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Upgrade and adaptation of prepare_sector_network
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@Eddy-JV
Eddy-JV committed Aug 9, 2023
1 parent d150b83 commit 0635c06
Showing 1 changed file with 137 additions and 28 deletions.
165 changes: 137 additions & 28 deletions scripts/prepare_sector_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -520,7 +520,7 @@ def add_biomass(n, costs):
nodes + " biomass EOP",
bus0=spatial.biomass.nodes,
bus1=nodes,
bus2="co2 atmosphere",
#bus2="co2 atmosphere",
marginal_cost=1, #costs.at[biomass_gen, "efficiency"]
#* costs.at[biomass_gen, "VOM"], # NB: VOM is per MWel
# NB: fixed cost is per MWel
Expand All @@ -529,7 +529,7 @@ def add_biomass(n, costs):
p_nom_extendable=True,
carrier=biomass_gen,
efficiency=costs.at[biomass_gen, "efficiency"],
efficiency2=costs.at["solid biomass", "CO2 intensity"],
#efficiency2=costs.at["solid biomass", "CO2 intensity"],
lifetime=costs.at[biomass_gen, "lifetime"],
)
n.madd(
Expand Down Expand Up @@ -1236,10 +1236,10 @@ def add_industry(n, costs):
# check land tranport

co2 = (
n.loads.loc[nodes + co2_release, "p_set"].sum().sum()
n.loads.loc[nodes + co2_release, "p_set"].sum()
* costs.at["oil", "CO2 intensity"]
# - industrial_demand["process emission from feedstock"].sum()
/ 8760
#/ 8760
)

n.add(
Expand All @@ -1250,6 +1250,22 @@ def add_industry(n, costs):
p_set=-co2,
)


co2 = (
industrial_demand["coal"].sum()
* costs.at["coal", "CO2 intensity"]
# - industrial_demand["process emission from feedstock"].sum()
/ 8760
)

n.add(
"Load",
"industry coal emissions",
bus="co2 atmosphere",
carrier="industry coal emissions",
p_set=-co2,
)

########################################################### CARIER = HEAT
# TODO simplify bus expression
n.madd(
Expand Down Expand Up @@ -1520,8 +1536,8 @@ def add_land_transport(n, costs):
* transport[nodes].sum().sum()
/ 8760
* costs.at["oil", "CO2 intensity"]
* (1 - bio_share)
/ ice_share
#* (1 - bio_share)
#/ ice_share
)

n.add(
Expand Down Expand Up @@ -1949,6 +1965,7 @@ def add_services(n, costs):
carrier="services biomass",
p_set=p_set_biomass,
)

# co2 = (
# p_set_biomass.sum().sum() * costs.at["solid biomass", "CO2 intensity"]
# ) / 8760
Expand All @@ -1961,6 +1978,60 @@ def add_services(n, costs):
# p_set=-co2,
# )

p_set_oil = (
profile_residential
* energy_totals.loc[countries, "services oil"].sum()
* 1e6
)

n.madd(
"Load",
nodes,
suffix=" services oil",
bus=spatial.oil.nodes,
carrier="services oil",
p_set=p_set_oil,
)

co2 = (
p_set_oil.sum().sum() * costs.at["oil", "CO2 intensity"]
) / 8760

n.add(
"Load",
"services oil emissions",
bus="co2 atmosphere",
carrier="oil emissions",
p_set=-co2,
)

p_set_gas = (
profile_residential
* energy_totals.loc[countries, "services gas"].sum()
* 1e6
)

n.madd(
"Load",
nodes,
suffix=" services gas",
bus=spatial.gas.nodes,
carrier="services gas",
p_set=p_set_gas,
)

co2 = (
p_set_gas.sum().sum() * costs.at["gas", "CO2 intensity"]
) / 8760

n.add(
"Load",
"services gas emissions",
bus="co2 atmosphere",
carrier="gas emissions",
p_set=-co2,
)


def add_agriculture(n, costs):
n.madd(
Expand Down Expand Up @@ -2024,6 +2095,13 @@ def add_residential(n, costs):
.dropna(axis=1)
.columns
)
gas_ind = (
n.loads_t.p_set.filter(like=countries[0])
.filter(like="residential")
.filter(like="gas")
.dropna(axis=1)
.columns
)

