diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bad_ruc.json b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bad_ruc.json
new file mode 100644
index 000000000..d80a8b2de
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bad_ruc.json
@@ -0,0 +1 @@
+{"elements": {"bus": {"Abel": {"id": "101", "base_kv": 138.0, "matpower_bustype": "PV", "vm": 1.04777, "va": -7.74152, "v_min": 0.95, "v_max": 1.05, "area": "1", "zone": "11.0"}, "Adams": {"id": "102", "base_kv": 138.0, "matpower_bustype": "PV", "vm": 1.04783, "va": -7.81784, "v_min": 0.95, "v_max": 1.05, "area": "1", "zone": "12.0"}, "Adler": {"id": "103", "base_kv": 138.0, "matpower_bustype": "PQ", "vm": 1.01085, "va": -7.2109, "v_min": 0.95, "v_max": 1.05, "area": "1", "zone": "11.0"}, "Agricola": {"id": "104", "base_kv": 138.0, "matpower_bustype": "PQ", "vm": 1.01765, "va": -10.56614, "v_min": 0.95, "v_max": 1.05, "area": "1", "zone": "11.0"}, "Aiken": {"id": "105", "base_kv": 138.0, "matpower_bustype": "PQ", "vm": 1.03568, "va": -10.70887, "v_min": 0.95, "v_max": 1.05, "area": "1", "zone": "11.0"}, "Alber": {"id": "106", "base_kv": 138.0, "matpower_bustype": "PQ", "vm": 1.03242, "va": -13.27944, "v_min": 0.95, "v_max": 1.05, "area": "1", "zone": "12.0"}, "Alder": {"id": "107", 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"2020-07-10 19:00", "2020-07-10 20:00", "2020-07-10 21:00", "2020-07-10 22:00", "2020-07-10 23:00", "2020-07-11 00:00", "2020-07-11 01:00", "2020-07-11 02:00", "2020-07-11 03:00", "2020-07-11 04:00", "2020-07-11 05:00", "2020-07-11 06:00", "2020-07-11 07:00", "2020-07-11 08:00", "2020-07-11 09:00", "2020-07-11 10:00", "2020-07-11 11:00", "2020-07-11 12:00", "2020-07-11 13:00", "2020-07-11 14:00", "2020-07-11 15:00", "2020-07-11 16:00", "2020-07-11 17:00", "2020-07-11 18:00", "2020-07-11 19:00", "2020-07-11 20:00", "2020-07-11 21:00", "2020-07-11 22:00", "2020-07-11 23:00"], "flexible_ramp_down_requirement": {"data_type": "time_series", "values": [93, 93, 93, 91, 90, 94, 87, 71, 54, 37, 36, 17, 17, 17, 17, 39, 7, 7, 5, 5, 5, 5, 5, 5, 93, 93, 93, 91, 90, 94, 87, 71, 54, 37, 36, 17, 17, 17, 17, 39, 7, 7, 5, 5, 5, 5, 5, 5]}, "flexible_ramp_up_requirement": {"data_type": "time_series", "values": [91, 94, 93, 93, 93, 96, 88, 62, 40, 16, 16, 33, 33, 33, 33, 60, 36, 36, 3, 3, 3, 3, 3, 3, 91, 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\ No newline at end of file
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/bus_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/bus_detail.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/contingency_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/contingency_detail.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/daily_summary.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/daily_summary.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/hourly_gen_summary.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/hourly_gen_summary.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/hourly_summary.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/hourly_summary.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/line_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/line_detail.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/renewables_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/renewables_detail.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/runtimes.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/runtimes.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/thermal_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/thermal_detail.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/virtual_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_multiperiod_usc/virtual_detail.csv
new file mode 100644
index 000000000..e69de29bb
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bidder_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bidder_detail.csv
new file mode 100644
index 000000000..dedb2f50a
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bidder_detail.csv
@@ -0,0 +1,97 @@
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bidding_model_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bidding_model_detail.csv
new file mode 100644
index 000000000..e0ee68ec7
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bidding_model_detail.csv
@@ -0,0 +1,97 @@
+Date,Hour,Horizon [hr],Thermal Power Generated [MW],Total Cost [$],Hot Tank Level [MT],Plant Heat Duty [MWth],Storage Power [MW],Plant Power [MW],HXC Duty,HXD Duty,HXC Salt Flow,HXD Salt Flow,HXC Salt T in,HXC Salt T out,HXD Salt T in,HXD Salt T out,HXD Steam T out,HXD Steam out Vfrac,Scenario
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bus_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bus_detail.csv
new file mode 100644
index 000000000..b4fec6509
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/bus_detail.csv
@@ -0,0 +1,3505 @@
+Date,Hour,Minute,Bus,Demand,Shortfall,Overgeneration,LMP,LMP DA
+2020-07-10,0,0,Abel,53.084538,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Adams,47.677779,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Adler,88.474229,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Agricola,36.372739,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Aiken,34.898168,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Alber,66.847195,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Alder,61.440437,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Alger,84.050518,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Ali,86.016612,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Allen,95.847082,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Anna,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Archer,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Arne,130.253726,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Arnold,95.355558,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Arthur,155.812948,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Asser,49.15235,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Aston,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Astor,163.677324,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Attar,88.965753,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Attila,62.915007,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Attlee,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Aubrey,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Austen,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Avery,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bach,57.554753,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bacon,51.692694,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Baffin,95.924588,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bailey,39.435664,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bain,37.836921,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bajer,72.476355,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Baker,66.614297,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Balch,91.128358,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Balzac,93.260016,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Banks,103.918303,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bardeen,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Barkla,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Barlow,141.222309,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Barry,103.385389,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Barton,168.933857,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Basov,53.291438,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bates,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bayle,177.460487,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bede,96.457502,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Beethoven,68.21304,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Behring,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bell,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bloch,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Bordet,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Cabell,43.994967,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Cabot,39.513998,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Caesar,73.324945,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Caine,30.1447,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Calvin,28.922617,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Camus,55.40107,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Carew,50.920101,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Carrel,69.658698,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Carter,71.288141,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Caruso,79.435357,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Cary,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Caxton,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Cecil,107.950613,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Chain,79.027996,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Chase,129.133375,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Chifa,40.736081,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Chuhsi,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Clark,135.651148,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Clay,73.732306,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Clive,52.142183,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Cobb,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Cole,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Comte,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Curie,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,0,0,Curtiss,0.0,0.0,0.0,19.983547,20.400003
+2020-07-10,1,0,Abel,51.044715,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Adams,45.845716,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Adler,85.074525,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Agricola,34.975082,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Aiken,33.557174,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Alber,64.27853,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Alder,59.079531,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Alger,80.820799,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Ali,82.711344,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Allen,92.164069,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Anna,0.0,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Archer,0.0,0.0,0.0,0.0,0.0
+2020-07-10,1,0,Arne,125.248606,0.0,0.0,0.0,0.0
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/contingency_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/contingency_detail.csv
new file mode 100644
index 000000000..fdf161382
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/contingency_detail.csv
@@ -0,0 +1 @@
+Date,Hour,Minute,Contingency,Line,Flow,Violation
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/daily_summary.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/daily_summary.csv
new file mode 100644
index 000000000..24a9010f2
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/daily_summary.csv
@@ -0,0 +1,3 @@
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/hourly_gen_summary.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/hourly_gen_summary.csv
new file mode 100644
index 000000000..523828124
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/hourly_gen_summary.csv
@@ -0,0 +1,49 @@
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/hourly_summary.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/hourly_summary.csv
new file mode 100644
index 000000000..acf75e413
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/hourly_summary.csv
@@ -0,0 +1,49 @@
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/line_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/line_detail.csv
new file mode 100644
index 000000000..c436b5722
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/line_detail.csv
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/overall_simulation_output.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/overall_simulation_output.csv
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--- /dev/null
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+Total demand,Total fixed costs,Total generation costs,Total costs,Total load shedding,Total over generation,Total reserve shortfall,Total renewables curtailment,Total on/offs,Total sum on/off ramps,Total sum nominal ramps,Maximum observed demand,Overall renewables penetration rate,Cumulative average price,Total energy payments,Total reserve payments,Total uplift payments,Total payments,Cumulative average payments
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/runtimes.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/runtimes.csv
new file mode 100644
index 000000000..7401d39d4
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/runtimes.csv
@@ -0,0 +1,49 @@
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/thermal_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/thermal_detail.csv
new file mode 100644
index 000000000..9977038f8
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/thermal_detail.csv
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/tracker_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/tracker_detail.csv
new file mode 100644
index 000000000..703ee3b6d
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/tracker_detail.csv
@@ -0,0 +1,481 @@
+Date,Hour,Horizon [hr],Power Dispatch [MW],Power Output [MW],Power Underdelivered [MW],Power Overdelivered [MW]
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/tracking_model_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/tracking_model_detail.csv
new file mode 100644
index 000000000..55dd28467
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/tracking_model_detail.csv
@@ -0,0 +1,481 @@
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diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/virtual_detail.csv b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/virtual_detail.csv
new file mode 100644
index 000000000..5f875ac15
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/bidding_plugin_test_multiperiod_rankine/virtual_detail.csv
@@ -0,0 +1 @@
+Date,Hour,Minute,Generator,Output,Output DA,Unit Market Revenue,Unit Uplift Payment
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_double_loop_usc.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_double_loop_usc.py
new file mode 100644
index 000000000..2846b94ab
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_double_loop_usc.py
@@ -0,0 +1,366 @@
+#################################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2022 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+#################################################################################
+
+"""
+This script describes a multiperiod class to build an object for the
+integrated ultra-supercritical power plant and molten-salt based
+thermal energy storage model.
+"""
+
+__author__ = "Naresh Susarla"
+
+import pyomo.environ as pyo
+import pandas as pd
+from collections import deque
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant \
+    .storage.multiperiod_integrated_storage_usc import create_multiperiod_usc_model
+
+
+class MultiPeriodUsc:
+    def __init__(
+        self, model_data=None
+    ):
+        """
+        Arguments:
+            horizon: Int64 - number of time points to use for associated multi-period model
+
+        Returns:
+            Float64: Value of power output in last time step
+        """
+        self.multiperiod_usc = None
+        self.result_list = []
+        self.result_listimp = []
+        self.model_data = model_data
+
+    def populate_model(self, b, horizon):
+        """
+        Create an integrated ultra-supercritical power plant and molten salt
+        thermal energy storage model using the `MultiPeriod` package.
+
+        Arguments:
+            blk: this is an empty block passed in from either a bidder or tracker
+
+        Returns:
+             None
+        """
+        tank_min = 76000  # in kg
+        tank_max = 6739292  # in kg
+
+        blk = b
+        if not blk.is_constructed():
+            blk.construct()
+
+        multiperiod_usc = create_multiperiod_usc_model(
+            n_time_points=horizon,
+            pmin=self.model_data.p_min, pmax=self.model_data.p_max
+        )
+        blk.usc_mp = multiperiod_usc
+
+        active_blks = multiperiod_usc.get_active_process_blocks()
+        active_blks[0].usc_mp.previous_salt_inventory_hot.fix(tank_min)
+        active_blks[0].usc_mp.previous_salt_inventory_cold.fix(tank_max-tank_min)
+        active_blks[0].usc_mp.previous_power.fix(380)
+
+        # create expression that references underlying power variables
+        blk.HOUR = pyo.Set(initialize=range(horizon))
+        blk.P_T = pyo.Expression(blk.HOUR)
+        blk.tot_cost = pyo.Expression(blk.HOUR)
+        blk.hot_level = pyo.Expression(blk.HOUR)
+        blk.storage_power = pyo.Expression(blk.HOUR)
+        blk.plant_power = pyo.Expression(blk.HOUR)
+        blk.plant_duty = pyo.Expression(blk.HOUR)
+        blk.hxc_salt = pyo.Expression(blk.HOUR)
+        blk.hxc_duty = pyo.Expression(blk.HOUR)
+        blk.hxc_salt_Tin = pyo.Expression(blk.HOUR)
+        blk.hxc_salt_Tout = pyo.Expression(blk.HOUR)
+        blk.hxd_salt = pyo.Expression(blk.HOUR)
+        blk.hxd_duty = pyo.Expression(blk.HOUR)
+        blk.hxd_salt_Tin = pyo.Expression(blk.HOUR)
+        blk.hxd_salt_Tout = pyo.Expression(blk.HOUR)
+        blk.hxd_steam_Tout = pyo.Expression(blk.HOUR)
+        blk.hxd_steam_vfrac = pyo.Expression(blk.HOUR)
+        for (t, b) in enumerate(active_blks):
+            blk.P_T[t] = b.usc_mp.fs.net_power
+            blk.hot_level[t] = b.usc_mp.salt_inventory_hot
+            blk.storage_power[t] = ((-1e-6)
+                                 * b.usc_mp.fs.es_turbine.work_mechanical[0])
+            blk.plant_duty[t] = b.usc_mp.fs.plant_heat_duty[0]
+            blk.tot_cost[t] = (
+                     b.usc_mp.fs.operating_cost
+                     + (b.usc_mp.fs.plant_fixed_operating_cost
+                     + b.usc_mp.fs.plant_variable_operating_cost) / (365 * 24)
+                )
+            blk.plant_power[t] = b.usc_mp.fs.plant_power_out[0]
+            blk.hxc_salt[t] = b.usc_mp.fs.hxc.tube_inlet.flow_mass[0]
+            blk.hxc_duty[t] = b.usc_mp.fs.hxc.heat_duty[0]
+            blk.hxc_salt_Tin[t] = b.usc_mp.fs.hxc.tube_inlet.temperature[0]
+            blk.hxc_salt_Tout[t] = b.usc_mp.fs.hxc.tube_outlet.temperature[0]
+            blk.hxd_salt[t] = b.usc_mp.fs.hxd.shell_inlet.flow_mass[0]
+            blk.hxd_duty[t] = b.usc_mp.fs.hxd.heat_duty[0]
+            blk.hxd_salt_Tin[t] = b.usc_mp.fs.hxd.shell_inlet.temperature[0]
+            blk.hxd_salt_Tout[t] = b.usc_mp.fs.hxd.shell_outlet.temperature[0]
+            blk.hxd_steam_Tout[t] = b.usc_mp.fs.hxd.cold_side.properties_out[0].temperature
+            blk.hxd_steam_vfrac[t] = b.usc_mp.fs.hxd.cold_side.properties_out[0].vapor_frac
+
+
+        self.multiperiod_usc = multiperiod_usc
+        return
+
+    def update_model(self, b, implemented_power_output, realized_soc):
+
+        """
+        Update `blk` variables using the actual implemented power output.
+
+        Arguments:
+            blk: the block that needs to be updated
+            realized soc, i.e. the hot salt storage tank level:
+            implemented_power_output:
+
+         Returns:
+             None
+        """
+        blk = b
+        multiperiod_usc = blk.usc_mp
+        active_blks = multiperiod_usc.get_active_process_blocks()
+
+        implemented_power = round(implemented_power_output[-1])
+        realized_soc = round(realized_soc[-1])
+        print("Implemented Power (MPC)", implemented_power)
+        print("Realized SOC (MPC)", realized_soc)
+
+        active_blks[0].usc_mp.previous_power.fix(implemented_power)
+        active_blks[0].usc_mp.previous_salt_inventory_hot.fix(realized_soc)
+
+        return
+
+    @staticmethod
+    def get_last_delivered_power(b, last_implemented_time_step):
+
+        """
+        Returns the last delivered power output.
+
+        Arguments:
+            blk: the block
+            last_implemented_time_step: time index for the last implemented time
+                                        step
+
+        Returns:
+            Float64: Value of power output in the last time step
+        """
+        blk = b
+        return pyo.value(blk.P_T[last_implemented_time_step])
+
+    @staticmethod
+    def get_implemented_profile(b, last_implemented_time_step):
+
+        """
+        This method gets the implemented variable profiles in the last optimization solve.
+
+        Arguments:
+            blk: a Pyomo block
+            last_implemented_time_step: time index for the last implemented time step
+
+         Returns:
+             profile: the intended profile, {unit: [...]}
+        """
+        blk = b
+        multiperiod_usc = blk.usc_mp
+        active_blks = multiperiod_usc.get_active_process_blocks()
+        implemented_power_output = deque(
+            [
+                pyo.value(active_blks[t].usc_mp.fs.net_power)
+                for t in range(last_implemented_time_step + 1)
+            ]
+        )
+        realized_soc = deque(
+            [
+                pyo.value(active_blks[t].usc_mp.salt_inventory_hot)
+                for t in range(last_implemented_time_step + 1)
+            ]
+        )
+
+        return {
+            "implemented_power_output": implemented_power_output,
+            "realized_soc": realized_soc,
+        }
+
+    def record_results(self, b, date=None, hour=None, **kwargs):
+
+        """
+        Record the operations stats for the model.
