: + 1) ExistingBoiler provides the thermal energy (steam) to a backpressure SteamTurbine for CHP application + 2) SteamTurbine serves the heating load with the condensing steam + + """ + # Setup inputs, make heating load large to entice SteamTurbine + input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + latitude = input_data["Site"]["latitude"] + longitude = input_data["Site"]["longitude"] + building = "Hospital" + elec_load_multiplier = 5.0 + heat_load_multiplier = 100.0 + input_data["ElectricLoad"]["doe_reference_name"] = building + input_data["SpaceHeatingLoad"]["doe_reference_name"] = building + input_data["DomesticHotWaterLoad"]["doe_reference_name"] = building + elec_load = REopt.ElectricLoad(latitude=latitude, longitude=longitude, doe_reference_name=building) + input_data["ElectricLoad"]["annual_kwh"] = elec_load_multiplier * sum(elec_load.loads_kw) + space_load = REopt.SpaceHeatingLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + input_data["SpaceHeatingLoad"]["annual_mmbtu"] = heat_load_multiplier * space_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + dhw_load = REopt.DomesticHotWaterLoad(latitude=latitude, longitude=longitude, doe_reference_name=building, existing_boiler_efficiency=input_data["ExistingBoiler"]["efficiency"]) + input_data["DomesticHotWaterLoad"]["annual_mmbtu"] = heat_load_multiplier * dhw_load.annual_mmbtu / input_data["ExistingBoiler"]["efficiency"] + s = Scenario(input_data) + inputs = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], inputs) + + # The expected values below were directly copied from the REopt_API V2 expected values + @test results["Financial"]["lcc"] ≈ 189359280.0 rtol=0.001 + @test results["Financial"]["npv"] ≈ 8085233.0 rtol=0.01 + @test results["SteamTurbine"]["size_kw"] ≈ 2616.418 atol=1.0 + @test results["SteamTurbine"]["annual_thermal_consumption_mmbtu"] ≈ 1000557.6 rtol=0.001 + @test results["SteamTurbine"]["annual_electric_production_kwh"] ≈ 18970374.6 rtol=0.001 + @test results["SteamTurbine"]["annual_thermal_production_mmbtu"] ≈ 924045.1 rtol=0.001 + + # BAU boiler loads + load_boiler_fuel = (s.space_heating_load.loads_kw + s.dhw_load.loads_kw) ./ REopt.KWH_PER_MMBTU ./ s.existing_boiler.efficiency + load_boiler_thermal = load_boiler_fuel * s.existing_boiler.efficiency + + # ExistingBoiler and SteamTurbine production + boiler_to_load = results["ExistingBoiler"]["thermal_to_load_series_mmbtu_per_hour"] + boiler_to_st = results["ExistingBoiler"]["thermal_to_steamturbine_series_mmbtu_per_hour"] + boiler_total = boiler_to_load + boiler_to_st + st_to_load = results["SteamTurbine"]["thermal_to_load_series_mmbtu_per_hour"] + + # Fuel/thermal **consumption** + boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + + # Check that all thermal supply to load meets the BAU load + thermal_to_load = sum(boiler_to_load) + sum(st_to_load) + @test thermal_to_load ≈ sum(load_boiler_thermal) atol=1.0 + + # Check the net electric efficiency of Boiler->SteamTurbine (electric out/fuel in) with the expected value from the Fact Sheet + steamturbine_electric = results["SteamTurbine"]["electric_production_series_kw"] + net_electric_efficiency = sum(steamturbine_electric) / (sum(boiler_fuel) * REopt.KWH_PER_MMBTU) + @test net_electric_efficiency ≈ 0.052 atol=0.005 + + # Check that the max production of the boiler is still less than peak heating load times thermal factor + factor = input_data["ExistingBoiler"]["max_thermal_factor_on_peak_load"] + boiler_capacity = maximum(load_boiler_thermal) * factor + @test maximum(boiler_total) <= boiler_capacity + end + + @testset "All heating supply/demand/storage energy balance" begin + """ + Validation to ensure that: + 1) Heat balance is correct with SteamTurbine (backpressure), CHP, HotTES, and AbsorptionChiller included + 2) The sum of a all thermal from techs supplying SteamTurbine is equal to SteamTurbine thermal consumption + 3) Techs are not supplying SteamTurbine with thermal if can_supply_steam_turbine = False + + :return: + """ + + # Start with steam turbine inputs, but adding a bunch below + input_data = JSON.parsefile("scenarios/backpressure_steamturbine_inputs.json") + input_data["ElectricLoad"]["doe_reference_name"] = "Hospital" + # Add SpaceHeatingLoad building for heating loads, ignore DomesticHotWaterLoad for simplicity of energy balance checks + input_data["SpaceHeatingLoad"]["doe_reference_name"] = "Hospital" + delete!(input_data, "DomesticHotWaterLoad") + + # Fix size of SteamTurbine, even if smaller than practical, because we're just looking at energy balances + input_data["SteamTurbine"]["min_kw"] = 30.0 + input_data["SteamTurbine"]["max_kw"] = 30.0 + + # Add CHP + input_data["CHP"] = Dict{Any, Any}([ + ("prime_mover", "recip_engine"), + ("size_class", 4), + ("min_kw", 250.0), + ("min_allowable_kw", 0.0), + ("max_kw", 250.0), + ("can_supply_steam_turbine", false), + ("fuel_cost_per_mmbtu", 8.0), + ("cooling_thermal_factor", 1.0) + ]) + + input_data["Financial"]["chp_fuel_cost_escalation_rate_fraction"] = 0.034 + + # Add CoolingLoad and AbsorptionChiller so we can test the energy balance on AbsorptionChiller too (thermal consumption) + input_data["CoolingLoad"] = Dict{Any, Any}("doe_reference_name" => "Hospital") + input_data["AbsorptionChiller"] = Dict{Any, Any}([ + ("min_ton", 600.0), + ("max_ton", 600.0), + ("cop_thermal", 0.7), + ("installed_cost_per_ton", 500.0), + ("om_cost_per_ton", 0.5), + ("heating_load_input", "SpaceHeating") + ]) + + # Add Hot TES + input_data["HotThermalStorage"] = Dict{Any, Any}([ + ("min_gal", 50000.0), + ("max_gal", 50000.0) + ]) + + s = Scenario(input_data) + inputs = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false, "mip_rel_gap" => 0.01)) + results = run_reopt(m, inputs) + + thermal_techs = ["ExistingBoiler", "CHP", "SteamTurbine"] + thermal_loads = ["load", "storage", "steamturbine", "waste"] # We don't track AbsorptionChiller thermal consumption by tech + tech_to_thermal_load = Dict{Any, Any}() + for tech in thermal_techs + tech_to_thermal_load[tech] = Dict{Any, Any}() + for load in thermal_loads + if (tech == "SteamTurbine" && load == "steamturbine") || (load == "waste" && tech != "CHP") + tech_to_thermal_load[tech][load] = [0.0] * 8760 + else + if load == "waste" + tech_to_thermal_load[tech][load] = results[tech]["thermal_curtailed_series_mmbtu_per_hour"] + else + tech_to_thermal_load[tech][load] = results[tech]["thermal_to_"*load*"_series_mmbtu_per_hour"] + end + end + end + end + # Hot TES is the other thermal supply + hottes_to_load = results["HotThermalStorage"]["storage_to_load_series_mmbtu_per_hour"] + + # BAU boiler loads + load_boiler_fuel = s.space_heating_load.loads_kw / input_data["ExistingBoiler"]["efficiency"] ./ REopt.KWH_PER_MMBTU + load_boiler_thermal = load_boiler_fuel .