#Correction using August profile in July scaled by 1.05
heat_demand.loc['2013-07-01':'2013-07-31', 'residential space'] = heat_demand.loc['2013-08-01':'2013-08-31', 'residential space'].iloc[::-1].values * 1.05
Expand All @@ -2046,6 +2124,11 @@ def add_residential(n, costs):
* energy_totals.loc[countries[0], "residential heat biomass"]
* 1e6
)
heat_gas_demand = (
heat_shape
* energy_totals.loc[countries[0], "residential heat gas"]
* 1e6
)

n.loads_t.p_set.loc[:, heat_ind] = (
heat_shape
Expand All @@ -2054,6 +2137,7 @@ def add_residential(n, costs):
+ energy_totals.loc[countries, "total residential water"].sum()
- energy_totals.loc[countries[0], "residential heat biomass"]
- energy_totals.loc[countries[0], "residential heat oil"]
- energy_totals.loc[countries[0], "residential heat gas"]
)
* 1e6
)
Expand All @@ -2075,6 +2159,12 @@ def add_residential(n, costs):
* 1e6
) + heat_biomass_demand.values

p_set_gas = (
profile_residential
* energy_totals.loc[countries, "residential gas"].sum()
* 1e6
) + heat_gas_demand.values

n.madd(
"Load",
nodes,
Expand All @@ -2100,6 +2190,23 @@ def add_residential(n, costs):
carrier="residential biomass",
p_set=p_set_biomass,
)
n.madd(
"Load",
nodes,
suffix=" residential gas",
bus=spatial.gas.nodes,
carrier="residential gas",
p_set=p_set_gas,
)
co2 = (p_set_gas.sum().sum() * costs.at["gas", "CO2 intensity"]) / 8760

n.add(
"Load",
"residential gas emissions",
bus="co2 atmosphere",
carrier="gas emissions",
p_set=-co2,
)

# co2 = (
# p_set_biomass.sum().sum() * costs.at["solid biomass", "CO2 intensity"]
Expand All @@ -2119,6 +2226,7 @@ def add_residential(n, costs):
+ energy_totals.loc[country, "total residential water"]
- energy_totals.loc[country, "residential heat biomass"]
- energy_totals.loc[country, "residential heat oil"]
- energy_totals.loc[country, "residential heat gas"]
)

heat_buses = (n.loads_t.p_set.filter(regex="heat")).columns
Expand Down Expand Up @@ -2172,23 +2280,23 @@ def add_residential(n, costs):
# )


# def add_custom_water_cost(n):
# for country in countries:
# water_costs = pd.read_csv(
# "resources/custom_data/{}_water_costs.csv".format(country),
# sep=",",
# index_col=0,
# )
# water_costs = water_costs.filter(like=country, axis=0).loc[nodes]
# electrolysis_links = n.links.filter(like=country, axis=0).filter(
# like="lectrolysis", axis=0
# )

# elec_index = n.links[
# (n.links.carrier == "H2 Electrolysis")
# & (n.links.bus0.str.contains(country))
# ].index
# n.links.loc[elec_index, "marginal_cost"] = water_costs.values
def add_custom_water_cost(n):
for country in countries:
water_costs = pd.read_csv(
"resources/custom_data/{}_water_costs.csv".format(country),
sep=",",
index_col=0,
)
water_costs = water_costs.filter(like=country, axis=0).loc[nodes]
electrolysis_links = n.links.filter(like=country, axis=0).filter(
like="lectrolysis", axis=0
)

elec_index = n.links[
(n.links.carrier == "H2 Electrolysis")
& (n.links.bus0.str.contains(country))
].index
n.links.loc[elec_index, "marginal_cost"] = water_costs.values
# n.links.filter(like=country, axis=0).filter(like='lectrolysis', axis=0)["marginal_cost"] = water_costs.values
# n.links.filter(like=country, axis=0).filter(like='lectrolysis', axis=0).apply(lambda x: water_costs[x.index], axis=0)
# print(n.links.filter(like=country, axis=0).filter(like='lectrolysis', axis=0).marginal_cost)
Expand Down Expand Up @@ -2225,13 +2333,13 @@ def add_rail_transport(n, costs):
snakemake = mock_snakemake(
"prepare_sector_network",
simpl="",
clusters="255",
clusters="184",
ll="c1.0",
opts="Co2L",
planning_horizons="2030",
sopts="3H",
discountrate="0.175",
demand="BS",
discountrate="0.098",
demand="NZ",
)

# Load population layout
Expand Down Expand Up @@ -2408,8 +2516,9 @@ def add_rail_transport(n, costs):
if options["dac"]:
add_dac(n, costs)

#if snakemake.config["custom_data"]["water_costs"]:
#add_custom_water_cost(n)
if snakemake.config["custom_data"]["water_costs"]:
add_custom_water_cost(n)


n.export_to_netcdf(snakemake.output[0])

Expand Down

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