+
+        Arguments:
+            blk:  pyomo block
+            date: current simulation date
+            hour: current simulation hour
+
+        Returns:
+            None
+
+        """
+        blk = b
+        df_list = []
+        df_listimp = []
+        for t in blk.HOUR:
+            result_dict = {}
+            result_implemented = {}
+
+            result_dict["Date"] = date
+            result_dict["Hour"] = hour
+
+            # simulation inputs
+            result_dict["Horizon [hr]"] = int(t)
+
+            # model vars
+            result_dict["Thermal Power Generated [MW]"] = float(
+                round(pyo.value(blk.P_T[t]), 2)
+            )
+            result_dict["Total Cost [$]"] = float(
+                round(pyo.value(blk.tot_cost[t]), 2)
+                )
+            result_dict["Hot Tank Level [MT]"] = float(
+                round(pyo.value(blk.hot_level[t]), 2)
+                )
+            result_dict["Plant Heat Duty [MWth]"] = float(
+                round(pyo.value(blk.plant_duty[t]), 2)
+                )
+            result_dict["Storage Power [MW]"] = float(
+                round(pyo.value(blk.storage_power[t]), 2)
+                )
+            result_dict["Plant Power [MW]"] = float(
+                round(pyo.value(blk.plant_power[t]), 2)
+                )
+            result_dict["HXC Duty"] = float(
+                round(pyo.value(blk.hxc_duty[t] * 1e-6), 2)
+                )
+            result_dict["HXD Duty"] = float(
+                round(pyo.value(blk.hxd_duty[t] * 1e-6), 2)
+                )
+            result_dict["HXC Salt Flow"] = float(
+                round(pyo.value(blk.hxc_salt[t]), 2)
+                )
+            result_dict["HXD Salt Flow"] = float(
+                round(pyo.value(blk.hxd_salt[t]), 2)
+                )
+            result_dict["HXC Salt T in"] = float(
+                round(pyo.value(blk.hxc_salt_Tin[t]), 2)
+                )
+            result_dict["HXC Salt T out"] = float(
+                round(pyo.value(blk.hxc_salt_Tout[t]), 2)
+                )
+            result_dict["HXD Salt T in"] = float(
+                round(pyo.value(blk.hxd_salt_Tin[t]), 2)
+                )
+            result_dict["HXD Salt T out"] = float(
+                round(pyo.value(blk.hxd_salt_Tout[t]), 2)
+                )
+            result_dict["HXD Steam T out"] = float(
+                round(pyo.value(blk.hxd_steam_Tout[t]), 2)
+                )
+            result_dict["HXD Steam out Vfrac"] = float(
+                round(pyo.value(blk.hxd_steam_vfrac[t]), 2)
+                )
+
+            if t == 0:
+                # simulation inputs
+                result_implemented["Date"] = date
+                result_implemented["Hour"] = hour
+                result_implemented["Horizon [hr]"] = int(t)
+
+                # model vars
+                result_implemented["Thermal Power Generated [MW]"] = float(
+                    round(pyo.value(blk.P_T[t]), 2)
+                )
+                result_implemented["Total Cost [$]"] = float(
+                    round(pyo.value(blk.tot_cost[t]), 2)
+                    )
+                result_implemented["Hot Tank Level [MT]"] = float(
+                    round(pyo.value(blk.hot_level[t]), 2)
+                    )
+                result_implemented["Plant Heat Duty [MWth]"] = float(
+                    round(pyo.value(blk.plant_duty[t]), 2)
+                    )
+                result_implemented["Storage Power [MW]"] = float(
+                    round(pyo.value(blk.storage_power[t]), 2)
+                    )
+                result_implemented["Plant Power [MW]"] = float(
+                    round(pyo.value(blk.plant_power[t]), 2)
+                    )
+                result_implemented["HXC Duty"] = float(
+                    round(pyo.value(blk.hxc_duty[t] * 1e-6), 2)
+                    )
+                result_implemented["HXD Duty"] = float(
+                    round(pyo.value(blk.hxd_duty[t] * 1e-6), 2)
+                    )
+                result_implemented["HXC Salt Flow"] = float(
+                    round(pyo.value(blk.hxc_salt[t]), 2)
+                    )
+                result_implemented["HXD Salt Flow"] = float(
+                    round(pyo.value(blk.hxd_salt[t]), 2)
+                    )
+                result_implemented["HXC Salt T in"] = float(
+                    round(pyo.value(blk.hxc_salt_Tin[t]), 2)
+                    )
+                result_implemented["HXC Salt T out"] = float(
+                    round(pyo.value(blk.hxc_salt_Tout[t]), 2)
+                    )
+                result_implemented["HXD Salt T in"] = float(
+                    round(pyo.value(blk.hxd_salt_Tin[t]), 2)
+                    )
+                result_implemented["HXD Salt T out"] = float(
+                    round(pyo.value(blk.hxd_salt_Tout[t]), 2)
+                    )
+                result_implemented["HXD Steam T out"] = float(
+                    round(pyo.value(blk.hxd_steam_Tout[t]), 2)
+                    )
+                result_implemented["HXD Steam out Vfrac"] = float(
+                    round(pyo.value(blk.hxd_steam_vfrac[t]), 2)
+                    )
+            for key in kwargs:
+                result_dict[key] = kwargs[key]
+                result_implemented[key] = kwargs[key]
+
+            result_df = pd.DataFrame.from_dict(result_dict, orient="index")
+            df_list.append(result_df.T)
+            result_df2 = pd.DataFrame.from_dict(result_implemented, orient="index")
+            df_listimp.append(result_df2.T)
+
+        # append to result list
+        self.result_list.append(pd.concat(df_list))
+        self.result_listimp.append(pd.concat(df_listimp))
+
+        return
+
+    def write_results(self, path):
+        """
+        Write the saved results to a csv file.
+
+        Arguments:
+            path: the path to write the results.
+
+        Return:
+            None
+        """
+
+        pd.concat(self.result_list).to_csv(path, index=False)
+        pd.concat(self.result_listimp).to_csv("tracking_results.csv")
+
+    @property
+    def power_output(self):
+        return "P_T"
+
+    @property
+    def total_cost(self):
+        return ("tot_cost", 1)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_integrated_storage_usc.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_integrated_storage_usc.py
new file mode 100644
index 000000000..83537e7af
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_integrated_storage_usc.py
@@ -0,0 +1,239 @@
+#################################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2022 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+#################################################################################
+
+"""
+This script uses the IDAES multiperiod class to create a steady state
+multiperiod model for the integrated ultra-supercritical power plant and
+energy storage system. The purpose of this script is to create a multiperiod
+model that can be use for market analysis either using a pricetaker assumption
+or in a real-time scenario using the double loop framework. The integrated
+storage with ultra-supercritical power plant model is used a steady state model
+for creating the multiperiod model.
+"""
+
+__author__ = "Naresh Susarla and Soraya Rawlings"
+
+try:
+    from importlib import resources  # Python 3.8+
+except ImportError:
+    import importlib_resources as resources  # Python 3.7
+
+
+from pyomo.environ import (NonNegativeReals, ConcreteModel, Constraint, Var)
+from idaes.apps.grid_integration.multiperiod.multiperiod import (
+    MultiPeriodModel)
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage import (
+    integrated_storage_with_ultrasupercritical_power_plant as usc)
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant import storage
+
+
+def create_usc_model(pmin, pmax):
+
+    # Set bounds for plant power
+    min_storage_heat_duty = 10  # in MW
+    max_storage_heat_duty = 200  # in MW
+
+    max_power = 436  # in MW
+    min_power = int(0.65 * max_power)  # 283 in MW
+
+    m = ConcreteModel()
+
+    with resources.path(storage, "initialized_integrated_storage_usc.json") as data_file_path:
+        assert data_file_path.is_file()
+        m.usc_mp = usc.main(max_power=max_power,
+                            load_from_file=str(data_file_path))
+
+    m.usc_mp.fs.plant_min_power_eq = Constraint(
+        expr=m.usc_mp.fs.plant_power_out[0] >= min_power
+    )
+    m.usc_mp.fs.plant_max_power_eq = Constraint(
+        expr=m.usc_mp.fs.plant_power_out[0] <= max_power
+    )
+
+    m.usc_mp.fs.hxc.heat_duty.setlb(min_storage_heat_duty * 1e6)
+    m.usc_mp.fs.hxd.heat_duty.setlb(min_storage_heat_duty * 1e6)
+
+    m.usc_mp.fs.hxc.heat_duty.setub(max_storage_heat_duty * 1e6)
+    m.usc_mp.fs.hxd.heat_duty.setub(max_storage_heat_duty * 1e6)
+
+    # Unfix data
+    m.usc_mp.fs.boiler.inlet.flow_mol[0].unfix()
+
+    # Unfix storage system data
+    m.usc_mp.fs.ess_hp_split.split_fraction[0, "to_hxc"].unfix()
+    m.usc_mp.fs.ess_bfp_split.split_fraction[0, "to_hxd"].unfix()
+    for salt_hxc in [m.usc_mp.fs.hxc]:
+        salt_hxc.shell_inlet.unfix()
+        salt_hxc.tube_inlet.flow_mass.unfix()  # kg/s, 1 DOF
+        salt_hxc.area.unfix()  # 1 DOF
+
+    for salt_hxd in [m.usc_mp.fs.hxd]:
+        salt_hxd.tube_inlet.unfix()
+        salt_hxd.shell_inlet.flow_mass.unfix()  # kg/s, 1 DOF
+        salt_hxd.area.unfix()  # 1 DOF
+
+    for unit in [m.usc_mp.fs.cooler]:
+        unit.inlet.unfix()
+    m.usc_mp.fs.cooler.outlet.enth_mol[0].unfix()  # 1 DOF
+
+    # Fix storage heat exchangers area and salt temperatures
+    m.usc_mp.fs.hxc.area.fix(1904)
+    m.usc_mp.fs.hxd.area.fix(2830)
+    m.usc_mp.fs.hxc.tube_outlet.temperature[0].fix(831)
+    m.usc_mp.fs.hxd.shell_inlet.temperature[0].fix(831)
+    m.usc_mp.fs.hxd.shell_outlet.temperature[0].fix(513.15)
+
+    return m
+
+
+def create_usc_mp_block(pmin=None, pmax=None):
+    print('>>> Creating USC model and initialization for each time period')
+
+    if pmin is None:
+        pmin = int(0.65 * 436) + 1
+    if pmax is None:
+        pmax = 436 + 30
+
+    m = create_usc_model(pmin, pmax)
+    b1 = m.usc_mp
+
+    # Add coupling variables
+    b1.previous_power = Var(
+        domain=NonNegativeReals,
+        initialize=300,
+        bounds=(pmin, pmax),
+        doc="Previous period power (MW)"
+        )
+
+    inventory_max = 1e7
+    inventory_min = 75000
+    tank_max = 6739292           # Units in kg
+
+    b1.previous_salt_inventory_hot = Var(
+        domain=NonNegativeReals,
+        initialize=inventory_min,
+        bounds=(0, inventory_max),
+        doc="Hot salt at the beginning of the hour (or time period), kg"
+        )
+    b1.salt_inventory_hot = Var(
+        domain=NonNegativeReals,
+        initialize=inventory_min,
+        bounds=(0, inventory_max),
+        doc="Hot salt inventory at the end of the hour (or time period), kg"
+        )
+    b1.previous_salt_inventory_cold = Var(
+        domain=NonNegativeReals,
+        initialize=tank_max-inventory_min,
+        bounds=(0, inventory_max),
+        doc="Cold salt at the beginning of the hour (or time period), kg"
+        )
+    b1.salt_inventory_cold = Var(
+        domain=NonNegativeReals,
+        initialize=tank_max-inventory_min,
+        bounds=(0, inventory_max),
+        doc="Cold salt inventory at the end of the hour (or time period), kg"
+        )
+
+    @b1.fs.Constraint(doc="Plant ramping down constraint")
+    def constraint_ramp_down(b):
+        return (
+            b1.previous_power - 60 <=
+            b1.fs.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Plant ramping up constraint")
+    def constraint_ramp_up(b):
+        return (
+            b1.previous_power + 60 >=
+            b1.fs.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Inventory balance at the end of the time period")
+    def constraint_salt_inventory_hot(b):
+        return (
+            b1.salt_inventory_hot ==
+            b1.previous_salt_inventory_hot
+            + (3600*b1.fs.hxc.tube_inlet.flow_mass[0]
+               - 3600*b1.fs.hxd.shell_inlet.flow_mass[0])
+        )
+
+    @b1.fs.Constraint(doc="Max salt flow to hxd based on available hot salt")
+    def constraint_salt_maxflow_hot(b):
+        return (
+            3600*b1.fs.hxd.shell_inlet.flow_mass[0] <=
+            b1.previous_salt_inventory_hot
+        )
+
+    @b1.fs.Constraint(doc="Max salt flow to hxc based on available cold salt")
+    def constraint_salt_maxflow_cold(b):
+        return (
+            3600*b1.fs.hxc.tube_inlet.flow_mass[0] <=
+            b1.previous_salt_inventory_cold
+        )
+
+    @b1.fs.Constraint(doc="Maximum salt inventory at any time")
+    def constraint_salt_inventory(b):
+        return (
+            b1.salt_inventory_hot +
+            b1.salt_inventory_cold == b1.fs.salt_amount)
+
+    return m
+
+
+# The tank level and power output are linked between the contiguous time periods
+def get_usc_link_variable_pairs(b1, b2):
+    """
+        b1: current time block
+        b2: next time block
+    """
+    return [(b1.usc_mp.salt_inventory_hot,
+             b2.usc_mp.previous_salt_inventory_hot),
+            (b1.usc_mp.fs.plant_power_out[0],
+             b2.usc_mp.previous_power)]
+
+
+# The tank level at the end of the last time period must be the same as at the
+# beginning of the first time period
+def get_usc_periodic_variable_pairs(b1, b2):
+    """
+        b1: final time block
+        b2: first time block
+    """
+    # return
+    return [(b1.usc_mp.salt_inventory_hot,
+             b2.usc_mp.previous_salt_inventory_hot)]
+
+# Create the multiperiod model object. You can pass arguments to your
+# "process_model_func" for each time period using a dict of dicts as
+# shown here.  In this case, it is setting up empty dictionaries for
+# each time period.
+
+
+def create_multiperiod_usc_model(n_time_points=4, pmin=None, pmax=None):
+    """
+    Create a multi-period usc_mp cycle object. This object contains a pyomo
+    model with a block for each time instance.