* input_data["ExistingBoiler"]["efficiency"] + + # Fuel/thermal **consumption** + boiler_fuel = results["ExistingBoiler"]["fuel_consumption_series_mmbtu_per_hour"] + chp_fuel_total = results["CHP"]["annual_fuel_consumption_mmbtu"] + steamturbine_thermal_in = results["SteamTurbine"]["thermal_consumption_series_mmbtu_per_hour"] + absorptionchiller_thermal_in = results["AbsorptionChiller"]["thermal_consumption_series_mmbtu_per_hour"] + + # Check that all thermal supply to load meets the BAU load plus AbsorptionChiller load which is not explicitly tracked + alltechs_thermal_to_load_total = sum([sum(tech_to_thermal_load[tech]["load"]) for tech in thermal_techs]) + sum(hottes_to_load) + thermal_load_total = sum(load_boiler_thermal) + sum(absorptionchiller_thermal_in) + @test alltechs_thermal_to_load_total ≈ thermal_load_total rtol=1e-5 + + # Check that all thermal to steam turbine is equal to steam turbine thermal consumption + alltechs_thermal_to_steamturbine_total = sum([sum(tech_to_thermal_load[tech]["steamturbine"]) for tech in ["ExistingBoiler", "CHP"]]) + @test alltechs_thermal_to_steamturbine_total ≈ sum(steamturbine_thermal_in) atol=3 + + # Check that "thermal_to_steamturbine" is zero for each tech which has input of can_supply_steam_turbine as False + for tech in ["ExistingBoiler", "CHP"] + if !(tech in inputs.techs.can_supply_steam_turbine) + @test sum(tech_to_thermal_load[tech]["steamturbine"]) == 0.0 + end + end + end + + @testset "Electric Heater" begin + d = JSON.parsefile("./scenarios/electric_heater.json") + d["SpaceHeatingLoad"]["annual_mmbtu"] = 0.4 * 8760 + d["DomesticHotWaterLoad"]["annual_mmbtu"] = 0.4 * 8760 + d["ProcessHeatLoad"]["annual_mmbtu"] = 0.2 * 8760 + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + + #first run: Boiler produces the required heat instead of the electric heater - electric heater should not be purchased + @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 + @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ 0.0 atol=0.1 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 87600.0 atol=0.1 + + d["ExistingBoiler"]["fuel_cost_per_mmbtu"] = 100 + d["ElectricHeater"]["installed_cost_per_mmbtu_per_hour"] = 1.0 + d["ElectricTariff"]["monthly_energy_rates"] = [0,0,0,0,0,0,0,0,0,0,0,0] + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + + annual_thermal_prod = 0.8 * 8760 #80% efficient boiler --> 0.8 MMBTU of heat load per hour + annual_electric_heater_consumption = annual_thermal_prod * REopt.KWH_PER_MMBTU #1.0 COP + annual_energy_supplied = 87600 + annual_electric_heater_consumption + + #Second run: ElectricHeater produces the required heat with free electricity + @test results["ElectricHeater"]["size_mmbtu_per_hour"] ≈ 0.8 atol=0.1 + @test results["ElectricHeater"]["annual_thermal_production_mmbtu"] ≈ annual_thermal_prod rtol=1e-4 + @test results["ElectricHeater"]["annual_electric_consumption_kwh"] ≈ annual_electric_heater_consumption rtol=1e-4 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ annual_energy_supplied rtol=1e-4 + + end + + @testset "Process Heat Load" begin + d = JSON.parsefile("./scenarios/process_heat.json") - # # Test set 1: Boiler has free fuel, no emissions, and serves all heating load. - # d["Boiler"]["fuel_cost_per_mmbtu"] = 0.0 - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 - # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0.0 atol=0.1 + # Test set 1: Boiler has free fuel, no emissions, and serves all heating load. + d["Boiler"]["fuel_cost_per_mmbtu"] = 0.0 + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 + @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test results["ElectricUtility"]["annual_energy_supplied_kwh"] ≈ 0.0 atol=0.1 - # #Test set 2: Boiler only serves process heat - # d["Boiler"]["can_serve_dhw"] = false - # d["Boiler"]["can_serve_space_heating"] = false - # d["Boiler"]["can_serve_process_heat"] = true - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 8.0 atol=0.1 - # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + #Test set 2: Boiler only serves process heat + d["Boiler"]["can_serve_dhw"] = false + d["Boiler"]["can_serve_space_heating"] = false + d["Boiler"]["can_serve_process_heat"] = true + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 8.0 atol=0.1 + @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # #Test set 3: Boiler cannot serve process heat but serves DHW, space heating - # d["Boiler"]["can_serve_dhw"] = true - # d["Boiler"]["can_serve_space_heating"] = true - # d["Boiler"]["can_serve_process_heat"] = false - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 16.0 atol=0.1 - # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + #Test set 3: Boiler cannot serve process heat but serves DHW, space heating + d["Boiler"]["can_serve_dhw"] = true + d["Boiler"]["can_serve_space_heating"] = true + d["Boiler"]["can_serve_process_heat"] = false + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 16.0 atol=0.1 + @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 140160.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 70080.