+
+    n_time_points: Number of time blocks to create
+    """
+    multiperiod_usc = MultiPeriodModel(
+        n_time_points,
+        lambda: create_usc_mp_block(pmin=None, pmax=None),
+        get_usc_link_variable_pairs,
+        get_usc_periodic_variable_pairs
+        )
+
+    # If you have no arguments, you don't actually need to pass in
+    # anything. NOTE: building the model will initialize each time block
+    multiperiod_usc.build_multi_period_model()
+    return multiperiod_usc
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_usc_storage_pricetaker.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_usc_storage_pricetaker.py
new file mode 100644
index 000000000..bb7a72939
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/multiperiod_usc_storage_pricetaker.py
@@ -0,0 +1,462 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2021 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+from idaes.apps.grid_integration.multiperiod.multiperiod import (
+    MultiPeriodModel)
+
+import pyomo.environ as pyo
+from pyomo.environ import (Param, Reals, Expression,
+                           NonNegativeReals,
+                           value, Var)
+from pyomo.util.infeasible import (log_infeasible_constraints,
+                                   log_close_to_bounds)
+import numpy as np
+
+from idaes.core.util.model_statistics import degrees_of_freedom
+
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage import (
+    integrated_storage_with_ultrasupercritical_power_plant as usc)
+
+# For plots
+from matplotlib import pyplot as plt
+import matplotlib
+matplotlib.rc('font', size=24)
+plt.rc('axes', titlesize=24)
+
+
+def create_ss_rankine_model():
+    p_lower_bound = 350  # MW
+    p_upper_bound = 450  # MW
+    boiler_heat_max = 918e6  # in W
+    boiler_heat_min = 626e6  # 586e6  # in W
+
+    m = pyo.ConcreteModel()
+    m.rankine = usc.main()
+    # set bounds for net cycle power output
+    m.rankine.fs.plant_power_out[0].unfix()
+    # m.rankine.fs.eq_min_power = pyo.Constraint(
+    #     expr=m.rankine.fs.plant_power_out[0] >= p_lower_bound)
+
+    # m.rankine.fs.eq_max_power = pyo.Constraint(
+    #     expr=m.rankine.fs.plant_power_out[0] <= p_upper_bound)
+
+    m.rankine.fs.boiler.inlet.flow_mol[0].unfix()  # normally fixed
+    # m.rankine.fs.boiler.inlet.flow_mol[0].setlb(1)
+    m.rankine.fs.boiler.inlet.flow_mol[0].setlb(11804)
+    m.rankine.fs.boiler.inlet.flow_mol[0].setub(17854)
+
+    m.rankine.fs.boiler.heat_duty[0].setlb(boiler_heat_min)
+    m.rankine.fs.boiler.heat_duty[0].setub(boiler_heat_max)
+
+    # Unfix all data
+    m.rankine.fs.ess_hp_split.split_fraction[0, "to_hxc"].unfix()
+    m.rankine.fs.ess_bfp_split.split_fraction[0, "to_hxd"].unfix()
+    for salt_hxc in [m.rankine.fs.hxc]:
+        salt_hxc.shell_inlet.unfix()
+        salt_hxc.tube_inlet.flow_mass.unfix()  # kg/s, 1 DOF
+        salt_hxc.area.unfix()  # 1 DOF
+
+    for salt_hxd in [m.rankine.fs.hxd]:
+        salt_hxd.tube_inlet.unfix()
+        salt_hxd.shell_inlet.flow_mass.unfix()  # kg/s, 1 DOF
+        salt_hxd.area.unfix()  # 1 DOF
+
+    for unit in [m.rankine.fs.cooler]:
+        unit.inlet.unfix()
+    m.rankine.fs.cooler.outlet.enth_mol[0].unfix()  # 1 DOF
+
+    # Fix storage heat exchangers area and salt temperatures
+    # m.rankine.fs.salt_hot_temperature = 831
+    m.rankine.fs.hxc.area.fix(1904)  # 1904
+    m.rankine.fs.hxd.area.fix(1095)  # 1095
+    m.rankine.fs.hxc.tube_outlet.temperature[0].fix(831)
+    m.rankine.fs.hxd.shell_inlet.temperature[0].fix(831)
+    m.rankine.fs.hxd.shell_outlet.temperature[0].fix(513.15)
+
+    return m
+
+
+# with open('rts_results_all_prices.npy', 'rb') as f:
+#     dispatch = np.load(f)
+#     price = np.load(f)
+
+# plt.figure(figsize=(12, 8))
+# prices_used = copy.copy(price)
+# prices_used[prices_used > 200] = 200
+# x = list(range(0, len(prices_used)))
+# plt.bar(x, (prices_used))
+# plt.xlabel("Hour")
+# plt.ylabel("LMP $/MWh")
+
+# weekly_prices = prices_used.reshape(52, 168)
+# plt.figure(figsize=(12, 8))
+# for week in [0, 15, 25, 35, 45, 51]:
+#     plt.plot(weekly_prices[week])
+# plt.title("6 Representative Weeks")
+# plt.xlabel("Hour")
+# plt.ylabel("LMP $/MWh")
+
+# plt.figure(figsize=(12, 8))
+# for week in range(0, 52):
+#     plt.plot(weekly_prices[week], color="blue", alpha=0.1)
+# plt.title("52 Representative Weeks")
+# plt.xlabel("Hour")
+# plt.ylabel("LMP $/MWh")
+
+# turbine_ramp_rate = 100
+# battery_ramp_rate = 50
+def create_mp_rankine_block():
+    m = create_ss_rankine_model()
+    b1 = m.rankine
+    #  DOF = 1
+    print('DOFs within mp create 1 =', degrees_of_freedom(m))
+    # Add coupling variable (next_power_output)
+    # b1.previous_power = Var(
+    #     # b1.fs.time,
+    #     domain=NonNegativeReals,
+    #     initialize=400,
+    #     bounds=(100, 450),
+    #     # bounds=(0, 6739292),
+    #     doc="Previous period power (MW)"
+    #     )
+
+    b1.previous_salt_inventory_hot = Var(
+        # b1.fs.time,
+        domain=NonNegativeReals,
+        initialize=1,
+        bounds=(0, 1e7),
+        # bounds=(0, 1e12),
+        # bounds=(0, 6739292),
+        doc="Hot salt at the beginning of the hour (or time period), kg"
+        )
+    b1.salt_inventory_hot = Var(
+        # b1.fs.time,
+        domain=NonNegativeReals,
+        initialize=80,
+        bounds=(0, 1e7),
+        # bounds=(0, 1e12),
+        # bounds=(0, 6739292),
+        doc="Hot salt inventory at the end of the hour (or time period), kg"
+        )
+    b1.previous_salt_inventory_cold = Var(
+        # b1.fs.time,
+        domain=NonNegativeReals,
+        initialize=1,
+        bounds=(0, 1e7),
+        # bounds=(0, 1e12),
+        # bounds=(0, 6739292),
+        doc="Cold salt at the beginning of the hour (or time period), kg"
+        )
+    b1.salt_inventory_cold = Var(
+        # b1.fs.time,
+        domain=NonNegativeReals,
+        initialize=80,
+        bounds=(0, 1e7),
+        # bounds=(0, 1e12),
+        # bounds=(0, 6739292),
+        doc="Cold salt inventory at the end of the hour (or time period), kg"
+        )
+
+    # @b1.fs.Constraint(doc="Plant ramping down constraint")
+    # def constraint_ramp_down(b):
+    #     return (
+    #         b1.previous_power - 40 >=
+    #         b1.fs.plant_power_out[0])
+
+    # @b1.fs.Constraint(doc="Plant ramping up constraint")
+    # def constraint_ramp_up(b):
+    #     return (
+    #         b1.previous_power + 40 <=
+    #         b1.fs.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Inventory balance at the end of the time period")
+    def constraint_salt_inventory_hot(b):
+        return (
+            b1.salt_inventory_hot ==
+            b1.previous_salt_inventory_hot
+            + 3600*b1.fs.hxc.tube_inlet.flow_mass[0]
+            - 3600*b1.fs.hxd.shell_inlet.flow_mass[0])
+
+    @b1.fs.Constraint(doc="Inventory balance at the end of the time period")
+    def constraint_salt_inventory_cold(b):
+        return (
+            b1.salt_inventory_cold ==
+            b1.previous_salt_inventory_cold
+            - 3600*b1.fs.hxc.tube_inlet.flow_mass[0]
+            + 3600*b1.fs.hxd.shell_inlet.flow_mass[0])
+
+    # @b1.fs.Constraint(doc="Maximum salt inventory at any time")
+    # def constraint_salt_inventory(b):
+    #     return (
+    #         b1.salt_inventory_hot +
+    #         b1.salt_inventory_cold == b1.fs.salt_amount)
+    # print('DOFs after mp create =', degrees_of_freedom(m))
+
+    @b1.fs.Constraint(doc="Maximum previous salt inventory at any time")
+    def constraint_salt_previous_inventory(b):
+        return (
+            b1.previous_salt_inventory_hot +
+            b1.previous_salt_inventory_cold == b1.fs.salt_amount)
+    print('DOFs after mp create =', degrees_of_freedom(m))
+    # raise Exception()
+    return m
+
+# the power output and battery state are linked between time periods
+
+
+def get_rankine_link_variable_pairs(b1, b2):
+    """
+        b1: current time block
+        b2: next time block
+    """
+    return [(b1.rankine.salt_inventory_hot,
+             b2.rankine.previous_salt_inventory_hot)]#,
+            # (b1.rankine.fs.plant_power_out[0],
+            #  b2.rankine.previous_power)]
+
+# the final power output and battery state must be the same
+# as the intial power output and battery state
+
+
+def get_rankine_periodic_variable_pairs(b1, b2):
+    """
+        b1: final time block
+        b2: first time block
+    """
+    # return
+    return [(b1.rankine.salt_inventory_hot,
+             b2.rankine.previous_salt_inventory_hot)]#,
+            # (b1.rankine.fs.plant_power_out[0],
+            #  b2.rankine.previous_power)]
+
+number_hours = 4
+n_time_points = 1 * number_hours  # hours in a week
+
+# create the multiperiod model object
+mp_rankine = MultiPeriodModel(
+    n_time_points=n_time_points,
+    process_model_func=create_mp_rankine_block,
+    linking_variable_func=get_rankine_link_variable_pairs,
+    periodic_variable_func=get_rankine_periodic_variable_pairs
+    )
+
+# you can pass arguments to your `process_model_func`
+# for each time period using a dict of dicts as shown here.
+# In this case, it is setting up empty dictionaries for each time period.
+
+# OPTIONAL KEYWORD ARGUMENTS
+# time_points = np.arange(0,n_time_points)
+# data_points = [{} for i in range(n_time_points)]
+# data_kwargs = dict(zip(time_points,data_points))
+# mp_rankine.build_multi_period_model(data_kwargs);
+
+# if you have no arguments, you don't actually need to pass in anything.
+mp_rankine.build_multi_period_model()
+# NOTE: building the model will initialize each time block
+
+# retrieve pyomo model and active process blocks (i.e. time blocks)
+m = mp_rankine.pyomo_model
+blks = mp_rankine.get_active_process_blocks()
+
+# power = [310, 325, 420, 400]  # , 310, 325, 420, 400]
+# lmp = [21, 22, 50, 100]  # , 22.4929, 21.8439, 23.4379, 23.4379]
+power = [310, 325, 420, 400] #, 310, 325, 420, 400, 310, 325, 420, 400,
+         # 310, 325, 420, 400, 310, 325, 420, 400, 310, 325, 420, 400]
+lmp = [10, 20, 50, 100] #, 21, 22, 50, 100, 21, 22, 50, 100,
+       # 21, 22, 50, 100, 21, 22, 50, 100, 21, 22, 50, 100]
+# lmp = [22.4929, 21.8439, 23.4379, 23.4379, 23.4379, 21.6473, 21.6473]
+
+count = 0
+# add market data for each block
+for blk in blks:
+    # dummy_water_cost = 1.5e-2 # $/mol
+    blk_rankine = blk.rankine
+    blk.lmp_signal = Param(default=0, mutable=True)
+    blk.net_power = Expression(expr=(
+        blk.rankine.fs.plant_power_out[0]
+        + (-1e-6) * blk.rankine.fs.es_turbine.work_mechanical[0]))
+    blk.revenue = lmp[count]*blk.net_power
+    # blk.revenue = blk.lmp_signal*blk_rankine.fs.plant_power_out[0]
+    blk.operating_cost = pyo.Expression(
+        expr=(
+            (blk_rankine.fs.operating_cost
+             + blk_rankine.fs.plant_fixed_operating_cost
+             + blk_rankine.fs.plant_variable_operating_cost) / (365 * 24)
+            # + blk_rankine.fs.condenser_mix.makeup.flow_mol[0] * dummy_water_cost * 3600
+        )
+    )
+    blk.cost = pyo.Expression(expr=-(blk.revenue - blk.operating_cost))
+    # blk.fix_power = pyo.Constraint(
+    #     expr=power[count] == blk.net_power
+    # )
+    # Cycle efficiency
+    blk.cycle_efficiency = Expression(
+        expr=blk.net_power / \
+        blk.rankine.fs.plant_heat_duty[0] * 100
+    )
+    # blk.fix_power = pyo.Constraint(
+    #     expr=blk.dispatch == (
+    #         blk.rankine.fs.plant_power_out[0]
+    #         + (-1e-6) * blk.rankine.fs.es_turbine.work_mechanical[0]
+    #     )
+    # )
+    count += 1
+
+m.obj = pyo.Objective(expr=sum([blk.cost for blk in blks]))
+blks[0].rankine.previous_salt_inventory_hot.fix(1)
+# blks[0].rankine.previous_salt_inventory_cold.fix(1)
+# blks[0].rankine.previous_power.fix(400)
+
+n_weeks = 1
+opt = pyo.SolverFactory('ipopt')
+hot_tank_level = []
+net_power = []
+
+for week in range(n_weeks):
+    print("Solving for week: ", week)
+    # for (i, blk) in enumerate(blks):
+    #     blk.lmp_signal = weekly_prices[week][i]
+    opt.solve(m, tee=True)
+    hot_tank_level.append(
+        [pyo.value(blks[i].rankine.salt_inventory_hot)
+         for i in range(n_time_points)])
+    net_power.append(
+        # [pyo.value(blks[i].rankine.fs.plant_power_out[0])
+        [pyo.value(blks[i].net_power)
+         for i in range(n_time_points)])
+log_close_to_bounds(m)
+log_infeasible_constraints(m)
+
+c = 0
+for blk in blks:
+    print()
+    print('Period {}'.format(c+1))
+    print(' Previous hot salt inventory: {:.4f}'.format(
+        value(blks[c].rankine.previous_salt_inventory_hot)))
+    print(' Hot salt inventory: {:.4f}'.format(
+        value(blks[c].rankine.salt_inventory_hot)))
+    print(' Boiler heat duty: {:.4f}'.format(
+        value(blks[c].rankine.fs.boiler.heat_duty[0]) * 1e-6))
+    print(' Boiler flow mol (mol/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.boiler.outlet.flow_mol[0])))
+    print(' Cycle efficiency (%): {:.4f}'.format(
+        value(blks[c].cycle_efficiency)))
+    print(' Net power (MW): {} (Plant Power Out: {:.4f}, ES Turbine: {:.4f})'.format(
+        value(blks[c].net_power),
+        value(blks[c].rankine.fs.plant_power_out[0]),
+        value(blks[c].rankine.fs.es_turbine.work_mechanical[0])*(-1e-6)))
+    print(' Salt from HXC (kg) [kg/s]: {:.4f} [{:.4f}]'.format(
+        value(blks[c].rankine.fs.hxc.tube_outlet.flow_mass[0]) * 3600,
+        value(blks[c].rankine.fs.hxc.tube_outlet.flow_mass[0])))
+    print(' Salt from HXD (kg) [kg/s]: {:.4f} [{:.4f}]'.format(
+        value(blks[c].rankine.fs.hxd.shell_outlet.flow_mass[0]) * 3600,
+        value(blks[c].rankine.fs.hxd.shell_outlet.flow_mass[0])))
+    print(' HXC Duty (MW): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.heat_duty[0]) * 1e-6))
+    print(' HXD Duty (MW): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.heat_duty[0]) * 1e-6))
+    print(' Split fraction to HXC: {:.4f}'.format(
+        value(blks[c].rankine.fs.ess_hp_split.split_fraction[0, "to_hxc"])))
+    print(' Split fraction to HXD: {:.4f}'.format(
+        value(blks[c].rankine.fs.ess_bfp_split.split_fraction[0, "to_hxd"])))
+    print(' Steam flow HXC (mol/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.shell_outlet.flow_mol[0])))
+    print(' Steam flow HXD (mol/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.tube_outlet.flow_mol[0])))
+    print(' Makeup water flow: {:.6f}'.format(
+        value(blks[c].rankine.fs.condenser_mix.makeup.flow_mol[0])))
+    print(' Delta T in HXC (K): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.delta_temperature_in[0])))
+    print(' Delta T out HXC (K): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.delta_temperature_out[0])))
+    print(' Delta T in HXD (K): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.delta_temperature_in[0])))
+    print(' Delta T out HXD (K): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.delta_temperature_out[0])))
+    c += 1
+
+n_weeks_to_plot = 1
+hours = np.arange(n_time_points*n_weeks_to_plot)
+# lmp_array = weekly_prices[0:n_weeks_to_plot].flatten()
+lmp_array = np.asarray(lmp[0:n_time_points])
+hot_tank_array = np.asarray(hot_tank_level[0:n_weeks_to_plot]).flatten()
+
+# Convert array to list to include hot tank level at time zero
+lmp_list = [0] + lmp_array.tolist()
+hot_tank_array0 = value(blks[0].rankine.previous_salt_inventory_hot)
+hours_list = hours.tolist() + [number_hours]
+hot_tank_list = [hot_tank_array0] + hot_tank_array.tolist()
+
+color = ['tab:green', 'b', 'r']
+plt.rcParams['lines.linewidth'] = 2
+font = {'size':16}
+plt.rc('font', **font)
+
+fig1, ax1 = plt.subplots(figsize=(10, 5))
+ax1.spines["top"].set_visible(False)
+ax1.spines["right"].set_visible(False)
+ax1.grid(linestyle=':', which='both',
+         color='#696969', alpha=0.20)
+ax1.set_xlabel('Time Period (hr)')
+ax1.set_ylabel('Hot Tank Level [kg]', color=color[2])
+ax1.step(# [x + 1 for x in hours], hot_tank_array,
+    hours_list, hot_tank_list,
+    marker='.', ms=8,
+    ls='-', lw=1,
+    color=color[2])
+ax1.tick_params(axis='y', labelcolor=color[2])
+ax1.set_xticks(np.arange(0, n_time_points*n_weeks_to_plot + 1, step=1))
+
+ax2 = ax1.twinx()
+ax2.set_ylabel('LMP [$/MWh]',
+               color=color[1])
+ax2.step(# [x + 1 for x in hours], lmp_array,
+    hours_list, lmp_list,
+    marker='o', ls='-', lw=1,
+    color=color[1])
+ax2.tick_params(axis='y', labelcolor=color[1])
+plt.savefig('hot_tank_lmp_vs_hours.png')
+
+power_array = np.asarray(net_power[0:n_weeks_to_plot]).flatten()
+# Convert array to list to include net power at time zero
+power_array0 = 0 # zero since the plant is not operating
+power_list = [power_array0] + power_array.tolist()
+
+fig2, ax3 = plt.subplots(figsize=(10, 5))
+ax3.spines["top"].set_visible(False)
+ax3.spines["right"].set_visible(False)
+ax3.grid(linestyle=':', which='both',
+         color='#696969', alpha=0.20)
+ax3.set_xlabel('Time Period (hr)')
+ax3.set_ylabel('Net Power [MW]', color=color[0])
+ax3.step(# [x + 1 for x in hours], power_array
+    hours_list, power_list,
+    marker='.', ms=8,
+    ls='-', lw=1,
+    color=color[0])
+ax3.tick_params(axis='y',
+                labelcolor=color[0])
+ax3.set_xticks(np.arange(0, n_time_points*n_weeks_to_plot + 1, step=1))
+
+ax4 = ax3.twinx()
+ax4.set_ylabel('LMP [$/MWh]',
+               color=color[1])
+ax4.step([x + 1 for x in hours], lmp_array,
+         marker='o', ls='-', lw=1,
+         color=color[1])
+ax4.tick_params(axis='y',
+                labelcolor=color[1])
+plt.savefig('net_power_lmp_vs_hours.png')
+plt.show()
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/nlp_multiperiod_usc_static_doubleloop.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/nlp_multiperiod_usc_static_doubleloop.py
new file mode 100644
index 000000000..33d50764a
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/nlp_multiperiod_usc_static_doubleloop.py
@@ -0,0 +1,576 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2021 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+from idaes.apps.grid_integration.multiperiod.multiperiod import (
+    MultiPeriodModel)
+
+import pyomo.environ as pyo
+from pyomo.environ import (Param, Reals,
+                           NonNegativeReals,
+                           value, Var)
+from pyomo.util.infeasible import log_close_to_bounds
+import numpy as np
+
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage import (
+    integrated_storage_with_ultrasupercritical_power_plant as usc)
+
+# For plots
+from matplotlib import pyplot as plt
+import matplotlib
+matplotlib.rc('font', size=24)
+plt.rc('axes', titlesize=24)
+
+import logging
+logging.getLogger('pyomo.repn.plugins.nl_writer').setLevel(logging.ERROR)
+
+max_power = 436 # in MW
+min_power = int(0.65 * max_power) # 283 in MW
+max_power_storage = 29 # in MW
+min_power_storage = 2 # in MW
+max_power_total = max_power + max_power_storage
+min_power_total = min_power + min_power_storage
+min_storage_heat_duty = 0.01 # in MW
+max_storage_heat_duty = 150 # in MW
+# TODO: Verify that this is the right data file
+load_from_file = '../final_report/initialized_usc_storage_nlp_mp_unfixed_area.json'
+
+# Add number of days and hours per week
+number_days = 1
+hours_per_day = 24
+number_hours = hours_per_day * number_days
+n_time_points = 1 * number_hours  # hours in a week
+
+lx = False
+if lx:
+    scaling_obj = 1
+    scaling_factor = 1e-3
+else:
+    scaling_obj = 1e-3
+    scaling_factor = 1e-3
+
+print()
+print('Scaling_factor:', scaling_factor)
+
+tank_scenario = "hot_empty" # scenarios: "hot_empty", "hot_full", "hot_half_full"
+tank_min = 1 * scaling_factor# in kg
+tank_max = 6739292 * scaling_factor# in kg
+
+power_dispatch = [400, 386, 430, 410,
+         450, 395, 380, 325,
+         300, 0, 0, 0,
+         0, 0, 0, 0,
+         320, 360, 415, 450,
+         455, 440, 445, 430]
+# Select lmp source data and scaling factor according to that
+use_rts_data = False
+use_mod_rts_data = True
+if use_rts_data:
+    print('>>>>>> Using RTS lmp data')
+    with open('rts_results_all_prices_base_case.npy', 'rb') as f:
+        dispatch = np.load(f)
+        price = np.load(f)
+elif use_mod_rts_data:
+    # price = [22.9684, 21.1168, 20.4, 20.419,
+    #          20.419, 21.2877, 23.07, 25,
+    #          18.4634, 0, 0, 0,
+    #          0, 0, 0, 0,
+    #          19.0342, 23.07, 200, 200,
+    #          200, 200, 200, 200]
+    price = [22.9684, 21.1168, 20.4, 20.419,
+             20.419, 21.2877, 23.07, 25,
+             18.4634, 0, 0, 0,
+             0, 0, 0, 0,
+             19.0342, 23.07, 22.9684, 21.1168,
+             19.0342, 23.07, 22.9684, 21.1168]
+             # 100, 100, 100, 100]
+else:
+    print('>>>>>> Using NREL lmp data')
+    price = np.load("nrel_scenario_average_hourly.npy")
+
+def create_ss_rankine_model():
+
+    m = pyo.ConcreteModel()
+    m.rankine = usc.main(max_power=max_power,
+                         load_from_file=load_from_file)
+
+    # Set bounds for plant power
+    m.rankine.fs.plant_min_power_eq = pyo.Constraint(
+        expr=m.rankine.fs.plant_power_out[0] >= min_power
+    )
+    m.rankine.fs.plant_max_power_eq = pyo.Constraint(
+        expr=m.rankine.fs.plant_power_out[0] <= max_power
+    )
+
+    # Set bounds for discharge turbine
+    m.rankine.fs.es_turbine_min_power_eq = pyo.Constraint(
+        expr=m.rankine.fs.es_turbine.work[0] * (-1e-6) >= min_power_storage
+    )
+    m.rankine.fs.es_turbine_max_power_eq = pyo.Constraint(
+        expr=m.rankine.fs.es_turbine.work[0] * (-1e-6) <= max_power_storage
+    )
+
+    m.rankine.fs.hxc.heat_duty.setlb(min_storage_heat_duty * 1e6)
+    m.rankine.fs.hxd.heat_duty.setlb(min_storage_heat_duty * 1e6)
+    m.rankine.fs.hxc.heat_duty.setub(max_storage_heat_duty * 1e6)
+    # m.rankine.fs.hxd.heat_duty.setub(max_storage_heat_duty * 1e6 * (1 - 0.01))
+
+    # Unfix data
+    m.rankine.fs.boiler.inlet.flow_mol[0].unfix()
+
+    # Unfix storage system data
+    m.rankine.fs.ess_hp_split.split_fraction[0, "to_hxc"].unfix()
+    m.rankine.fs.ess_bfp_split.split_fraction[0, "to_hxd"].unfix()
+    for salt_hxc in [m.rankine.fs.hxc]:
+        salt_hxc.shell_inlet.unfix()
+        salt_hxc.tube_inlet.flow_mass.unfix()  # kg/s, 1 DOF
+        salt_hxc.area.unfix()  # 1 DOF
+
+    for salt_hxd in [m.rankine.fs.hxd]:
+        salt_hxd.tube_inlet.unfix()
+        salt_hxd.shell_inlet.flow_mass.unfix()  # kg/s, 1 DOF
+        salt_hxd.area.unfix()  # 1 DOF
+
+    for unit in [m.rankine.fs.cooler]:
+        unit.inlet.unfix()
+    m.rankine.fs.cooler.outlet.enth_mol[0].unfix()  # 1 DOF
+
+    # Fix storage heat exchangers area and salt temperatures
+    m.rankine.fs.hxc.area.fix(1904)
+    m.rankine.fs.hxd.area.fix(2830)
+    m.rankine.fs.hxc.tube_outlet.temperature[0].fix(831)
+    m.rankine.fs.hxd.shell_inlet.temperature[0].fix(831)
+    m.rankine.fs.hxd.shell_outlet.temperature[0].fix(513.15)
+
+    return m
+
+
+def create_mp_rankine_block():
+    print('>>> Creating USC model and initialization for each time period')
+    m = create_ss_rankine_model()
+    b1 = m.rankine
+
+    # print('DOFs within mp create 1 =', degrees_of_freedom(m))
+
+    # Add coupling variables
+    b1.previous_power = Var(
+        domain=NonNegativeReals,
+        initialize=400,
+        bounds=(min_power, max_power_total),
+        doc="Previous period power (MW)"
+        )
+
+    inventory_max = 1e7 * scaling_factor
+    b1.previous_salt_inventory_hot = Var(
+        domain=NonNegativeReals,
+        initialize=1,
+        bounds=(0, inventory_max),
+        doc="Hot salt at the beginning of the hour (or time period), kg"
+        )
+    b1.salt_inventory_hot = Var(
+        domain=NonNegativeReals,
+        initialize=80,
+        bounds=(0, inventory_max),
+        doc="Hot salt inventory at the end of the hour (or time period), kg"
+        )
+    b1.previous_salt_inventory_cold = Var(
+        domain=NonNegativeReals,
+        initialize=1,
+        bounds=(0, inventory_max),
+        doc="Cold salt at the beginning of the hour (or time period), kg"
+        )
+    b1.salt_inventory_cold = Var(
+        domain=NonNegativeReals,
+        initialize=80,
+        bounds=(0, inventory_max),
+        doc="Cold salt inventory at the end of the hour (or time period), kg"
+        )
+
+    @b1.fs.Constraint(doc="Plant ramping down constraint")
+    def constraint_ramp_down(b):
+        return (
+            b1.previous_power - 60 <=
+            b1.fs.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Plant ramping up constraint")
+    def constraint_ramp_up(b):
+        return (
+            b1.previous_power + 60 >=
+            b1.fs.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Inventory balance at the end of the time period")
+    def constraint_salt_inventory_hot(b):
+        return (
+            b1.salt_inventory_hot ==
+            b1.previous_salt_inventory_hot
+            + (3600*b1.fs.hxc.tube_inlet.flow_mass[0]
+               - 3600*b1.fs.hxd.shell_inlet.flow_mass[0]) * scaling_factor
+        )
+
+    @b1.fs.Constraint(doc="Maximum salt inventory at any time")
+    def constraint_salt_inventory(b):
+        return (
+            b1.salt_inventory_hot +
+            b1.salt_inventory_cold == b1.fs.salt_amount * scaling_factor)
+    # print('DOFs after mp create =', degrees_of_freedom(m))
+
+    return m
+
+
+# The tank level and power output are linked between time periods
+def get_rankine_link_variable_pairs(b1, b2):
+    """
+        b1: current time block
+        b2: next time block
+    """
+    return [(b1.rankine.salt_inventory_hot,
+             b2.rankine.previous_salt_inventory_hot),
+            (b1.rankine.fs.plant_power_out[0],
+             b2.rankine.previous_power)]
+
+
+# The final tank level and power output must be the same as the initial
+# tank level and power output state
+def get_rankine_periodic_variable_pairs(b1, b2):
+    """
+        b1: final time block
+        b2: first time block
+    """
+    # return
+    return [(b1.rankine.salt_inventory_hot,
+             b2.rankine.previous_salt_inventory_hot)]#,
+    #         # (b1.rankine.fs.plant_power_out[0],
+    #         #  b2.rankine.previous_power)]
+
+
+# Create the multiperiod model object. You can pass arguments to your
+# "process_model_func" for each time period using a dict of dicts as
+# shown here.  In this case, it is setting up empty dictionaries for
+# each time period.
+
+mp_rankine = MultiPeriodModel(
+    n_time_points=n_time_points,
+    process_model_func=create_mp_rankine_block,
+    linking_variable_func=get_rankine_link_variable_pairs,
+    # periodic_variable_func=get_rankine_periodic_variable_pairs
+)
+
+# OPTIONAL KEYWORD ARGUMENTS
+# time_points = np.arange(0,n_time_points)
+# data_points = [{} for i in range(n_time_points)]
+# data_kwargs = dict(zip(time_points,data_points))
+# mp_rankine.build_multi_period_model(data_kwargs);
+
+# If you have no arguments, you don't actually need to pass in
+# anything. NOTE: building the model will initialize each time block
+mp_rankine.build_multi_period_model()
+
+# Retrieve pyomo model and active process blocks (i.e. time blocks)
+m = mp_rankine.pyomo_model
+blks = mp_rankine.get_active_process_blocks()
+
+if use_rts_data:
+    lmp = price[0:number_hours].tolist()
+elif use_mod_rts_data:
+    lmp = price
+# print(lmp)
+
+# Add lmp market data for each block
+count = 0
+for blk in blks:
+    blk_rankine = blk.rankine
+    blk.lmp_signal = Param(default=0, mutable=True)
+    blk.revenue = lmp[count]*blk.rankine.fs.net_power * scaling_factor
+    # blk.revenue = blk.lmp_signal*blk_rankine.fs.plant_power_out[0]
+    blk.operating_cost = pyo.Expression(
+        expr=(
+            (blk_rankine.fs.operating_cost
+             + blk_rankine.fs.plant_fixed_operating_cost
+             + blk_rankine.fs.plant_variable_operating_cost) / (365 * 24)
+        ) * scaling_factor
+    )
+    blk.cost = pyo.Expression(expr=-(blk.revenue - blk.operating_cost))
+    blk_rankine.fs.plant_power_dispatch_eq = pyo.Constraint(
+        expr=blk.rankine.fs.net_power >= power_dispatch[count]
+    )
+    count += 1
+
+m.obj = pyo.Objective(expr=sum([blk.cost for blk in blks]) * scaling_obj)
+
+# Initial state for salt tank for different scenarios
+if tank_scenario == "hot_empty":
+    blks[0].rankine.previous_salt_inventory_hot.fix(tank_min)
+    blks[0].rankine.previous_salt_inventory_cold.fix(tank_max-tank_min)
+elif tank_scenario == "hot_half_full":
+    blks[0].rankine.previous_salt_inventory_hot.fix(tank_max/2)
+    blks[0].rankine.previous_salt_inventory_cold.fix(tank_max/2)
+elif tank_scenario == "hot_full":
+    blks[0].rankine.previous_salt_inventory_hot.fix(tank_max-tank_min)
+    blks[0].rankine.previous_salt_inventory_cold.fix(tank_min)
+else:
+    print("Unrecognized scenario! Try hot_empty, hot_full, or hot_half_full")
+
+blks[0].rankine.previous_power.fix(400)
+
+# Plot results
+n_weeks = 1
+opt = pyo.SolverFactory('ipopt')
+opt.options = {
+    "max_iter": 100,
+}
+hot_tank_level = []
+cold_tank_level = []
+net_power = []
+hxc_duty = []
+hxd_duty = []
+for week in range(n_weeks):
+    print()
+    print(">>>>>> Solving for week {}: {} hours of operation in {} day(s) ".format(week + 1, number_hours, number_days))
+    # for (i, blk) in enumerate(blks):
+    #     blk.lmp_signal = weekly_prices[week][i]
+    results = opt.solve(m, tee=True)
+    hot_tank_level.append(
+        [(pyo.value(blks[i].rankine.salt_inventory_hot) / scaling_factor) * 1e-3
+         for i in range(n_time_points)])
+    cold_tank_level.append(
+        [(pyo.value(blks[i].rankine.salt_inventory_cold) / scaling_factor) * 1e-3
+         for i in range(n_time_points)])
+    # cold_tank_level.append(
+    #     [((tank_max - pyo.value(blks[i].rankine.salt_inventory_hot)) / scaling_factor) * 1e-3
+    #      for i in range(n_time_points)])
+    net_power.append(
+        [pyo.value(blks[i].rankine.fs.net_power)
+         for i in range(n_time_points)])
+    hxc_duty.append(
+        [pyo.value(blks[i].rankine.fs.hxc.heat_duty[0]) * 1e-6
+         for i in range(n_time_points)])
+    hxd_duty.append(
+        [pyo.value(blks[i].rankine.fs.hxd.heat_duty[0]) * 1e-6
+         for i in range(n_time_points)])
+
+log_close_to_bounds(m)
+# log_infeasible_constraints(m)
+print(results)
+
+c = 0
+print('Objective: {:.4f}'.format(value(m.obj)))
+for blk in blks:
+    print()
+    print('Period {}'.format(c+1))
+    print(' Net power: {:.4f}'.format(
+        value(blks[c].rankine.fs.net_power)))
+    print(' Plant Power Out: {:.4f}'.format(
+        value(blks[c].rankine.fs.plant_power_out[0])))
+    print(' ES Turbine Power: {:.4f}'.format(
+        value(blks[c].rankine.fs.es_turbine.work_mechanical[0])*(-1e-6)))
+    print(' Cost ($): {:.4f}'.format(value(blks[c].cost) / scaling_factor))
+    print(' Revenue ($): {:.4f}'.format(value(blks[c].revenue) / scaling_factor))
+    print(' Operating cost ($): {:.4f}'.format(value(blks[c].operating_cost) / scaling_factor))
+    print(' Specific Operating cost ($/MWh): {:.4f}'.format(
+        (value(blks[c].operating_cost) / scaling_factor) / value(blks[c].rankine.fs.net_power)))
+    print(' Cycle efficiency (%): {:.4f}'.format(
+        value(blks[c].rankine.fs.cycle_efficiency)))
+    print(' Boiler efficiency (%): {:.4f}'.format(
+        value(blks[c].rankine.fs.boiler_eff) * 100))
+    print(' Boiler heat duty: {:.4f}'.format(
+        value(blks[c].rankine.fs.boiler.heat_duty[0]) * 1e-6))
+    print(' Boiler flow mol (mol/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.boiler.outlet.flow_mol[0])))
+    print(' Previous salt inventory (mton): {:.4f}'.format(
+        (value(blks[c].rankine.previous_salt_inventory_hot) / scaling_factor) * 1e-3))
+    print(' Salt from HXC (mton): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.tube_outlet.flow_mass[0]) * 3600 * 1e-3))
+    print(' Salt from HXD (mton): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.shell_outlet.flow_mass[0]) * 3600 * 1e-3))
+    print(' HXC Duty (MW): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.heat_duty[0]) * 1e-6))
+    print(' HXD Duty (MW): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.heat_duty[0]) * 1e-6))
+    print(' Split fraction to HXC: {:.4f}'.format(
+        value(blks[c].rankine.fs.ess_hp_split.split_fraction[0, "to_hxc"])))
+    print(' Split fraction to HXD: {:.4f}'.format(
+        value(blks[c].rankine.fs.ess_bfp_split.split_fraction[0, "to_hxd"])))
+    print(' Salt flow HXC (kg/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.tube_outlet.flow_mass[0])))
+    print(' Salt flow HXD (kg/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.shell_outlet.flow_mass[0])))