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # #Test set 4: Fuel expensive, but ExistingBoiler is retired - # d["Boiler"]["can_serve_dhw"] = true - # d["Boiler"]["can_serve_space_heating"] = true - # d["Boiler"]["can_serve_process_heat"] = true - # d["Boiler"]["fuel_cost_per_mmbtu"] = 30.0 - # d["ExistingBoiler"]["retire_in_optimal"] = true - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 - # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + #Test set 4: Fuel expensive, but ExistingBoiler is retired + d["Boiler"]["can_serve_dhw"] = true + d["Boiler"]["can_serve_space_heating"] = true + d["Boiler"]["can_serve_process_heat"] = true + d["Boiler"]["fuel_cost_per_mmbtu"] = 30.0 + d["ExistingBoiler"]["retire_in_optimal"] = true + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 24.0 atol=0.1 + @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # #Test set 5: Fuel expensive, ExistingBoiler not retired - # d["ExistingBoiler"]["retire_in_optimal"] = false - # s = Scenario(d) - # p = REoptInputs(s) - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt(m, p) - # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 - # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 - # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + #Test set 5: Fuel expensive, ExistingBoiler not retired + d["ExistingBoiler"]["retire_in_optimal"] = false + s = Scenario(d) + p = REoptInputs(s) + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt(m, p) + @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 0.0 atol=0.1 + @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 0.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test sum(results["Boiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 0.0 atol=0.1 + @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 210240.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_dhw_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_space_heating_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 + @test sum(results["ExistingBoiler"]["thermal_to_process_heat_load_series_mmbtu_per_hour"]) ≈ 70080.0 atol=0.1 - # # Test 6: reduce emissions by half, get half the new boiler size - # d["Site"]["CO2_emissions_reduction_min_fraction"] = 0.50 - # s = Scenario(d) - # p = REoptInputs(s) - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # results = run_reopt([m1,m2], p) - # @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 12.0 atol=0.1 - # @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 - # @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 - # end - - # @testset "Custom REopt logger" begin - - # # Throw a handled error - # d = JSON.parsefile("./scenarios/logger.json") - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - # @test r["Messages"]["has_stacktrace"] == false - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # # Type is dict when errors, otherwise type REoptInputs - # @test isa(REoptInputs(d), Dict) - - # # Using filepath - # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([n1,n2], "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(n, "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging - # d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" - # d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" - - # m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([m1,m2], d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(m, d) - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # # Type is dict when errors, otherwise type REoptInputs - # @test isa(REoptInputs(d), Dict) - - # # Using filepath - # n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt([n1,n2], "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - - # n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) - # r = run_reopt(n, "./scenarios/logger.json") - # @test r["status"] == "error" - # @test "Messages" ∈ keys(r) - # @test "errors" ∈ keys(r["Messages"]) - # @test "warnings" ∈ keys(r["Messages"]) - # @test length(r["Messages"]["errors"]) > 0 - # @test length(r["Messages"]["warnings"]) > 0 - # end + # Test 6: reduce emissions by half, get half the new boiler size + d["Site"]["CO2_emissions_reduction_min_fraction"] = 0.50 + s = Scenario(d) + p = REoptInputs(s) + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + results = run_reopt([m1,m2], p) + @test results["Boiler"]["size_mmbtu_per_hour"] ≈ 12.0 atol=0.1 + @test results["Boiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 + @test results["ExistingBoiler"]["annual_thermal_production_mmbtu"] ≈ 105120.0 atol=0.1 + end + + @testset "Custom REopt logger" begin + + # Throw a handled error + d = JSON.parsefile("./scenarios/logger.json") + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + @test r["Messages"]["has_stacktrace"] == false + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + # Type is dict when errors, otherwise type REoptInputs + @test isa(REoptInputs(d), Dict) + + # Using filepath + n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([n1,n2], "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(n, "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + # Throw an unhandled error: Bad URDB rate -> stack gets returned for debugging + d["ElectricLoad"]["doe_reference_name"] = "MidriseApartment" + d["ElectricTariff"]["urdb_label"] = "62c70a6c40a0c425535d387x" + + m1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + m2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([m1,m2], d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + m = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(m, d) + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + # Type is dict when errors, otherwise type REoptInputs + @test isa(REoptInputs(d), Dict) + + # Using filepath + n1 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + n2 = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt([n1,n2], "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + + n = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) + r = run_reopt(n, "./scenarios/logger.json") + @test r["status"] == "error" + @test "Messages" ∈ keys(r) + @test "errors" ∈ keys(r["Messages"]) + @test "warnings" ∈ keys(r["Messages"]) + @test length(r["Messages"]["errors"]) > 0 + @test length(r["Messages"]["warnings"]) > 0 + end end end \ No newline at end of file From c2de7166953429612750cef35762932ad496306e Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 15:15:25 -0600 Subject: [PATCH 134/151] fix missing end from merge --- src/results/proforma.