+    print(' Steam flow HXC (mol/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.shell_outlet.flow_mol[0])))
+    print(' Steam flow HXD (mol/s): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.tube_outlet.flow_mol[0])))
+    print(' Delta T in HXC (kg): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.delta_temperature_in[0])))
+    print(' Delta T out HXC (kg): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxc.delta_temperature_out[0])))
+    print(' Delta T in HXD (kg): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.delta_temperature_in[0])))
+    print(' Delta T out HXD (kg): {:.4f}'.format(
+        value(blks[c].rankine.fs.hxd.delta_temperature_out[0])))
+    c += 1
+
+n_weeks_to_plot = 1
+hours = np.arange(n_time_points*n_weeks_to_plot)
+# lmp_array = weekly_prices[0:n_weeks_to_plot].flatten()
+lmp_array = np.asarray(lmp[0:n_time_points])
+hot_tank_array = np.asarray(hot_tank_level[0:n_weeks_to_plot]).flatten()
+cold_tank_array = np.asarray(cold_tank_level[0:n_weeks_to_plot]).flatten()
+
+# Convert array to list to include hot tank level at time zero
+lmp_list = [0] + lmp_array.tolist()
+hot_tank_array0 = (value(blks[0].rankine.previous_salt_inventory_hot) / scaling_factor) * 1e-3
+cold_tank_array0 = (value(blks[0].rankine.previous_salt_inventory_cold) / scaling_factor) * 1e-3
+hours_list = hours.tolist() + [number_hours]
+hot_tank_list = [hot_tank_array0] + hot_tank_array.tolist()
+cold_tank_list = [cold_tank_array0] + cold_tank_array.tolist()
+
+font = {'size':16}
+plt.rc('font', **font)
+fig1, ax1 = plt.subplots(figsize=(12, 8))
+
+color = ['r', 'b', 'tab:green', 'k', 'tab:orange']
+ax1.set_xlabel('Time Period (hr)')
+ax1.set_ylabel('Salt Tank Level (metric ton)',
+               color=color[3])
+ax1.spines["top"].set_visible(False)
+ax1.spines["right"].set_visible(False)
+ax1.grid(linestyle=':', which='both',
+         color='gray', alpha=0.30)
+plt.axhline((tank_max / scaling_factor)*1e-3, ls=':', lw=1.75,
+            color=color[4])
+plt.text(number_hours / 2 - 1.5, (tank_max/scaling_factor)*1e-3 + 100, 'max salt',
+         color=color[4])
+ax1.step(# [x + 1 for x in hours], hot_tank_array,
+    hours_list, hot_tank_list,
+    marker='^', ms=4, label='Hot Salt',
+    lw=1, color=color[0])
+ax1.step(# [x + 1 for x in hours], hot_tank_array,
+    hours_list, cold_tank_list,
+    marker='v', ms=4, label='Cold Salt',
+    lw=1, color=color[1])
+ax1.legend(loc="center right", frameon=False)
+ax1.tick_params(axis='y')#,
+                # labelcolor=color[3])
+ax1.set_xticks(np.arange(0, n_time_points*n_weeks_to_plot + 1, step=2))
+
+ax2 = ax1.twinx()
+ax2.set_ylabel('LMP ($/MWh)',
+               color=color[2])
+ax2.step([x + 1 for x in hours], lmp_array,
+         marker='o', ms=3, alpha=0.5,
+         ls='-', lw=1,
+         color=color[2])
+ax2.tick_params(axis='y',
+                labelcolor=color[2])
+plt.savefig('multiperiod_usc_storage_new_area_rts1_salt_tank_level_{}h.png'.format(hours_per_day))
+
+
+font = {'size':18}
+plt.rc('font', **font)
+
+power_array = np.asarray(net_power[0:n_weeks_to_plot]).flatten()
+# Convert array to list to include net power at time zero
+power_array0 = value(blks[0].rankine.previous_power)
+power_list = [power_array0] + power_array.tolist()
+
+fig2, ax3 = plt.subplots(figsize=(12, 8))
+ax3.set_xlabel('Time Period (hr)')
+ax3.set_ylabel('Net Power Output (MW)',
+               color=color[1])
+ax3.spines["top"].set_visible(False)
+ax3.spines["right"].set_visible(False)
+ax3.grid(linestyle=':', which='both',
+         color='gray', alpha=0.30)
+plt.text(number_hours / 2 - 3, max_power - 5.5, 'max plant power',
+         color=color[4])
+plt.text(number_hours / 2 - 2.8, max_power_total + 1, 'max net power',
+         color=color[4])
+plt.axhline(max_power, ls='-.', lw=1.75,
+            color=color[4])
+plt.axhline(max_power_total, ls=':', lw=1.75,
+            color=color[4])
+ax3.step(hours_list, power_list,
+         marker='o', ms=4,
+         lw=1, color=color[1])
+ax3.tick_params(axis='y',
+                labelcolor=color[1])
+ax3.set_xticks(np.arange(0, n_time_points*n_weeks_to_plot + 1, step=2))
+
+ax4 = ax3.twinx()
+ax4.set_ylabel('LMP ($/MWh)',
+               color=color[2])
+ax4.step([x + 1 for x in hours], lmp_array,
+         marker='o', ms=3, alpha=0.5,
+         ls='-', lw=1,
+         color=color[2])
+ax4.tick_params(axis='y',
+                labelcolor=color[2])
+plt.savefig('multiperiod_usc_storage_new_area_rts1_power_{}h.png'.format(hours_per_day))
+
+
+zero_point = True
+hxc_array = np.asarray(hxc_duty[0:n_weeks_to_plot]).flatten()
+hxd_array = np.asarray(hxd_duty[0:n_weeks_to_plot]).flatten()
+hxc_duty0 = 0 # zero since the plant is not operating
+hxc_duty_list = [hxc_duty0] + hxc_array.tolist()
+hxd_duty0 = 0 # zero since the plant is not operating
+hxd_duty_list = [hxd_duty0] + hxd_array.tolist()
+
+fig3, ax5 = plt.subplots(figsize=(12, 8))
+ax5.set_xlabel('Time Period (hr)')
+ax5.set_ylabel('Storage Heat Duty (MW)',
+               color=color[3])
+ax5.spines["top"].set_visible(False)
+ax5.spines["right"].set_visible(False)
+ax5.grid(linestyle=':', which='both',
+         color='gray', alpha=0.30)
+plt.text(number_hours / 2 - 2.2, max_storage_heat_duty + 1, 'max storage',
+         color=color[4])
+plt.text(number_hours / 2 - 2, min_storage_heat_duty - 6.5, 'min storage',
+         color=color[4])
+plt.axhline(max_storage_heat_duty, ls=':', lw=1.75,
+            color=color[4])
+plt.axhline(min_storage_heat_duty, ls=':', lw=1.75,
+            color=color[4])
+if zero_point:
+    ax5.step(hours_list, hxc_duty_list,
+             marker='^', ms=4, label='Charge',
+             color=color[0])
+    ax5.step(hours_list, hxd_duty_list,
+             marker='v', ms=4, label='Discharge',
+             color=color[1])
+else:
+    ax5.step([x + 1 for x in hours], hxc_array,
+             marker='^', ms=4, lw=1,
+             label='Charge',
+             color=color[0])
+    ax5.step([x + 1 for x in hours], hxd_array,
+             marker='v', ms=4, lw=1,
+             label='Discharge',
+             color=color[1])
+ax5.legend(loc="center right", frameon=False)
+ax5.tick_params(axis='y',
+                labelcolor=color[3])
+ax5.set_xticks(np.arange(0, n_time_points*n_weeks_to_plot + 1, step=2))
+
+ax6 = ax5.twinx()
+ax6.set_ylabel('LMP ($/MWh)',
+               color=color[2])
+ax6.step([x + 1 for x in hours], lmp_array,
+         marker='o', ms=3, alpha=0.5,
+         ls='-', color=color[2])
+ax6.tick_params(axis='y',
+                labelcolor=color[2])
+plt.savefig('multiperiod_usc_storage_new_area_rts1_hxduty_{}h.png'.format(hours_per_day))
+
+plt.show()
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/plot_doubleloop_results.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/plot_doubleloop_results.py
new file mode 100644
index 000000000..a9a212b18
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/plot_doubleloop_results.py
@@ -0,0 +1,215 @@
+#################################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2022 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+#################################################################################
+
+import os
+from types import ModuleType
+import pandas as pd
+from importlib import resources
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MultipleLocator, FormatStrFormatter, AutoMinorLocator
+try:
+    from importlib import resources  # Python 3.8+
+except ImportError:
+    import importlib_resources as resources  # Python 3.7
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant import storage
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage.double_loop import bidding_plugin_test_multiperiod_rankine
+
+# result_dir = 'bidding_plugin_test_multiperiod_rankine3d2s_0420'
+
+
+
+# comparison_result_dir = "no_plugin_result"
+# result_dir = "double_loop_plugin_multiperiod_wind_battery"
+# rts_dir = "/home/xgao1/DowlingLab/RTS-GMLC/RTS_Data/SourceData"
+
+fossil_gen = "102_STEAM_3"
+bus = 102
+
+MAJOR_TICK_SIZE = "xx-large"
+MINOR_TICK_SIZE = "xx-large"
+LABEL_SIZE = "xx-large"
+LEGEND_SIZE = "xx-large"
+TITLE_SIZE = "xx-large"
+
+# tracker_df = pd.read_csv(os.path.join(result_dir, "tracking_model_detail_new.csv"))
+# tracker_df = pd.read_csv(os.path.join("tracking_results.csv"))
+# tracker_df = tracker_df.loc[tracker_df["Horizon [hr]"]==0]
+# tracker_df["Time Index"] = range(len(tracker_df))
+# tracker_df
+
+# fig, ax = plt.subplots(figsize=(10,5))
+# tracker_df.plot(x="Time Index", y="Power Dispatch [MW]", ax=ax, label='RT Dispatches')
+# tracker_df.plot(x="Time Index", y="Power Output [MW]", ax=ax, label='Power Output')
+
+# ax.set_xlabel("Time [hr]", fontsize=LABEL_SIZE)
+# ax.set_ylabel("Power Output [MW]", fontsize=LABEL_SIZE)
+# ax.xaxis.set_minor_locator(MultipleLocator(4))
+# ax.xaxis.set_major_locator(MultipleLocator(24))
+
+# ax.tick_params(axis='both', which='major', labelsize=MAJOR_TICK_SIZE)
+# ax.tick_params(axis='both', which='minor', labelsize=MINOR_TICK_SIZE)
+
+# plt.rc('legend', fontsize = LEGEND_SIZE)
+# plt.grid(False)
+# plt.savefig('dispatch_results_3day_0413.png')
+
+# ax.grid()
+# ax.set_title(f"{fossil_gen} Plugin Power Output", fontsize=TITLE_SIZE)
+with resources.path(storage, "tracking_results.csv") as data_file_path:
+    assert data_file_path.is_file()
+    tracking_model_df = pd.read_csv(str(data_file_path))
+# with resources.path(bidding_plugin_test_multiperiod_rankine, "tracker_detail.csv") as data_file_path2:
+#     assert data_file_path2.is_file()
+#     tracking_model_df2 = pd.read_csv(str(data_file_path2))
+
+# tracking_model_df = pd.read_csv(os.path.join(result_dir, "tracking_model_detail_new.csv"))
+tracking_model_df = tracking_model_df.loc[tracking_model_df["Horizon [hr]"]==0]
+# tracking_model_df2 = tracking_model_df2.loc[tracking_model_df2["Horizon [hr]"]==0]
+tracking_model_df["Time Index"] = range(len(tracking_model_df))
+# tracking_model_df
+tracking_model_df["Storage Tank Level [%]"] = tracking_model_df["Hot Tank Level [MT]"]/67392.92
+tracking_model_df["Boiler Duty [MWth]"] = tracking_model_df["Plant Heat Duty [MWth]"]
+
+fig, ax = plt.subplots(figsize=(12, 6))
+ax2 = ax.twinx()
+cols = ["Plant Power [MW]", "Storage Power [MW]", "Time Index"]
+ylabels = ["Plant Power [MW]", "Storage Power [MW]"]
+tracking_model_df[cols].plot(x="Time Index", kind='bar', width=1, stacked=True, ax=ax, label=ylabels, ylim=(0, 600))
+# tracking_model_df[cols].plot.area(x="Time Index", ax=ax)
+# tracking_model_df.plot(x="Time Index", y="Power Dispatch [MW]", drawstyle="steps-mid",ax=ax, label='RT Dispatches [MWe]', color="black", ylim=(0, 600))
+# plt.savefig('tracking_dispatch_results_3day.png')
+# plt.show()
+
+tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, label='Storage Level [%]', color="red", ylim=(0, 600))
+tracking_model_df.plot(x="Time Index", y="Boiler Duty [MWth]", ax=ax, label='Boiler Duty [$MW_{th}$]', color="pink", ylim=(0, 600))
+# legend1 = pyplot.legend(plot_lines[0], ["algo1", "algo2", "algo3"], loc=1)
+# tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, color="red")
+# tracking_model_df.plot(x="Time Index", y="Power Prices [$/MWh]", ax=ax2, label='LMP [$/MWh]', color="gold", ylim=(0, 40))
+# legend1 = plt.legend(fontsize=LEGEND_SIZE, ncol=2, loc = "upper left", bbox_to_anchor=(0.7,1.2))
+# tracker_df.legend(fontsize=LEGEND_SIZE, ncol=2, loc = "upper left", bbox_to_anchor=(0.7,1.2))
+ax.set_xlabel("Time [hr]", fontsize=LABEL_SIZE)
+# ax.set_ylabel("Power [MW]", fontsize=LABEL_SIZE)
+ax2.set_ylabel("LMP [$/MWh]", fontsize=LABEL_SIZE)
+ax.xaxis.set_minor_locator(MultipleLocator(4))
+ax.xaxis.set_major_locator(MultipleLocator(24))
+ax2.xaxis.set_minor_locator(MultipleLocator(4))
+ax2.xaxis.set_major_locator(MultipleLocator(24))
+ax.legend(fontsize=LEGEND_SIZE, ncol=2, loc = "upper left", bbox_to_anchor=(0.26,1.3))
+
+ax.tick_params(axis='both', which='major', labelsize=MAJOR_TICK_SIZE)
+ax.tick_params(axis='both', which='minor', labelsize=MINOR_TICK_SIZE)
+ax2.tick_params(axis='both', which='minor', labelsize=MINOR_TICK_SIZE)
+ax2.tick_params(axis='both', which='major', labelsize=MAJOR_TICK_SIZE)
+
+# ax.grid()
+# plt.ylim([0, 500])
+# plt.grid(False)
+plt.legend(fontsize=LEGEND_SIZE, ncol=2, loc = "upper left", bbox_to_anchor=(-0.01,1.2))
+plt.savefig('doubleloop_standard_0422_3d.png')
+plt.show()
+
+fig, ax = plt.subplots(figsize=(10, 5))
+ax2 = ax.twinx()
+# cols = ["Plant Power [MW]", "Storage Power [MW]", "Time Index"]
+# tracking_model_df[cols].plot(x="Time Index", kind='bar', width=1, stacked=True, ax=ax)
+# tracking_model_df[cols].plot.area(x="Time Index", ax=ax)
+# tracker_df.plot(x="Time Index", y="Power Dispatch [MW]", drawstyle="steps-mid",ax=ax, label='RT Dispatches', color="black")
+# plt.savefig('tracking_dispatch_results_3day.png')
+# plt.show()
+# tracking_model_df.plot(x="Time Index", y="Power Prices [$/MWh]", ax=ax, label='LMP [$/MWh]', color="green", ylim=(0,40))
+# tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, label='Tank Level [%]', color="red")
+tracking_model_df.plot(x="Time Index", y="Hot Tank Level [MT]", drawstyle="steps-mid",ax=ax2, label='Tank Level [MT]', color="red", ylim=(0,3e6))
+# 
+# tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, color="red")
+# tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, color="red")
+
+ax.set_xlabel("Time [hr]", fontsize=LABEL_SIZE)
+# ax.set_ylabel("LMP [$/MWh]", fontsize=LABEL_SIZE)
+ax2.set_ylabel("Salt Level [MT]", fontsize=LABEL_SIZE)
+ax.xaxis.set_minor_locator(MultipleLocator(4))
+ax.xaxis.set_major_locator(MultipleLocator(24))
+
+ax.tick_params(axis='both', which='major', labelsize=MAJOR_TICK_SIZE)
+ax.tick_params(axis='both', which='minor', labelsize=MINOR_TICK_SIZE)
+
+# ax.grid()
+# plt.legend(fontsize=LEGEND_SIZE, ncol=2, loc = "upper center", bbox_to_anchor=(0.5,1.35))
+# plt.ylim([0, 500])
+# plt.grid(False)
+plt.savefig('LMP_SaltLevel_0422_3d.png')
+plt.show()
+# ****************************************************
+
+# ax.set_title(f"{fossil_gen} Plugin Tracking Power States", fontsize=TITLE_SIZE)
+
+# bidding_model_df = pd.read_csv(os.path.join(result_dir, "bidding_model_detail_3day.csv"))
+# bidding_model_df = bidding_model_df.loc[bidding_model_df["Horizon [hr]"]<24]
+# bidding_model_df["Time Index"] = range(len(bidding_model_df))
+# #bidding_model_df
+
+# fig, ax = plt.subplots(figsize=(10, 5))
+# cols = ["Plant Power [MW]", "Storage Power [MW]", "Time Index"]
+# bidding_model_df[cols].plot(x="Time Index", kind='bar', width=1, stacked=True, ax=ax)
+# bidding_model_df[cols].plot.area(x="Time Index", ax=ax)
+# # tracker_df.plot(x="Time Index", y="Power Dispatch [MW]", ax=ax, label='RT Dispatches', color="black")
+# # bidding_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, color="red")
+# bidding_model_df.plot(x="Time Index", y="Total Power Output [MW]", drawstyle="steps-mid", ax=ax, color="black")
+
+# ax.set_xlabel("Time [hr]", fontsize=LABEL_SIZE)
+# ax.set_ylabel("Power [MW]", fontsize=LABEL_SIZE)
+# ax.xaxis.set_minor_locator(MultipleLocator(4))
+# ax.xaxis.set_major_locator(MultipleLocator(24))
+
+# ax.tick_params(axis='both', which='major', labelsize=MAJOR_TICK_SIZE)
+# ax.tick_params(axis='both', which='minor', labelsize=MINOR_TICK_SIZE)
+
+# plt.rc('legend', fontsize = LEGEND_SIZE)
+
+# ax.grid()
+# ax.set_title(f"{fossil_gen} Plugin Bidding Power States", fontsize=TITLE_SIZE)
+
+fig, ax = plt.subplots(figsize=(10, 5))
+ax2 = ax.twinx()
+# cols = ["Plant Power [MW]", "Storage Power [MW]", "Time Index"]
+# tracking_model_df[cols].plot(x="Time Index", kind='bar', width=1, stacked=True, ax=ax)
+# tracking_model_df[cols].plot.area(x="Time Index", ax=ax)
+# tracker_df.plot(x="Time Index", y="Power Dispatch [MW]", drawstyle="steps-mid",ax=ax, label='RT Dispatches', color="black")
+# plt.savefig('tracking_dispatch_results_3day.png')
+# plt.show()
+# tracking_model_df.plot(x="Time Index", y="Power Prices [$/MWh]", ax=ax, label='LMP [$/MWh]', color="green", ylim=(0,40))
+tracking_model_df.plot(x="Time Index", y="HXC Duty", drawstyle="steps-mid", ax=ax2, label='Charge Heat Exchanger [$MW_{th}$]', color="red", ylim=(0,200))
+tracking_model_df.plot(x="Time Index", y="HXD Duty", drawstyle="steps-mid",ax=ax2, label='Discharge Heat Exchanger [$MW_{th}$]', color="blue", ylim=(0,200))
+