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/results/proforma.jl b/src/results/proforma.jl index 7135478a3..0b12452bd 100644 --- a/src/results/proforma.jl +++ b/src/results/proforma.jl @@ -101,6 +101,7 @@ function proforma_results(p::REoptInputs, d::Dict) depreciation_schedule = get_depreciation_schedule(p, storage, federal_itc_basis) m.total_depreciation += depreciation_schedule end + end end # calculate Generator o+m costs, incentives, and depreciation From b5840df800340d59dbe9d0c5d62b228d033607ec Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 15:15:41 -0600 Subject: [PATCH 135/151] rm allow_simultaneous_charge_discharge --- CHANGELOG.md | 1 - src/core/energy_storage/electric_storage.jl | 4 ---- src/mpc/structs.jl | 2 -- test/scenarios/multiple_elec_storage.json | 6 ++---- 4 files changed, 2 insertions(+), 11 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index d449b996d..9e5049403 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -29,7 +29,6 @@ Classify the change according to the following categories: - Added outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added new input **self_discharge_fraction_per_timestep** to `ElectricStorage` for modeling battery self-discharge - Added input option **can_export_to_grid** (defaults to _false_) to `ElectricStorage` and decision variable **dvStorageToGrid** -- Added input **allow_simultaneous_charge_discharge** (defaults to _true_) to `ElectricStorage` to give option to disallow battery from simultaneously charging and discharging, which adds binary decision variables often unnecessarily - Added input **fixed_duration** to `ElectricStorage`, which fixes the ratio between **size_kw** and **size_kwh** in the optimized solution if provided - Added input option **optimize_soc_init_fraction** (defaults to _false_) to `ElectricStorage`, which makes the optimization choose the inital SOC (equal to final SOC) instead of using **soc_init_fraction**. The initial SOC is also constrained to equal the final SOC, which eliminates the "free energy" issue. We currently do not fix SOC when **soc_init_fraction** is used because this has caused infeasibility. - Added output **initial_capital_cost** to all techs diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 900bea1e8..4615d574d 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -171,7 +171,6 @@ end self_discharge_fraction_per_timestep::Float64 = 0.0 # Battery self-discharge as a fraction per timestep loss based on kWh stored in each timestep fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false - allow_simultaneous_charge_discharge::Bool = true # Simultaneous charge/discharge is typically suboptimal anyway and allowing this avoids additional binary variables (which can slow solve time) name::String = "ElectricStorage" ``` """ @@ -212,7 +211,6 @@ Base.@kwdef struct ElectricStorageDefaults self_discharge_fraction_per_timestep::Float64 = 0.0 fixed_duration::Union{Real, Nothing} = nothing optimize_soc_init_fraction::Bool = false - allow_simultaneous_charge_discharge::Bool = true name::String = "ElectricStorage" end @@ -261,7 +259,6 @@ struct ElectricStorage <: AbstractElectricStorage self_discharge_fraction_per_timestep::Float64 fixed_duration::Union{Real, Nothing} optimize_soc_init_fraction::Bool - allow_simultaneous_charge_discharge::Bool name::String function ElectricStorage(d::Dict, f::Financial) @@ -357,7 +354,6 @@ struct ElectricStorage <: AbstractElectricStorage s.self_discharge_fraction_per_timestep, s.fixed_duration, s.optimize_soc_init_fraction, - s.allow_simultaneous_charge_discharge, s.name ) end diff --git a/src/mpc/structs.jl b/src/mpc/structs.jl index ddc8cd665..4e063eb50 100644 --- a/src/mpc/structs.jl +++ b/src/mpc/structs.jl @@ -228,7 +228,6 @@ Base.@kwdef struct MPCElectricStorage < AbstractElectricStorage can_grid_charge::Bool = true can_export_to_grid::Bool = false grid_charge_efficiency::Float64 = 0.96 * 0.975^2 - allow_simultaneous_charge_discharge::Bool = true end ``` """ @@ -246,7 +245,6 @@ Base.@kwdef struct MPCElectricStorage <: AbstractElectricStorage max_kwh::Float64 = size_kwh minimum_avg_soc_fraction::Float64 = 0.0 self_discharge_fraction_per_timestep::Float64 = 0.0 - allow_simultaneous_charge_discharge::Bool = true end diff --git a/test/scenarios/multiple_elec_storage.json b/test/scenarios/multiple_elec_storage.json index 99b31e0a8..e0704c4e1 100644 --- a/test/scenarios/multiple_elec_storage.json +++ b/test/scenarios/multiple_elec_storage.json @@ -39,8 +39,7 @@ "replace_cost_per_kwh": 230.0, "installed_cost_per_kw": 1000.0, "installed_cost_per_kwh": 500.0, - "total_itc_fraction": 0.0, - "allow_simultaneous_charge_discharge": false + "total_itc_fraction": 0.0 }, { "total_rebate_per_kw": 100.0, @@ -51,8 +50,7 @@ "replace_cost_per_kwh": 230.0, "installed_cost_per_kw": 1500.0, "installed_cost_per_kwh": 500.0, - "total_itc_fraction": 0.0, - "allow_simultaneous_charge_discharge": false + "total_itc_fraction": 0.0 } ], "ElectricTariff": { From dc3ba22cebd8b38e5fda99dc6f1bdf71e15795f0 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 25 Sep 2024 17:08:07 -0600 Subject: [PATCH 136/151] Revert "Update expected Solar dataset test from updated NSRDB" This reverts commit d25ba18536121eb6576be2ddbbf21093e5407fe4. --- test/runtests.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/runtests.jl b/test/runtests.jl index 32d7de768..8a740a162 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -92,7 +92,7 @@ else # run HiGHS tests latitude, longitude = 65.0102196310875, 25.465387094897675 radius = 0 dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - @test dataset ≈ "nsrdb" + @test dataset ≈ "intl" # 4. Fairbanks, AK site = "Fairbanks" From fef2dec29d482095969dcdd2e1d3e2ca0649d5f6 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 8 Oct 2024 13:37:47 -0600 Subject: [PATCH 137/151] rm debugging write to file --- test/runtests.jl | 3 --- 1 file changed, 3 deletions(-) diff --git a/test/runtests.jl b/test/runtests.jl index 8a740a162..317b7e87a 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -26,9 +26,6 @@ else # run HiGHS tests @testset "Multiple Electric Storage" begin model = Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "log_to_console" => false)) r = run_reopt(model, "./scenarios/multiple_elec_storage.json") - open("debug_results.json","w") do f - JSON.print(f, r, 4) - end @test r["PV"]["size_kw"] ≈ 216.6667 atol=0.01 @test r["Financial"]["lcc"] ≈ 1.2391786e7 rtol=1e-5 for stor in r["ElectricStorage"] From a4f5bf274e264750c0f3ba7a7e40bcf05daa0068 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 8 Oct 2024 13:46:43 -0600 Subject: [PATCH 138/151] Update CHANGELOG.md --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9e5049403..555e3b5a3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,6 +32,7 @@ Classify the change according to the following categories: - Added input **fixed_duration** to `ElectricStorage`, which fixes the ratio between **size_kw** and **size_kwh** in the optimized solution if provided - Added input option **optimize_soc_init_fraction** (defaults to _false_) to `ElectricStorage`, which makes the optimization choose the inital SOC (equal to final SOC) instead of using **soc_init_fraction**. The initial SOC is also constrained to equal the final SOC, which eliminates the "free energy" issue. We currently do not fix SOC when **soc_init_fraction** is used because this has caused infeasibility. - Added output **initial_capital_cost** to all techs +- Added ability to model multiple `ElectricStorage` at once. Inputs are provided as a _list_ of _dict_ under the `ElectricStorage` key and outputs are similarly formatted. This mirrors the existing multiple `PV` implementation. This functionality is not yet implemented for MPC scenarios. ### Fixed - Added missing outputs **lifecycle_export_benefit_before_tax** and **year_one_export_benefit_after_tax** to `ElectricTariff` - Add missing output **year_one_om_cost_before_tax** to `PV` From d1f84f906f58afa6a741984e99e581a536e59352 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 11 Oct 2024 12:35:21 -0600 Subject: [PATCH 139/151] trigger tests --- test/runtests.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/runtests.jl b/test/runtests.jl index 1e96a9bd4..5c886a1db 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -2907,4 +2907,4 @@ else # run HiGHS tests @test length(r["Messages"]["warnings"]) > 0 end end -end \ No newline at end of file +end \ No newline at end of file From 300755b32024b78cb57cfa78a19d548a5137583e Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Tue, 15 Oct 2024 13:34:56 -0600 Subject: [PATCH 140/151] make storage_to_grid_series_kw array not denseaxisarray --- src/results/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index 7548052c4..c4122a90f 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -52,7 +52,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid"*_n)][b, :]), digits = 3) + r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid"*_n)][b, ts] for ts in time_steps), digits = 3) else r["soc_series_fraction"] = [] From ab1d65b660aaba7f96ce844a103b1f85caf8c25e Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Wed, 16 Oct 2024 16:35:12 -0600 Subject: [PATCH 141/151] fix typo --- src/results/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/results/electric_storage.jl b/src/results/electric_storage.jl index c4122a90f..4f586a247 100644 --- a/src/results/electric_storage.jl +++ b/src/results/electric_storage.jl @@ -52,7 +52,7 @@ function add_electric_storage_results(m::JuMP.AbstractModel, p::REoptInputs, d:: end end # report the exported electricity from the battery: - r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid"*_n)][b, ts] for ts in time_steps), digits = 3) + r["storage_to_grid_series_kw"] = round.(value.(m[Symbol("dvStorageToGrid"*_n)][b, ts] for ts in p.time_steps), digits = 3) else r["soc_series_fraction"] = [] From 1ff7a31a86ae0888889f0fc9420498931ba2d995 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Mon, 21 Oct 2024 12:12:43 -0600 Subject: [PATCH 142/151] Update thermal decay variables to match electric storage self-discharge --- src/constraints/storage_constraints.jl | 12 ++++++---- src/core/energy_storage/thermal_storage.jl | 24 ++++++++++++------- test/scenarios/ghp_inputs.json | 4 ++-- .../heat_cool_energy_balance_inputs.json | 4 ++-- test/scenarios/re_emissions_with_thermal.json | 2 +- 5 files changed, 28 insertions(+), 18 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 1a38ab58e..6af7232ea 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -111,11 +111,12 @@ function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, [b in p.s.storage.types.hot, ts in p.time_steps], m[Symbol("dvStoredEnergy"*_n)][b,ts] == m[Symbol("dvStoredEnergy"*_n)][b,ts-1] + (1/p.s.settings.time_steps_per_hour) * ( p.s.storage.attr[b].charge_efficiency * sum(m[Symbol("dvHeatToStorage"*_n)][b,t,q,ts] for t in union(p.techs.heating, p.techs.chp), q in p.heating_loads) - - sum(m[Symbol("dvHeatFromStorage"*_n)][b,q,ts] for q in p.heating_loads) / p.s.storage.attr[b].discharge_efficiency - - p.s.storage.attr[b].thermal_decay_rate_fraction * m[Symbol("dvStorageEnergy"*_n)][b] + sum(m[Symbol("dvHeatFromStorage"*_n)][b,q,ts] for q in p.heating_loads) / p.s.storage.attr[b].discharge_efficiency ) + - (p.s.storage.attr[b].soc_based_per_ts_thermal_decay_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].capacity_based_per_ts_thermal_decay_fraction * m[Symbol("dvStorageEnergy"*_n)][b]) ) - + #Constraint (4n)-1: Dispatch to and from thermal storage is no greater than power capacity @constraint(m, [b in p.s.storage.types.hot, ts in p.time_steps], m[Symbol("dvStoragePower"*_n)][b] >= @@ -154,9 +155,10 @@ function add_cold_thermal_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, ColdTESInventoryCon[b in p.s.storage.types.cold, ts in p.time_steps], m[Symbol("dvStoredEnergy"*_n)][b,ts] == m[Symbol("dvStoredEnergy"*_n)][b,ts-1] + (1/p.s.settings.time_steps_per_hour) * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.cooling) - - m[Symbol("dvDischargeFromStorage"*_n)][b,ts]/p.s.storage.attr[b].discharge_efficiency - - p.s.storage.attr[b].thermal_decay_rate_fraction * m[Symbol("dvStorageEnergy"*_n)][b] + m[Symbol("dvDischargeFromStorage"*_n)][b,ts]/p.s.storage.attr[b].discharge_efficiency ) + - (p.s.storage.attr[b].soc_based_per_ts_thermal_decay_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].capacity_based_per_ts_thermal_decay_fraction * m[Symbol("dvStorageEnergy"*_n)][b]) ) #Constraint (4n)-2: Dispatch to and from thermal storage is no greater than power capacity diff --git a/src/core/energy_storage/thermal_storage.jl b/src/core/energy_storage/thermal_storage.jl index 41ffb6aff..a0703578f 100644 --- a/src/core/energy_storage/thermal_storage.jl +++ b/src/core/energy_storage/thermal_storage.jl @@ -15,7 +15,8 @@ Cold thermal energy storage sytem; specifically, a chilled water system used to soc_min_fraction::Float64 = 0.1 # Minimum allowable TES thermal state of charge soc_init_fraction::Float64 = 0.5 # TES thermal state of charge at first hour of optimization installed_cost_per_gal::Float64 = 1.50 # Thermal energy-based cost of TES (e.g. volume of the tank) - thermal_decay_rate_fraction::Float64 = 0.0004 # Thermal loss (gain) rate as a fraction of energy storage capacity, per hour (frac*energy_capacity/hr = kw_thermal) + soc_based_per_ts_thermal_decay_fraction = 0.0 # Thermal loss (gain) rate as a fraction of energy storage capacity per timestep (frac*energy_capacity = kw_thermal); the provided default is for an hourly thermal loss/gain + capacity_based_per_ts_thermal_decay_fraction = 0.0004 # Thermal loss (gain) rate as a fraction of the energy storaged in each timestep (frac*energy_stored = kw_thermal) om_cost_per_gal::Float64 = 0.0 # Yearly fixed O&M cost dependent on storage energy size macrs_option_years::Int = 7 macrs_bonus_fraction::Float64 = 0.6 @@ -33,7 +34,8 @@ Base.@kwdef struct ColdThermalStorageDefaults <: AbstractThermalStorageDefaults soc_min_fraction::Float64 = 0.1 soc_init_fraction::Float64 = 0.5 installed_cost_per_gal::Float64 = 1.50 - thermal_decay_rate_fraction::Float64 = 0.0004 + soc_based_per_ts_thermal_decay_fraction::Float64 = 0.0 + capacity_based_per_ts_thermal_decay_fraction::Float64 = 0.0004 om_cost_per_gal::Float64 = 0.0 macrs_option_years::Int = 7 macrs_bonus_fraction::Float64 = 0.6 @@ -55,7 +57,8 @@ end soc_min_fraction::Float64 = 0.1 soc_init_fraction::Float64 = 0.5 installed_cost_per_gal::Float64 = 1.50 - thermal_decay_rate_fraction::Float64 = 0.0004 + soc_based_per_ts_thermal_decay_fraction = 0.0 + capacity_based_per_ts_thermal_decay_fraction = 0.0004 om_cost_per_gal::Float64 = 0.0 macrs_option_years::Int = 7 macrs_bonus_fraction::Float64 = 0.6 @@ -76,7 +79,8 @@ Base.@kwdef struct HotThermalStorageDefaults <: AbstractThermalStorageDefaults soc_min_fraction::Float64 = 0.1 soc_init_fraction::Float64 = 0.5 installed_cost_per_gal::Float64 = 1.50 - thermal_decay_rate_fraction::Float64 = 0.0004 + soc_based_per_ts_thermal_decay_fraction::Float64 = 0.0 + capacity_based_per_ts_thermal_decay_fraction::Float64 = 0.0004 om_cost_per_gal::Float64 = 0.0 macrs_option_years::Int = 7 macrs_bonus_fraction::Float64 = 0.6 @@ -105,7 +109,8 @@ struct ColdThermalStorage <: AbstractThermalStorage soc_min_fraction::Float64 soc_init_fraction::Float64 installed_cost_per_gal::Float64 - thermal_decay_rate_fraction::Float64 + soc_based_per_ts_thermal_decay_fraction::Float64 + capacity_based_per_ts_thermal_decay_fraction::Float64 om_cost_per_gal::Float64 macrs_option_years::Int macrs_bonus_fraction::Float64 @@ -154,7 +159,8 @@ struct ColdThermalStorage <: AbstractThermalStorage s.soc_min_fraction, s.soc_init_fraction, s.installed_cost_per_gal, - s.thermal_decay_rate_fraction, + s.soc_based_per_ts_thermal_decay_fraction, + s.capacity_based_per_ts_thermal_decay_fraction, s.om_cost_per_gal, s.macrs_option_years, s.macrs_bonus_fraction, @@ -188,7 +194,8 @@ struct HotThermalStorage <: AbstractThermalStorage soc_min_fraction::Float64 soc_init_fraction::Float64 installed_cost_per_gal::Float64 - thermal_decay_rate_fraction::Float64 + soc_based_per_ts_thermal_decay_fraction::Float64 + capacity_based_per_ts_thermal_decay_fraction::Float64 om_cost_per_gal::Float64 macrs_option_years::Int macrs_bonus_fraction::Float64 @@ -240,7 +247,8 @@ struct HotThermalStorage <: AbstractThermalStorage s.soc_min_fraction, s.soc_init_fraction, s.installed_cost_per_gal, - s.thermal_decay_rate_fraction, + s.soc_based_per_ts_thermal_decay_fraction, + s.capacity_based_per_ts_thermal_decay_fraction, s.om_cost_per_gal, s.macrs_option_years, s.macrs_bonus_fraction, diff --git a/test/scenarios/ghp_inputs.json b/test/scenarios/ghp_inputs.json index 43b816527..abd01faad 100644 --- a/test/scenarios/ghp_inputs.json +++ b/test/scenarios/ghp_inputs.json @@ -59,11 +59,11 @@ "ColdThermalStorage": { "min_gal": 10, "max_gal": 10, - "thermal_decay_rate_fraction": 0.0 + "capacity_based_per_ts_thermal_decay_fraction": 0.0 }, "HotThermalStorage": { "min_gal": 10, "max_gal": 10, - "thermal_decay_rate_fraction": 0.0 + "capacity_based_per_ts_thermal_decay_fraction": 0.0 } } \ No newline at end of file diff --git a/test/scenarios/heat_cool_energy_balance_inputs.json