+ax.legend(loc = "upper left", bbox_to_anchor=(0.01,1))
+
+# tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, color="red")
+# tracking_model_df.plot(x="Time Index", y="Storage Tank Level [%]", drawstyle="steps-mid", ax=ax, color="red")
+
+ax.set_xlabel("Time [hr]", fontsize=LABEL_SIZE)
+# ax.set_ylabel("LMP [$/MWh]", fontsize=LABEL_SIZE)
+ax2.set_ylabel("Heat Duty [$MW_{th}$]", fontsize=LABEL_SIZE)
+ax.xaxis.set_minor_locator(MultipleLocator(4))
+ax.xaxis.set_major_locator(MultipleLocator(24))
+
+ax.tick_params(axis='both', which='major', labelsize=MAJOR_TICK_SIZE)
+ax.tick_params(axis='both', which='minor', labelsize=MINOR_TICK_SIZE)
+
+# ax.grid()
+# plt.legend(fontsize=LEGEND_SIZE, ncol=2, loc = "upper center", bbox_to_anchor=(0.5,1.35))
+# plt.ylim([0, 500])
+# plt.grid(False)
+plt.savefig('hx_duties_0422_3d.png')
+plt.show()
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/plugin_double_loop_usc.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/plugin_double_loop_usc.py
new file mode 100644
index 000000000..89a54c864
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/plugin_double_loop_usc.py
@@ -0,0 +1,133 @@
+#################################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2022 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+#################################################################################
+
+# custom plugin file for running the rankine cycle with a battery as a
+# multi-period model in the Prescient double-loop
+import pickle
+import pandas as pd
+import pyomo.environ as pyo
+from pyomo.common.config import ConfigDict, ConfigValue
+from pyomo.common.fileutils import this_file_dir
+
+from idaes.apps.grid_integration.examples.utils import (
+    rts_gmlc_generator_dataframe,
+    rts_gmlc_bus_dataframe,
+    prescient_5bus,
+    daily_da_price_means,
+    daily_rt_price_means,
+    daily_da_price_stds,
+    daily_rt_price_stds,
+)
+
+from idaes.apps.grid_integration import Tracker
+from idaes.apps.grid_integration import Bidder
+from idaes.apps.grid_integration import PlaceHolderForecaster
+from idaes.apps.grid_integration import DoubleLoopCoordinator
+
+from multiperiod_double_loop_usc import MultiPeriodUsc
+from idaes.apps.grid_integration.model_data import GeneratorModelData
+from idaes.apps.grid_integration.model_data import (
+    GeneratorModelData as _UpstreamGeneratorModelData,
+    RealValueValidator,
+    AtLeastPminValidator
+)
+import os.path
+
+# with open("usc_gen_data.pkl", "rb") as f:
+#     gen_data = pickle.load(f)
+
+generator_data = {
+    "gen_name": "102_STEAM_3",
+    "bus": "Carter",
+    "p_min": 286,
+    "p_max": 460,
+}
+model_data = GeneratorModelData(**generator_data)
+
+# default_bid_curve = gen_data["Original Marginal Cost Curve"]
+# pmin = gen_data["PMin MW"]
+# pmax = gen_data["PMax MW"]
+# gen_name = gen_data["generator_name"]
+# raise Exception()
+tracking_horizon = 12  # hours
+bidding_horizon = 48  # hours
+day_ahead_horizon = 48  # hours
+real_time_horizon = 4  # hours
+n_scenario = 2  # for bidding
+n_tracking_hour = 1  # advance n_tracking_hour (i.e. assume we solve every hour)
+
+# create forecasterprice_forecasts_df = pd.read_csv(os.path.join(this_file_dir(), "C:\\grid\\source_code\\idaes-pse\\idaes\\apps\\grid_integration\\examples\\lmp_forecasts_concat.csv"))
+forecaster = PlaceHolderForecaster(
+    daily_da_price_means=daily_da_price_means,
+    daily_rt_price_means=daily_rt_price_means,
+    daily_da_price_stds=daily_da_price_stds,
+    daily_rt_price_stds=daily_rt_price_stds,
+)
+# create solver
+solver = pyo.SolverFactory("ipopt")
+solver.options = {
+    "max_iter": 200,
+}
+
+
+# Setup trackers, bidder, and coordinator
+#################################################################################
+# Tracker
+mp_usc_tracker = MultiPeriodUsc(
+    model_data=model_data
+)
+
+thermal_tracker = Tracker(
+    tracking_model_object=mp_usc_tracker,
+    tracking_horizon=tracking_horizon,
+    n_tracking_hour=n_tracking_hour,
+    solver=solver,
+)
+
+# Projection Tracker
+mp_usc_projection_tracker = MultiPeriodUsc(
+    model_data=model_data
+)
+
+thermal_projection_tracker = Tracker(
+    tracking_model_object=mp_usc_projection_tracker,
+    tracking_horizon=tracking_horizon,
+    n_tracking_hour=n_tracking_hour,
+    solver=solver,
+)
+
+# Bidder
+mp_usc_bidder = MultiPeriodUsc(
+    model_data=model_data
+)
+thermal_bidder = Bidder(
+    bidding_model_object=mp_usc_bidder,
+    day_ahead_horizon=day_ahead_horizon,
+    real_time_horizon=real_time_horizon,
+    n_scenario=n_scenario,
+    solver=solver,
+    forecaster=forecaster,
+)
+
+# Coordinator
+coordinator = DoubleLoopCoordinator(
+    bidder=thermal_bidder,
+    tracker=thermal_tracker,
+    projection_tracker=thermal_projection_tracker,
+)
+
+## Prescient requires the following functions in this module
+get_configuration = coordinator.get_configuration
+register_plugins = coordinator.register_plugins
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/run_double_loop_usc.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/run_double_loop_usc.py
new file mode 100644
index 000000000..d0941cffc
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/run_double_loop_usc.py
@@ -0,0 +1,197 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2021 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+"""
+This script uses the multiperiod object for the integrated fossil case study
+to set up and run the double-loop framework.
+"""
+
+__author__ = "Naresh Susarla"
+
+import os
+from types import ModuleType
+import pandas as pd
+from importlib import resources
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MultipleLocator, FormatStrFormatter, AutoMinorLocator
+try:
+    from importlib import resources  # Python 3.8+
+except ImportError:
+    import importlib_resources as resources  # Python 3.7
+
+# Import Pyomo packages
+import pyomo.environ as pyo
+
+# Import IDAES packages
+from idaes.apps.grid_integration import Tracker
+from idaes.apps.grid_integration import Bidder
+from idaes.apps.grid_integration import PlaceHolderForecaster
+from idaes.apps.grid_integration import DoubleLoopCoordinator
+# from idaes.apps.grid_integration.model_data import GeneratorModelData
+from idaes.apps.grid_integration.model_data import (
+    GeneratorModelData as _UpstreamGeneratorModelData,
+    RealValueValidator,
+    AtLeastPminValidator
+)
+from idaes.apps.grid_integration.examples.utils import (
+    rts_gmlc_generator_dataframe,
+    rts_gmlc_bus_dataframe,
+    prescient_5bus,
+    daily_da_price_means,
+    daily_rt_price_means,
+    daily_da_price_stds,
+    daily_rt_price_stds,
+)
+
+# Import Prescient simulator
+from prescient.simulator import Prescient
+
+# Import integrated ultra-supercritical power plant with energy storage model
+from dispatches_sample_data import rts_gmlc
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage import multiperiod_integrated_storage_usc
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage.multiperiod_double_loop_usc import MultiPeriodUsc
+
+class GeneratorModelData(_UpstreamGeneratorModelData):
+    p_min_agc = RealValueValidator(min_val=0)
+    p_max_agc = AtLeastPminValidator()
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.p_min_agc = self.p_min
+        self.p_max_agc = self.p_max
+
+generator_data = {
+    "gen_name": "102_STEAM_3",
+    "bus": "Carter",
+    "p_min": 286,
+    "p_max": 460,
+}
+model_data = GeneratorModelData(**generator_data)
+
+tracking_horizon = 4  # hours
+bidding_horizon = 48  # hours
+day_ahead_horizon = 48  # hours
+real_time_horizon = 4  # hours
+n_scenario = 2  # for bidding
+n_tracking_hour = 1  # advance n_tracking_hour (i.e. assume we solve every hour)
+num_days = 2
+
+forecaster = PlaceHolderForecaster(
+    daily_da_price_means=daily_da_price_means,
+    daily_rt_price_means=daily_rt_price_means,
+    daily_da_price_stds=daily_da_price_stds,
+    daily_rt_price_stds=daily_rt_price_stds,
+)
+
+# create solver
+solver = pyo.SolverFactory("ipopt")
+solver.options = {
+    "max_iter": 200,
+}
+
+
+# Setup trackers, bidder, and coordinator
+#################################################################################
+# Tracker
+mp_usc_tracker = MultiPeriodUsc(
+    model_data=model_data
+)
+
+thermal_tracker = Tracker(
+    tracking_model_object=mp_usc_tracker,
+    tracking_horizon=tracking_horizon,
+    n_tracking_hour=n_tracking_hour,
+    solver=solver,
+)
+
+# Projection Tracker
+mp_usc_projection_tracker = MultiPeriodUsc(
+    model_data=model_data
+)
+
+thermal_projection_tracker = Tracker(
+    tracking_model_object=mp_usc_projection_tracker,
+    tracking_horizon=tracking_horizon,
+    n_tracking_hour=n_tracking_hour,
+    solver=solver,
+)
+
+# Bidder
+mp_usc_bidder = MultiPeriodUsc(
+    model_data=model_data
+)
+thermal_bidder = Bidder(
+    bidding_model_object=mp_usc_bidder,
+    day_ahead_horizon=day_ahead_horizon,
+    real_time_horizon=real_time_horizon,
+    n_scenario=n_scenario,
+    solver=solver,
+    forecaster=forecaster,
+)
+
+# Coordinator
+coordinator = DoubleLoopCoordinator(
+    bidder=thermal_bidder,
+    tracker=thermal_tracker,
+    projection_tracker=thermal_projection_tracker,
+)
+
+
+class PrescientPluginModule(ModuleType):
+    def __init__(self, get_configuration, register_plugins):
+        self.get_configuration = get_configuration
+        self.register_plugins = register_plugins
+
+
+plugin_module = PrescientPluginModule(
+    get_configuration=coordinator.get_configuration,
+    register_plugins=coordinator.register_plugins,
+)
+
+
+# If installing the dispatches-sample-data
+rts_gmlc_data_dir = str(rts_gmlc.source_data_path)
+# rts_gmlc_data_dir = "C:\\grid\\source_code\\Prescient\\downloads\\rts_gmlc\\RTS-GMLC\\RTS_Data\\SourceData"
+
+options = {
+    "data_path": rts_gmlc_data_dir,
+    "input_format": "rts-gmlc",
+    "simulate_out_of_sample": True,
+    "run_sced_with_persistent_forecast_errors": True,
+    "output_directory": "bidding_multiperiod_usc",
+    "start_date": "2020-07-10",
+    "num_days": num_days,
+    "sced_horizon": tracking_horizon,
+    "ruc_horizon": bidding_horizon,
+    "compute_market_settlements": True,
+    "day_ahead_pricing": "LMP",
+    "ruc_mipgap": 0.05,
+    "symbolic_solver_labels": True,
+    "reserve_factor": 0.0,
+    "deterministic_ruc_solver": "gurobi",
+    "output_ruc_solutions": True,
+    "sced_solver": "gurobi",
+    "print_sced": True,
+    "enforce_sced_shutdown_ramprate": True,
+    "plugin": {
+        "doubleloop": {
+            "module": plugin_module,
+            "bidding_generator": "102_STEAM_3",
+        }
+    },
+}
+
+Prescient().simulate(**options)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/run_prescient_double_loop_usc.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/run_prescient_double_loop_usc.py
new file mode 100644
index 000000000..814c7a1ea
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/run_prescient_double_loop_usc.py
@@ -0,0 +1,105 @@
+#################################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2022 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+#################################################################################
+
+from prescient.simulator import Prescient
+
+#point to rts_gmlc scenario data
+print("                      ")
+print("------Start Prescient Simulation-------")
+print("                      ")
+# raise Exception()
+# rts_gmlc_data_dir = "C:\\grid\\source_code\\Prescient\\downloads\\rts_gmlc\\deterministic_with_network_scenarios"
+rts_gmlc_data_dir = "C:\\grid\\source_code\\Prescient\\downloads\\rts_gmlc\\RTS-GMLC\\RTS_Data\\SourceData"
+
+options = {
+    "data_path": rts_gmlc_data_dir,
+    "input_format": "rts-gmlc",
+    "simulate_out_of_sample": True,
+    "run_sced_with_persistent_forecast_errors": True,
+    "output_directory": "bidding_plugin_test_multiperiod_rankine",
+    "start_date": "2020-07-10",
+    "num_days": 1,
+    "sced_horizon": 12,
+    "ruc_horizon": 48,
+    "compute_market_settlements": True,
+    "day_ahead_pricing": "LMP",
+    "ruc_mipgap": 0.01,
+    "symbolic_solver_labels": True,
+    "reserve_factor": 0.0,
+    "deterministic_ruc_solver": "gurobi",
+    "output_ruc_solutions": True,
+    "sced_solver": "gurobi",
+    "print_sced": True,
+    # "output_sced_loads": True,
+    "enforce_sced_shutdown_ramprate": True,
+    "plugin": {
+        "doubleloop": {
+            "module": "plugin_double_loop_usc.py",
+            "bidding_generator": "102_STEAM_3",
+        }
+    },
+}
+
+Prescient().simulate(**options)
+
+# # plot every bid curve over 96 hours
+# from mpl_toolkits.mplot3d import Axes3D
+# from matplotlib import cm
+# costs = bid_profiles_sorted.iloc[:,0:6].to_numpy()
+# powers = bid_profiles_sorted.iloc[:,6:-1].to_numpy()
+# costs = np.array([costs[i][~np.isnan(costs[i])] for i in range(len(costs))])
+# powers = np.array([powers[i][~np.isnan(powers[i])] for i in range(len(powers))])
+# times = list(range(len(costs)))
+
+# plt.figure(figsize = (8,8))
+# t = np.arange(len(times))
+# ax = plt.subplot(projection='3d')
+# for t in times:
+#     costs_plt = costs[t]
+#     powers_plt = powers[t]
+#     ax.plot(np.ones(len(costs_plt))*t, powers_plt,costs_plt,color="black")
+# ax.set_xlabel("Hour")
+# ax.set_ylabel("\nPower [MW]")
+# ax.set_zlabel("\n Cost [$/MWh]",linespacing=3.0)
+# plt.tight_layout()
+
+
+
+# rts_gmlc_data_dir = "/home/xgao1/DowlingLab/RTS-GMLC/RTS_Data/SourceData"
+# options = {
+#     ​​​​​​"data_path": rts_gmlc_data_dir,
+#     "input_format":"rts-gmlc",
+#     "simulate_out_of_sample": True,
+#     "run_sced_with_persistent_forecast_errors": True,
+#     "output_directory": "bidding_plugin_test_multiperiod_rankine",
+#     "start_date": "01-02-2020",
+#     "num_days": 1,
+#     "sced_horizon": 4,
+#     "ruc_horizon": 48,
+#     "compute_market_settlements": True,
+#     "day_ahead_pricing": "LMP",
+#     "ruc_mipgap": 0.05,
+#     "symbolic_solver_labels": True,
+#     "reserve_factor": 0.0,
+#     "deterministic_ruc_solver": "gurobi",
+#     "sced_solver": "gurobi",
+#     "plugin": {​​​​​​
+#                "doubleloop": {​​​​​​
+#                               "module": "plugin_wind_battery_doubleloop.py",
+#                               "bidding_generator": "309_WIND_1",
+#                               }​​​​​​
+#                }​​​​​​,
+#     }​​​​​​
+
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/usc_gen_data.pkl b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/usc_gen_data.pkl
new file mode 100644
index 000000000..ffd052b24
Binary files /dev/null and b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/double_loop/usc_gen_data.pkl differ
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/CO2_capture.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/CO2_capture.py
new file mode 100644
index 000000000..eeed3d2fd
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/CO2_capture.py
@@ -0,0 +1,506 @@
+##############################################################################
+# Institute for the Design of Advanced Energy Systems Process Systems
+# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2020, by the
+# software owners: The Regents of the University of California, through
+# Lawrence Berkeley National Laboratory,  National Technology & Engineering
+# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia
+# University Research Corporation, et al. All rights reserved.
+#
+# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and
+# license information, respectively. Both files are also available online
+# at the URL "https://github.com/IDAES/idaes-pse".
+##############################################################################
+"""
+IDAES carbon capture system block, hybrid mass balances and surrogate models
+
+The unit has 1 inlet stream for fluegas: inlet
+The unit has 2 outlet streams: pureCO2 and exhaust_gas
+
+The state variables are component flow mol, temperature, and pressure.
+The state variables can be accessed thotough ports named:
+
+* inlet - flue gas from NGCC plant
+* pure_CO2 - stream for compression train
+* exhaust_gas - exhaust gas to DAC system or stack
+
+Surrogate models are used to compute solvent flow rate and specific reboiler duty.
+The inlet variables to estimate such output variables are: lean loading and PZ molality.