b/test/scenarios/heat_cool_energy_balance_inputs.json index 6d8af917e..30aecc394 100644 --- a/test/scenarios/heat_cool_energy_balance_inputs.json +++ b/test/scenarios/heat_cool_energy_balance_inputs.json @@ -74,13 +74,13 @@ "min_gal": 20000.0, "max_gal": 20000.0, "internal_efficiency_fraction": 0.97, - "thermal_decay_rate_fraction": 0.004 + "capacity_based_per_ts_thermal_decay_fraction": 0.004 }, "ColdThermalStorage":{ "min_gal": 30000.0, "max_gal": 30000.0, "internal_efficiency_fraction": 0.97, - "thermal_decay_rate_fraction": 0.004, + "capacity_based_per_ts_thermal_decay_fraction": 0.004, "soc_init_fraction": 0.1 }, "AbsorptionChiller": { diff --git a/test/scenarios/re_emissions_with_thermal.json b/test/scenarios/re_emissions_with_thermal.json index fbc0346f1..747f1792e 100644 --- a/test/scenarios/re_emissions_with_thermal.json +++ b/test/scenarios/re_emissions_with_thermal.json @@ -79,7 +79,7 @@ "soc_min_fraction": 0.1 , "soc_init_fraction": 0.5 , "installed_cost_per_gal": 3.0 , - "thermal_decay_rate_fraction": 0.004 , + "capacity_based_per_ts_thermal_decay_fraction": 0.004 , "om_cost_per_gal": 0.0 , "macrs_option_years": 0 , "macrs_bonus_fraction": 0.0 From 78793c0493d8b5daac38ed8660fe33e91f7a5110 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Mon, 21 Oct 2024 13:34:40 -0600 Subject: [PATCH 143/151] Remove extra minus sign --- src/constraints/storage_constraints.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 9df637469..a34c287ff 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -178,7 +178,7 @@ function add_cold_thermal_storage_dispatch_constraints(m, p, b; _n="") @constraint(m, ColdTESInventoryCon[ts in p.time_steps], m[Symbol("dvStoredEnergy"*_n)][b,ts] == m[Symbol("dvStoredEnergy"*_n)][b,ts-1] + p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b,t,ts] for t in p.techs.cooling) - - m[Symbol("dvDischargeFromStorage"*_n)][b,ts]/p.s.storage.attr[b].discharge_efficiency - + m[Symbol("dvDischargeFromStorage"*_n)][b,ts]/p.s.storage.attr[b].discharge_efficiency ) - (p.s.storage.attr[b].soc_based_per_ts_thermal_decay_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) - (p.s.storage.attr[b].capacity_based_per_ts_thermal_decay_fraction * m[Symbol("dvStorageEnergy"*_n)][b]) From b80f4652f2896d28adba6e439f9d896fc1a1a158 Mon Sep 17 00:00:00 2001 From: lixiangk1 <72464565+lixiangk1@users.noreply.github.com> Date: Tue, 22 Oct 2024 12:37:15 -0600 Subject: [PATCH 144/151] Fix typo --- src/core/energy_storage/electric_storage.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/core/energy_storage/electric_storage.jl b/src/core/energy_storage/electric_storage.jl index 29a49d67d..f7a80df33 100644 --- a/src/core/energy_storage/electric_storage.jl +++ b/src/core/energy_storage/electric_storage.jl @@ -374,7 +374,7 @@ struct ElectricStorage <: AbstractElectricStorage degr, s.minimum_avg_soc_fraction, s.capacity_based_per_ts_self_discharge_fraction, - s.soc_based_per_ts_self_discharge_fraction. + s.soc_based_per_ts_self_discharge_fraction, s.fixed_duration, s.optimize_soc_init_fraction, s.name From d4979c25f0d2893628510c67db44f5f4ac6bd1c8 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 24 Oct 2024 17:44:45 -0600 Subject: [PATCH 145/151] fix degradation merge --- src/core/reopt.jl | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/src/core/reopt.jl b/src/core/reopt.jl index 4e8fc58b2..ca9e50317 100644 --- a/src/core/reopt.jl +++ b/src/core/reopt.jl @@ -486,7 +486,7 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) for b in p.s.storage.types.elec if p.s.storage.attr[b].model_degradation - add_degradation(m, p; b=b) + add_degradation(m, p, b) if p.s.settings.add_soc_incentive # this warning should be tied to IF condition where SOC incentive is added @warn "Settings.add_soc_incentive is set to true and it will incentivize BESS energy levels to be kept high. It could conflict with the battery degradation model and should be disabled." end @@ -566,15 +566,6 @@ function build_reopt!(m::JuMP.AbstractModel, p::REoptInputs) # Set model objective @objective(m, Min, m[:Costs] + m[:ObjectivePenalties] ) - - for b in p.s.storage.types.elec - if p.s.storage.attr[b].model_degradation - add_degradation(m, p, b) - if p.s.settings.add_soc_incentive - @warn "Settings.add_soc_incentive is set to true but no incentive will be added because it conflicts with the battery degradation model." - end - end - end nothing end From a10fb6bc83f20c5badbd6c8610f3169fe9c1cbd6 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Thu, 24 Oct 2024 18:07:55 -0600 Subject: [PATCH 146/151] add self discharge terms to elec no simultaneous charge/discharge constraint --- src/constraints/storage_constraints.jl | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 65ebb97a4..e811025af 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -86,10 +86,13 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") # Constraint (4h): prevent simultaneous charge and discharge by limitting charging alone to not make the SOC exceed 100% # (4h)-1: with grid @constraint(m, [ts in p.time_steps_with_grid], - m[Symbol("dvStorageEnergy"*_n)][b] >= m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + p.hours_per_time_step * ( + m[Symbol("dvStorageEnergy"*_n)][b] >= m[Symbol("dvStoredEnergy"*_n)][b, ts-1] + + p.hours_per_time_step * ( sum(p.s.storage.attr[b].charge_efficiency * m[Symbol("dvProductionToStorage"*_n)][b, t, ts] for t in p.techs.elec) + p.s.storage.attr[b].grid_charge_efficiency * m[Symbol("dvGridToStorage"*_n)][b, ts] ) + - (p.s.storage.attr[b].soc_based_per_ts_self_discharge_fraction * m[Symbol("dvStoredEnergy"*_n)][b, ts]) + - (p.s.storage.attr[b].capacity_based_per_ts_self_discharge_fraction * m[Symbol("dvStorageEnergy"*_n)][b]) ) # (4h)-2: no grid @constraint(m, [ts in p.time_steps_without_grid], From db87859393ab976dac37418dd865893f6df75496 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 25 Oct 2024 13:25:09 -0600 Subject: [PATCH 147/151] fix changelog merge --- CHANGELOG.md | 1 - 1 file changed, 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 18fb31a78..476559cfa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -42,7 +42,6 @@ Classify the change according to the following categories: - Added missing outputs **lifecycle_export_benefit_before_tax** and **year_one_export_benefit_after_tax** to `ElectricTariff` - Add missing output **year_one_om_cost_before_tax** to `PV` -## v0.48.2 ## Develop degradation-cleanup ### Added - Battery residual value if choosing replacement strategy for degradation From 59a458676a83b5d300d736e007b13b2b41640da7 Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 25 Oct 2024 13:26:34 -0600 Subject: [PATCH 148/151] fix changelog merge --- CHANGELOG.