+
+Inlet Vars:
+
+* flue gas (component molar flowrate [H20, CO2, O2, N2], temperature, pressure)
+* solvent lean loading (0.1 to 0.6, mol CO2/mol PZ)
+* solvent molality (PZ = 3, 5, 7 mol)
+
+Output Vars:
+
+* specific reboiler duty (GJ/t CO2 or MJ/kg CO2)
+* lean solvent flowrate in kmol/s (0-100)
+* L/G ratio for absorber columns (0 - 12) (only for Surrogate.PZ_ref1_90_capture)
+* exhaust_gas (component molar flowrate [H20, CO2, O2, N2], temperature, pressure)
+* pureCO2 (component molar flowrate [H20, CO2, O2, N2], temperature, pressure)
+
+Surrogate models, the team explored different surrogate models. We developed
+different models using commercial software for running simulations for 90% CO2 Capture,
+95 % CO2 capture, and 97% CO2 Capture. Then, we provide data from a public source (ref 1)
+and finally, we develop a simple correction factor to provide data to train data for 5 molal PZAS technology.
+The models available are:
+
+* PZ_90_capture: Conventional plant configuration to capture 90% capture
+* PZ_95_capture: Conventional plant configuration to capture 95% capture
+* PZ_97_capture: Conventional plant configuration to capture 97% capture
+* PZ_90to97_capture: Conventional plant configuration to between 90 to 97% capture (using data obtained for previous surrogates)
+* rel_PZAS_5mol_capture: using results for 5 molal PZ_90_capture applied a correction factor for SRD (using FEED studies).
+* PZ_ref1_90_capture: Literature review, data obtained for reference 1 (see below).
+
+Users can select any of these models while constructing the models, see examples -->
+idaes/power_generation/flowsheets/ngcc/ngcc_withCCS.py
+and/or idaes/power_generation/carbon_capture/piperazine_surrogates/tests/test_co2capture.py
+
+Reference 1 (public data used to train surrogate models = PZ_ref1_90_capture):
+Gaspar J. von Solms N., Thomsen K., Fosbol F. (2016) Multivariable Optimization
+ of the Piperazine CO2 Post-Combustion Process. Energy Procedia 86(2016)229-238
+"""
+# Import IDAES cores
+from idaes.core import declare_process_block_class, UnitModelBlockData, useDefault
+import idaes.core.util.scaling as iscale
+# Additional import for the unit operation
+import pyomo.environ as pyo
+from pyomo.environ import Var, units as pyunits, Constraint, exp, log
+from pyomo.network import Port
+import idaes.logger as idaeslog
+# import gen_sm as ifit
+from pyomo.common.config import ConfigBlock, ConfigValue, In
+from enum import Enum
+from idaes.power_generation.carbon_capture.piperazine_surrogates import L_G_ratio  # surrogate model for L_G_ratio
+
+__author__ = "M. Zamarripa"
+__version__ = "1.0.0"
+
+class Surrogates(Enum):
+    PZ_90_capture = 0
+    PZ_95_capture = 1
+    PZ_97_capture = 2
+    PZ_90to97_capture = 3
+    rel_PZAS_5mol_capture = 4
+    PZ_ref1_90_capture = 5
+    MEA = 6
+
+class Technology(Enum):
+    ngcc = 0  # natural gas combined cycle
+    scpc = 1  # supercritical coal fired power plant
+
+# ----------------------------------------------------------------------------
+@declare_process_block_class("CO2Capture")
+class CO2CaptureData(UnitModelBlockData):
+    '''
+    CO2Capture surrogate model based on total flow of CO2 rich stream flow only
+    Assumptions: (toDo: update Assumptions)
+    Fixed composition, temperature, and pressure of feed stream
+    Fixed CO2 purity in the CO2 product stream
+    '''
+    CONFIG = ConfigBlock()
+    CONFIG.declare("dynamic", ConfigValue(
+            domain=In([useDefault, True, False]),
+            default=useDefault,
+            description="Dynamic model flag",
+            doc="""Indicates whether this model will be dynamic or not,
+    **default** = useDefault.
+    **Valid values:** {
+    **useDefault** - get flag from parent (default = False),
+    **True** - set as a dynamic model,
+    **False** - set as a steady-state model.}"""))
+    CONFIG.declare("has_holdup", ConfigValue(
+            default=False,
+            domain=In([True, False]),
+            description="Holdup construction flag",
+            doc="""Indicates whether holdup terms should be constructed or not.
+    Must be True if dynamic = True,
+    **default** - False.
+    **Valid values:** {
+    **True** - construct holdup terms,
+    **False** - do not construct holdup terms}"""))
+    CONFIG.declare("CO2_capture_rate_surrogates", ConfigValue(
+        default=Surrogates.PZ_90_capture,
+        domain=In(Surrogates),
+        description='surrogate models to be used in SRD eqn',
+        doc='4 surrogates have been used, 90, 95, 97, and 90to97'))
+    CONFIG.declare("flue_gas_source", ConfigValue(
+        default=Technology.ngcc,
+        domain=In(Technology),
+        description='surrogate models to be used in SRD eqn',
+        doc='4 surrogates have been used, 90, 95, 97, and 90to97'))
+
+
+    def build(self):
+        # Call UnitModel.build to setup dynamics
+        super().build()
+
+        self.component_list = ['CO2', 'H2O', 'O2', 'N2']
+
+        self.make_vars()
+        self.add_material_balances()
+
+        # Add ports: 3 (1 for inlet and 2 for outlets)
+        self.inlet = Port(noruleinit=True,
+                          doc="A port for co2 rich inlet stream")
+        self.pureCO2 = Port(noruleinit=True,
+                            doc="A port for pure CO2 outlet stream")
+        self.exhaust_gas = Port(noruleinit=True,
+                                doc="A port for vent gas outlet stream")
+
+        # Add state vars to the ports
+        # self.inlet.add(self.inlet_flow_mol, "flow_mol")
+        self.inlet.add(self.inlet_temperature, "temperature")
+        self.inlet.add(self.inlet_pressure, "pressure")
+        self.inlet.add(self.inlet_flow_mol_comp, "flow_mol_comp")
+
+        # self.pureco2.add(self.pureco2_flow_mol, "flow_mol")
+        self.pureCO2.add(self.pureCO2_temperature, "temperature")
+        self.pureCO2.add(self.pureCO2_pressure, "pressure")
+        self.pureCO2.add(self.pureCO2_flow_mol_comp, "flow_mol_comp")
+
+        # self.exhaust_gas.add(self.exhaust_gas_flow_mol, "flow_mol")
+        self.exhaust_gas.add(self.exhaust_gas_temperature, "temperature")
+        self.exhaust_gas.add(self.exhaust_gas_pressure, "pressure")
+        self.exhaust_gas.add(self.exhaust_gas_flow_mol_comp, "flow_mol_comp")
+
+    def make_vars(self):
+        '''
+        This section builds port vars (Fc, T, P), CO2 capture rate
+
+        '''
+
+        # units declaration for vars
+        flow_units = pyunits.mol/pyunits.s
+        pressure_units = pyunits.Pa
+        temperature_units = pyunits.K
+        heat_duty_units = pyunits.J/pyunits.s
+
+        # Component mole flows [mol/s]
+        self.inlet_flow_mol_comp = Var(
+            self.flowsheet().config.time,
+            self.component_list,
+            initialize=1400/len(self.component_list),
+            units=flow_units,
+            doc='Inlet stream: Component mole flow [mol/s]')
+
+        self.pureCO2_flow_mol_comp = Var(
+            self.flowsheet().config.time,
+            self.component_list,
+            initialize=1200/len(self.component_list),
+            units=flow_units,
+            doc='PureCO2 stream: Component mole flow [mol/s]')
+
+        self.exhaust_gas_flow_mol_comp = Var(
+            self.flowsheet().config.time,
+            self.component_list,
+            initialize=100/len(self.component_list),
+            units=flow_units,
+            doc='exhaust_gas stream: Component mole flow [mol/s]')
+
+        # Temperature [K]
+        self.inlet_temperature = Var(self.flowsheet().config.time,
+                                     initialize=110,
+                                     units=temperature_units,
+                                     doc='Inlet temperature [K]')
+
+        self.pureCO2_temperature = Var(self.flowsheet().config.time,
+                                       initialize=110,
+                                       units=temperature_units,
+                                       doc='PureCO2 temperature [K]')
+
+        self.exhaust_gas_temperature = Var(self.flowsheet().config.time,
+                                           initialize=110,
+                                           units=temperature_units,
+                                           doc='exhaust_gas temperature [K]')
+
+        # Pressue [Pa]
+        self.inlet_pressure = Var(self.flowsheet().config.time,
+                                  initialize=17,
+                                  units=pressure_units,
+                                  doc='Inlet pressure [Pa]')
+
+        self.pureCO2_pressure = Var(self.flowsheet().config.time,
+                                    initialize=17,
+                                    units=pressure_units,
+                                    doc='PureCO2 pressure [Pa]')
+
+        self.exhaust_gas_pressure = Var(self.flowsheet().config.time,
+                                        initialize=17,
+                                        units=pressure_units,
+                                        doc='exhaust_gas pressure [Pa]')
+        # CO2 Capture rate
+        self.CO2_capture_rate = Var(self.flowsheet().config.time,
+                                    initialize=0.9,
+                                    doc='CO2 capture rate')
+        # lean loading
+        self.lean_loading = Var(self.flowsheet().config.time,
+                                initialize=0.1,
+                                bounds=(0.1, 6.0),
+                                doc='lean loading')
+        # Pz molality
+        self.Pz_mol = Var(self.flowsheet().config.time,
+                        # domain=pyo.Integers,
+                          initialize=3,
+                          bounds=(3, 7),
+                          doc='Pz molality')
+
+        self.SRD = Var(self.flowsheet().config.time,
+                       initialize=8,
+                       bounds=(0, 100),
+                       doc='Specific reboiler duty GJ/ton CO2 or MJ/kg CO2')
+
+    def add_material_balances(self):
+        ''' This section is for material balance constraints'''
+
+        # pureCO2 mass balance
+        @self.Constraint(self.flowsheet().config.time,
+                         self.component_list,
+                         doc="pureCO2 mass balances")
+        def pureCO2_eqn(b, t, c):
+            if c == "CO2":
+                return b.pureCO2_flow_mol_comp[t, c] == \
+                    b.inlet_flow_mol_comp[t, c] * b.CO2_capture_rate[t]
+            else:
+                return b.pureCO2_flow_mol_comp[t, c] == 0.0
+
+        # water drop in flue gas
+        @self.Expression(self.flowsheet().config.time, doc="water drop")
+        def water_drop(b, t):
+            return b.inlet_flow_mol_comp[0, 'H2O']*0.5
+
+        # Overall mass balances
+        @self.Constraint(self.flowsheet().config.time,
+                         self.component_list,
+                         doc="Inlet component mole flow eqn")
+        def flow_mol_comp_inlet_eqn(b, t, c):
+            if c == "H2O":
+                return b.inlet_flow_mol_comp[t, c] == \
+                    b.exhaust_gas_flow_mol_comp[t, c] + b.water_drop[t]
+            elif c == "CO2":
+                return b.inlet_flow_mol_comp[t, c] == \
+                    b.exhaust_gas_flow_mol_comp[t, c] \
+                    + b.pureCO2_flow_mol_comp[t, c]
+            else:
+                return b.inlet_flow_mol_comp[t, c] == \
+                    b.exhaust_gas_flow_mol_comp[t, c]
+
+        # Pressure equations
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Pressure drop")
+        def exh_pressure_eqn(b, t):
+            return b.inlet_pressure[t] == b.exhaust_gas_pressure[t]
+
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Pressure drop")
+        def pureCO2_pressure_eqn(b, t):
+            return b.inlet_pressure[t] == b.pureCO2_pressure[t]
+
+        # Temperature equations
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Temperature")
+        def pureCO2_temp_eqn(b, t):
+            return b.inlet_temperature[t] == b.pureCO2_temperature[t]
+
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Temperature")
+        def exh_temp_eqn(b, t):
+            return b.inlet_temperature[t] == b.exhaust_gas_temperature[t]
+
+
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Specific reboiler duty in GJ/t CO2 or MJ/ kg CO2")
+        def SRD_eqn(b, t):
+            CO2leanloading = b.lean_loading[t]
+            molality = b.Pz_mol[t]
+            Capture = b.CO2_capture_rate[t]
+            if b.config.flue_gas_source == Technology.ngcc:
+                if b.config.CO2_capture_rate_surrogates == Surrogates.PZ_90_capture:
+                    return b.SRD[t] == - 3.6233485374100991016633 * log(molality/4.) + 0.33347583941977870791717E-001 * (molality/4.)**3 + 6.8390136776297296705707 * (molality*CO2leanloading/4.)**0.5 - 5.7867174977020807702388 * (molality*CO2leanloading/4.)**2 + 5.1342721429236659602680 * (molality*CO2leanloading/4.)**3 + 0.19963960036749664461730 * (molality/CO2leanloading/4.)