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7acd324d0..e3a56e457 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -25,7 +25,7 @@ Classify the change according to the following categories: ### Deprecated ### Removed -## Develop 2024-09-07 +## Develop battery_om ### Added - Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M - Added new input **soc_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling SOC-based battery self-discharge @@ -33,7 +33,6 @@ Classify the change according to the following categories: - Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` - Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` -## v0.47.3 ## Develop degradation-cleanup ### Added - Battery residual value if choosing replacement strategy for degradation From 6aa56d4cd85d2896a63c3ef80d5bf3e0c910ae6c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 25 Oct 2024 13:31:16 -0600 Subject: [PATCH 149/151] align changelog with multiple-elec-stor --- CHANGELOG.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e3a56e457..24ada26b3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -27,11 +27,10 @@ Classify the change according to the following categories: ## Develop battery_om ### Added -- Added new inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M -- Added new input **soc_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling SOC-based battery self-discharge -- Added new input **capacity_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling capacity-based battery self-discharge -- Added new outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` -- Added testsets **Electric Storage O&M** and **Electric Storage Self-Discharge** in `test/runtests.jl` +- Added inputs **om_cost_per_kw** and **om_cost_per_kwh** to `ElectricStorage` for modeling capacity-based O&M +- Added outputs **lifecycle_om_cost_after_tax** and **year_one_om_cost_before_tax** to `ElectricStorage` +- Added input **soc_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling SOC-based battery self-discharge +- Added input **capacity_based_per_ts_self_discharge_fraction** to `ElectricStorage` for modeling capacity-based battery self-discharge ## Develop degradation-cleanup ### Added From c847aa075a0bcbc758e7d20dee31b8a8a176cdfd Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 25 Oct 2024 13:51:07 -0600 Subject: [PATCH 150/151] rm fixed_duration constraint --- src/constraints/storage_constraints.jl | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/src/constraints/storage_constraints.jl b/src/constraints/storage_constraints.jl index 88d5be970..9349217ef 100644 --- a/src/constraints/storage_constraints.jl +++ b/src/constraints/storage_constraints.jl @@ -27,13 +27,10 @@ function add_storage_size_constraints(m, p, b; _n="") @constraint(m, m[Symbol("dvStorageEnergy"*_n)][b] <= m[Symbol("dvStoragePower"*_n)][b] * p.s.storage.attr[b].max_duration_hours ) - @constraint(m, m[Symbol("dvStorageEnergy"*_n)][b] >= m[Symbol("dvStoragePower"*_n)][b] * p.s.storage.attr[b].min_duration_hours ) - end - - + end end @@ -158,10 +155,6 @@ function add_elec_storage_dispatch_constraints(m, p, b; _n="") sum(m[Symbol("dvStorageEnergy"*_n)][b]) ) end - - if p.s.storage.attr[b] isa ElectricStorage && !isnothing(p.s.storage.attr[b].fixed_duration) - @constraint(m, m[Symbol("dvStoragePower"*_n)][b] == m[Symbol("dvStorageEnergy"*_n)][b] / p.s.storage.attr[b].fixed_duration) - end end function add_hot_thermal_storage_dispatch_constraints(m, p, b; _n="") From dc729d8ff0be6abef3abd7657866a3675f801c5c Mon Sep 17 00:00:00 2001 From: Hallie Dunham <70401017+hdunham@users.noreply.github.com> Date: Fri, 25 Oct 2024 16:18:57 -0600 Subject: [PATCH 151/151] fix conversion error when multiple pvs or elec stors and only their Real inputs provided, was trying to push name string to a Dict{String,Any} --- src/core/scenario.jl | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/core/scenario.jl b/src/core/scenario.jl index f453b3c0a..5fd8df026 100644 --- a/src/core/scenario.jl +++ b/src/core/scenario.jl @@ -93,10 +93,11 @@ function Scenario(d::Dict; flex_hvac_from_json=false) if haskey(d, "PV") if typeof(d["PV"]) <: AbstractArray for (i, pv) in enumerate(d["PV"]) - if !(haskey(pv, "name")) - pv["name"] = string("PV", i) + pv = dictkeys_tosymbols(pv) + if !(haskey(pv, :name)) + pv[:name] = string("PV", i) end - push!(pvs, PV(;dictkeys_tosymbols(pv)..., off_grid_flag = settings.off_grid_flag, + push!(pvs, PV(;pv..., off_grid_flag = settings.off_grid_flag, latitude=site.latitude)) end elseif typeof(d["PV"]) <: AbstractDict @@ -170,11 +171,11 @@ function Scenario(d::Dict; flex_hvac_from_json=false) if haskey(d, "ElectricStorage") if typeof(d["ElectricStorage"]) <: AbstractArray for (i, elecstor) in enumerate(d["ElectricStorage"]) - if !(haskey(elecstor, "name")) - elecstor["name"] = string("ElectricStorage", i) - end storage_dict = dictkeys_tosymbols(elecstor) storage_dict[:off_grid_flag] = settings.off_grid_flag + if !(haskey(storage_dict, :name)) + storage_dict[:name] = string("ElectricStorage", i) + end storage_structs[storage_dict[:name]] = ElectricStorage(storage_dict, financial) end elseif typeof(d["ElectricStorage"]) <: AbstractDict