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_95_capture:
+                    return b.SRD[t] == - 2.1195314593911489531308 * log(molality/4.) + 2.3838807672964539285942 * exp(CO2leanloading) + 0.17427301681432125213256 * (molality/4.)**2 + 0.12496410061822192660852 * (molality/CO2leanloading/4.) + 0.93458997890860340262975 * (CO2leanloading/molality/0.25)
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_97_capture:
+                    return b.SRD[t] == - 912.90813190561812007218 * CO2leanloading + 963.87626280230563224904 * exp(CO2leanloading) - 840.95461087585590576055 * (molality/2.)**2 - 614.38277546147764951456 * CO2leanloading**2 + 274.40532024830611135258 * (molality/2.)**3 - 0.61984641382653959951199 * (molality*CO2leanloading/2.)**3 + 0.26430328623580695568407 * (molality/CO2leanloading/2.) - 11.869291574687059309667 * (CO2leanloading/molality/0.5) - 0.23630953358941128236714E-002 * (molality/CO2leanloading/2.)**2
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_90to97_capture:
+                    return b.SRD[t] ==  - 0.10526756950236890175709 * Capture + 0.52057178809591142520929 * CO2leanloading + 0.98788205977359255793857 * molality + 2.0990364304677582296677 * Capture**0.5 + 3.5938248110847506033849 * CO2leanloading**0.5 - 6.3111812164050720141972 * molality**0.5 + 2.4029805693688151002618 * CO2leanloading**3 - 0.10937466124959506139080E-001 * Capture/molality - 41.496993880686794398116 * CO2leanloading/Capture + 0.36946342949766633467767E-001 * molality/CO2leanloading
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.rel_PZAS_5mol_capture:
+                    return (- 22.312295301354438947783 * CO2leanloading + 117.51384451486856619340 * CO2leanloading**2 - 269.05987870514132964672 * CO2leanloading**3 + 234.98153128275288281657 * CO2leanloading**4 + 3.9380879255782788028739)
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_ref1_90_capture:
+                    return b.SRD[t] == (- 15565.227507172588957474 * CO2leanloading
+                                        + 8.9237367058697429911263 * log(CO2leanloading)
+                                        + 15285.362653727905126289 * exp(CO2leanloading)
+                                        - 6741.0217492965430210461 * CO2leanloading**2
+                                        - 3865.5900513667870654899 * CO2leanloading**3
+                                        + 0.17312045830047528404555E-002 * molality**3
+                                        - 1.2960483459315448317994 * CO2leanloading*molality
+                                        - 15235.956076189686427824)
+                else:
+                    raise Exception('solvent is not supported for this technology')
+            elif b.config.flue_gas_source == Technology.scpc:
+                if b.config.CO2_capture_rate_surrogates == Surrogates.MEA:
+                    return b.SRD[t] == 1135585.36238969 * CO2leanloading - 440.509698856962 * log(CO2leanloading) - 1127184.48012032 * exp(CO2leanloading) + 533953.122356288 * CO2leanloading**2 + 248202.546339126 * CO2leanloading**3 + 1125580.12992624
+                else:
+                    raise Exception('solvent not supported for this technology')
+            else:
+                raise Exception('Flue gas source is not supported')
+
+        @self.Expression(self.flowsheet().config.time,
+                         doc="Reboiler duty in MW")
+        def reboiler_duty(b, t):   # mol/s /1000 = kgmol/s * 44 kg/kgmol = kg / s
+            return b.SRD[t] * (b.pureCO2_flow_mol_comp[t, "CO2"]
+                               * 44.01 / 1000)
+
+        @self.Expression(self.flowsheet().config.time,
+                         doc="Lean loading flowrate - kmol.hr")
+        def LL_flowrate(b, t):
+            CO2leanloading = b.lean_loading[t]
+            molality = b.Pz_mol[t]
+            Capture = b.CO2_capture_rate[t]
+            if b.config.flue_gas_source == Technology.ngcc:
+                if b.config.CO2_capture_rate_surrogates == Surrogates.PZ_90_capture:
+                    return - 33769665655.708782196045 * CO2leanloading - 85501806.085839942097664 * log(CO2leanloading) + 32051499965.332691192627 * exp(CO2leanloading) + 1399851299.6695346832275 * CO2leanloading**0.5 + 207401.67766238830517977 * (molality/4.)**2 - 13931345844.245344161987 * CO2leanloading**2 - 7882628979.2478094100952 * CO2leanloading**3 - 1216008.0524285589344800 * (molality*CO2leanloading/4.) - 721743.35243441292550415 * (molality/CO2leanloading/4.)**0.5 + 3619336.4956937171518803 * (CO2leanloading/molality/0.25) - 32534484771.443439483643
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_95_capture:
+                    return - 8128346023.5960435867310 * CO2leanloading + 8097036304.4302654266357 * exp(CO2leanloading) + 143943.79958957055350766 * (molality/4.)**2 - 3906104299.9280681610107 * CO2leanloading**2 - 1694548695.5476403236389 * CO2leanloading**3 - 326827.12907762406393886 * (molality*CO2leanloading/4.)**2 + 14089575.472042093053460 * (CO2leanloading/molality/0.25) - 4257.3482804741406653193 * (molality/CO2leanloading/4.)**2 - 22772347.982399865984917 * (CO2leanloading/molality/0.25)**2 + 131.22409336241202026940 * (molality/CO2leanloading/4.)**3 + 22174561.087608262896538 * (CO2leanloading/molality/0.25)**3 - 8094525387.7033281326294
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_97_capture:
+                    return - 11073017307.372232437134 * CO2leanloading + 868882.20868137793149799 * log(CO2leanloading) - 73653243483.647430419922 * exp(molality/2.) + 11052450134.832271575928 * exp(CO2leanloading) + 141797181109.30328369141 * (molality/2.)**2 - 5395322021.3101339340210 * CO2leanloading**2 - 920354.31067807460203767 * (molality*CO2leanloading/2.) - 117144381.90464735031128 * (molality*CO2leanloading/2.)**3 - 6153045658.4519367218018 * (CO2leanloading/molality/0.5)**3
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_90to97_capture:
+                    return 21493.893918343124823878 * Capture - 18837259.090658042579889 * CO2leanloading - 303798.35064064717153087 * molality - 547220.78199295536614954 * Capture**0.5 + 7997911.0828296802937984 * CO2leanloading**0.5 + 1594229.8231671806424856 * molality**0.5 + 16593654.797202151268721 * CO2leanloading**2 + 58165.759382280644786078 * Capture*CO2leanloading + 60595.207259644026635215 * Capture/molality + 60446137.647265411913395 * (CO2leanloading/molality)**2
+                # No data for PZAS, using same as PZ 95 capture
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.rel_PZAS_5mol_capture:
+                    return - 8128346023.5960435867310 * CO2leanloading + 8097036304.4302654266357 * exp(CO2leanloading) + 143943.79958957055350766 * (molality/4.)**2 - 3906104299.9280681610107 * CO2leanloading**2 - 1694548695.5476403236389 * CO2leanloading**3 - 326827.12907762406393886 * (molality*CO2leanloading/4.)**2 + 14089575.472042093053460 * (CO2leanloading/molality/0.25) - 4257.3482804741406653193 * (molality/CO2leanloading/4.)**2 - 22772347.982399865984917 * (CO2leanloading/molality/0.25)**2 + 131.22409336241202026940 * (molality/CO2leanloading/4.)**3 + 22174561.087608262896538 * (CO2leanloading/molality/0.25)**3 - 8094525387.7033281326294
+                elif b.config.CO2_capture_rate_surrogates == Surrogates.PZ_ref1_90_capture:
+                    A2 = b.lean_loading[t]
+                    B2 = b.Pz_mol[t]
+                    return (291019.778242091 * A2 - 4.98043194086976 * B2 - 287661.899241911 * exp(A2) + 130101.667827899 * A2**2 + 71279.9986297754 * A2**3 + 0.0148339624265494 * (B2/A2)**2 + 287396.698159522)
+                else:
+                    raise Exception('CO2 surrogate model is not supported')
+            elif b.config.flue_gas_source == Technology.scpc:
+                if b.config.CO2_capture_rate_surrogates == Surrogates.MEA:
+                    return 1  # No data available for solvent flowrate
+                else:
+                    raise Exception('solvent not supported for this technology')
+            else:
+                raise Exception('Flue gas source is not supported')
+
+        if self.config.CO2_capture_rate_surrogates == Surrogates.PZ_ref1_90_capture:
+            # This surrogate model is only valid for reference 1
+            @self.Expression(self.flowsheet().config.time,
+                             doc="L/G ratio")
+            def L_Gratio(b, t):
+                x1 = b.lean_loading[t]
+                x2 = b.Pz_mol[t]
+                return L_G_ratio.f(b.lean_loading[t], b.Pz_mol[t])
+
+        @self.Expression(self.flowsheet().config.time,
+                         doc="lean loading cost $/year")
+        def solvent_cost(b, t):
+            basis = 1 # 1 kg H2O basis for estimating mol fractions
+            PZ_MW = 86.1356
+            H2O_MW = 18.0153 # kg/kgmol
+            mol_H2O = basis / H2O_MW * 1000  # mol fraction
+            mol_CO2 = b.lean_loading[t] * b.Pz_mol[t]
+            PZ_mol_frac = b.Pz_mol[t]/(b.Pz_mol[t] + mol_H2O + mol_CO2)
+            H2O_mol_frac = mol_H2O / (b.Pz_mol[t] + mol_H2O + mol_CO2)
+            CO2_mol_frac = mol_CO2 / (b.Pz_mol[t] + mol_H2O + mol_CO2)
+            PZ_cost = 9 * PZ_MW  # PZ $9/kg  - converting to $/kmol
+            PZ_makeup = 0.02  # 2 % of total flowrate
+            return b.LL_flowrate[t] * PZ_makeup * PZ_mol_frac * 24 * 365 * PZ_cost
+
+    def initialize(blk,
+                   outlvl=idaeslog.NOTSET,
+                   solver='ipopt',
+                   optarg={'tol': 1e-6}):
+        '''
+        CO2 pure pyomo block initialization routine
+
+        Keyword Arguments:
+            outlvl : sets output level of initialisation routine
+
+            optarg : solver options dictionary object (default={'tol': 1e-6})
+            solver : str indicating whcih solver to use during
+                     initialization (default = 'ipopt')
+
+        Returns:
+            None
+        '''
+        iscale.calculate_scaling_factors(blk)  # remove to solve using baron
+        init_log = idaeslog.getInitLogger(blk.name, outlvl, tag="unit")
+        solve_log = idaeslog.getSolveLogger(blk.name, outlvl, tag="unit")
+        opt = pyo.SolverFactory(solver)
+        opt.options = optarg
+
+        init_log.info_low("Starting initialization...")
+
+        blk.inlet.flow_mol_comp[0, 'CO2'].fix()
+        blk.inlet.flow_mol_comp[0, 'O2'].fix()
+        blk.inlet.flow_mol_comp[0, 'H2O'].fix()
+        blk.inlet.flow_mol_comp[0, 'N2'].fix()
+        blk.CO2_capture_rate.fix()
+
+        # solve model
+        with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc:
+            res = opt.solve(blk, tee=slc.tee)
+        init_log.info_high(
+                "Initialization Step 1 {}.".format(idaeslog.condition(res))
+            )
+        init_log.info_high("Initialization Step 1 Complete.")
+
+        # ToDo: release state
+        init_log.info("Initialization Complete.")
+
+    def calculate_scaling_factors(self):
+        super().calculate_scaling_factors()
+
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'CO2'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'H2O'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'O2'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'N2'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'CO2'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'H2O'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'O2'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'N2'], 1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'CO2'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'H2O'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'O2'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'N2'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.inlet_temperature[0.0], 1e-2)
+        iscale.set_scaling_factor(self.pureCO2_temperature[0.0], 1e-2)
+        iscale.set_scaling_factor(self.exhaust_gas_temperature[0.0], 1e-2)
+        iscale.set_scaling_factor(self.inlet_pressure[0.0], 1e-5)
+        iscale.set_scaling_factor(self.pureCO2_pressure[0.0], 1e-5)
+        iscale.set_scaling_factor(self.exhaust_gas_pressure[0.0], 1e-5)
+
+        for t, c in self.exh_pressure_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_pressure[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.pureCO2_pressure_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_pressure[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.pureCO2_temp_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_temperature[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.exh_temp_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_temperature[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.flow_mol_comp_inlet_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_flow_mol_comp[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.pureCO2_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_flow_mol_comp[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/co2_capture_system.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/co2_capture_system.py
new file mode 100644
index 000000000..a7ce59e9a
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/co2_capture_system.py
@@ -0,0 +1,384 @@
+###############################################################################
+# Institute for the Design of Advanced Energy Systems Process Systems
+# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2020, by the
+# software owners: The Regents of the University of California, through
+# Lawrence Berkeley National Laboratory,  National Technology & Engineering
+# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia
+# University Research Corporation, et al. All rights reserved.
+#
+# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and
+# license information, respectively. Both files are also available online
+# at the URL "https://github.com/IDAES/idaes-pse".
+###############################################################################
+"""
+IDAES carbon capture system block, hybrid mass balances and surrogate models
+
+The unit has 1 inlet stream for rich CO2 inlet: inlet
+the unit has 2 outlet streams: pureCO2 and exhaust_gas
+
+The state variables are component flow mol, temperature, and pressure.
+The state variables can be accessed thotough ports named:
+    inlet - flue gas from NGCC plant
+    pure_CO2 - stream for compression train
+    exhaust_gas - exhaust gas to DAC system or stack
+
+Surrogate models are used to compute operating costs as a function of solvent
+flow rate, and specific reboiler duty. The inlet variables to estimate such
+output variables are: lean loading and PZ molality.
+Inlet Vars:
+
+* solvent lean loading (0.1 to 0.6)
+* solvent molality (PZ = 3, 5, 7 mol)
+
+Output Vars:
+
+* L/G ratio for absorber columns (0 - 12)
+* lean solvent flowrate in kmol/s (0-100)
+* specific reboiler duty (GJ/t CO2)
+
+Reference:
+Gaspar J. von Solms N., Thomsen K., Fosbol F. (2016) Multivariable Optimization
+ of the Piperazine CO2 Post-Combustion Process. Energy Procedia 86(2016)229-238
+"""
+# Import IDAES cores
+from idaes.core import declare_process_block_class, UnitModelBlockData
+import idaes.core.util.scaling as iscale
+# Additional import for the unit operation
+import pyomo.environ as pyo
+from pyomo.environ import Var, units as pyunits, exp, log
+from pyomo.network import Port
+import idaes.logger as idaeslog
+
+__author__ = "M. Zamarripa"
+__version__ = "1.0.0"
+
+
+# ----------------------------------------------------------------------------
+@declare_process_block_class("CO2Capture")
+class CO2CaptureData(UnitModelBlockData):
+    '''
+    CO2Capture surrogate model based on total flow of CO2 rich stream flow only
+    Assumptions: (toDo: update Assumptions)
+    Fixed composition, temperature, and pressure of feed stream
+    Fixed CO2 purity in the CO2 product stream
+
+    '''
+
+    def build(self):
+
+        self.component_list = ['CO2', 'H2O', 'O2', 'N2', 'Ar']
+
+        self.make_vars()
+        self.add_material_balances()
+        # self.add_surrogates()
+        # self.add_costing()
+
+        # Add ports: 3 (1 for inlet and 2 for outlets)
+        self.inlet = Port(noruleinit=True,
+                          doc="A port for co2 rich inlet stream")
+        self.pureCO2 = Port(noruleinit=True,
+                            doc="A port for pure CO2 outlet stream")
+        self.exhaust_gas = Port(noruleinit=True,
+                                doc="A port for vent gas outlet stream")
+
+        # Add state vars to the ports
+        # self.inlet.add(self.inlet_flow_mol, "flow_mol")
+        self.inlet.add(self.inlet_temperature, "temperature")
+        self.inlet.add(self.inlet_pressure, "pressure")
+        self.inlet.add(self.inlet_flow_mol_comp, "flow_mol_comp")
+
+        # self.pureco2.add(self.pureco2_flow_mol, "flow_mol")
+        self.pureCO2.add(self.pureCO2_temperature, "temperature")
+        self.pureCO2.add(self.pureCO2_pressure, "pressure")
+        self.pureCO2.add(self.pureCO2_flow_mol_comp, "flow_mol_comp")
+
+        # self.exhaust_gas.add(self.exhaust_gas_flow_mol, "flow_mol")
+        self.exhaust_gas.add(self.exhaust_gas_temperature, "temperature")
+        self.exhaust_gas.add(self.exhaust_gas_pressure, "pressure")
+        self.exhaust_gas.add(self.exhaust_gas_flow_mol_comp, "flow_mol_comp")
+
+    def make_vars(self):
+        '''
+        This section builds port vars (Fc, T, P), CO2 capture rate
+
+        '''
+        # units declaration for vars
+        flow_units = pyunits.mol/pyunits.s
+        pressure_units = pyunits.Pa
+        temperature_units = pyunits.K
+        # heat_duty_units = pyunits.J/pyunits.s
+
+        # Component mole flows [mol/s]
+        self.inlet_flow_mol_comp = Var(
+            self.flowsheet().config.time,
+            self.component_list,
+            initialize=1400/len(self.component_list),
+            units=flow_units,
+            doc='Inlet stream: Component mole flow [mol/s]')
+
+        self.pureCO2_flow_mol_comp = Var(
+            self.flowsheet().config.time,
+            self.component_list,
+            initialize=1200/len(self.component_list),
+            units=flow_units,
+            doc='PureCO2 stream: Component mole flow [mol/s]')
+
+        self.exhaust_gas_flow_mol_comp = Var(
+            self.flowsheet().config.time,
+            self.component_list,
+            initialize=100/len(self.component_list),
+            units=flow_units,
+            doc='exhaust_gas stream: Component mole flow [mol/s]')
+
+        # Temperature [K]
+        self.inlet_temperature = Var(self.flowsheet().config.time,
+                                     initialize=110,
+                                     units=temperature_units,
+                                     doc='Inlet temperature [K]')
+
+        self.pureCO2_temperature = Var(self.flowsheet().config.time,
+                                       initialize=110,
+                                       units=temperature_units,
+                                       doc='PureCO2 temperature [K]')
+
+        self.exhaust_gas_temperature = Var(self.flowsheet().config.time,
+                                           initialize=110,
+                                           units=temperature_units,
+                                           doc='exhaust_gas temperature [K]')
+
+        # Pressue [Pa]
+        self.inlet_pressure = Var(self.flowsheet().config.time,
+                                  initialize=17,
+                                  units=pressure_units,
+                                  doc='Inlet pressure [Pa]')
+
+        self.pureCO2_pressure = Var(self.flowsheet().config.time,
+                                    initialize=17,
+                                    units=pressure_units,
+                                    doc='PureCO2 pressure [Pa]')
+
+        self.exhaust_gas_pressure = Var(self.flowsheet().config.time,
+                                        initialize=17,
+                                        units=pressure_units,
+                                        doc='exhaust_gas pressure [Pa]')
+        # CO2 Capture rate
+        self.CO2_capture_rate = Var(self.flowsheet().config.time,
+                                    initialize=0.9,
+                                    doc='CO2 capture rate')
+        # lean loading
+        self.lean_loading = Var(self.flowsheet().config.time,
+                                initialize=0.1,
+                                bounds=(0.1, 6.0),
+                                doc='lean loading')
+        # Pz molality
+        self.Pz_mol = Var(self.flowsheet().config.time,
+                          initialize=3,
+                          bounds=(3, 7),
+                          doc='Pz molality')
+
+        self.SRD = Var(self.flowsheet().config.time,
+                       initialize=8,
+                       bounds=(0, 100),
+                       doc='Specific reformer duty GJ/ton CO2')
+
+    def add_material_balances(self):
+        ''' This section is for material balance constraints'''
+
+        # pureCO2 mass balance
+        @self.Constraint(self.flowsheet().config.time,
+                         self.component_list,
+                         doc="pureCO2 mass balances")
+        def pureCO2_eqn(b, t, c):
+            if c == "CO2":
+                return b.pureCO2_flow_mol_comp[t, "CO2"] == \
+                    b.inlet_flow_mol_comp[t, "CO2"] * b.CO2_capture_rate[t]
+            else:
+                return b.pureCO2_flow_mol_comp[t, c] == 0.0
+
+        # water drop in flue gas
+        @self.Expression(self.flowsheet().config.time, doc="water drop")
+        def water_drop(b, t):
+            return b.inlet_flow_mol_comp[0, 'H2O']*0.5
+
+        # Overall mass balances
+        @self.Constraint(self.flowsheet().config.time,
+                         self.component_list,
+                         doc="Inlet component mole flow eqn")
+        def flow_mol_comp_inlet_eqn(b, t, c):
+            if c == "H2O":
+                return b.inlet_flow_mol_comp[t, c] == \
+                    b.exhaust_gas_flow_mol_comp[t, c] + b.water_drop[t]
+            elif c == "CO2":
+                return b.inlet_flow_mol_comp[t, c] == \
+                    b.exhaust_gas_flow_mol_comp[t, c] \
+                    + b.pureCO2_flow_mol_comp[t, "CO2"]
+            else:
+                return b.inlet_flow_mol_comp[t, c] == \
+                    b.exhaust_gas_flow_mol_comp[t, c]
+
+        # Pressure equations
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Pressure drop")
+        def exh_pressure_eqn(b, t):
+            return b.inlet_pressure[t] == b.exhaust_gas_pressure[t]
+
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Pressure drop")
+        def pureCO2_pressure_eqn(b, t):
+            return b.inlet_pressure[t] == b.pureCO2_pressure[t]
+
+        # Temperature equations
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Temperature")
+        def pureCO2_temp_eqn(b, t):
+            return b.inlet_temperature[t] == b.pureCO2_temperature[t]
+
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Temperature")
+        def exh_temp_eqn(b, t):
+            return b.inlet_temperature[t] == b.exhaust_gas_temperature[t]
+
+        # surrogates and additional constraints/expressions
+        ''' This section is to add the surrogate models'''
+
+        @self.Constraint(self.flowsheet().config.time,
+                         doc="Specific reboiler duty in GJ/t CO2 or MJ/kg CO2")
+        def SRD_eqn(b, t):
+            x1 = b.lean_loading[t]
+            x2 = b.Pz_mol[t]
+            # return b.SRD[t] == (1135585.36238969 * x1
+            #                     - 440.509698856962 * log(x1)
+            #                     - 1127184.48012032 * exp(x1)
+            #                     + 533953.122356288 * x1**2
+            #                     + 248202.546339126 * x1**3
+            #                     + 1125580.12992624)
+            return b.SRD[t] == (- 15565.227507172588957474 * x1
+                                + 8.9237367058697429911263 * log(x1)
+                                + 15285.362653727905126289 * exp(x1)
+                                - 6741.0217492965430210461 * x1**2
+                                - 3865.5900513667870654899 * x1**3
+                                + 0.17312045830047528404555E-002 * x2**3
+                                - 1.2960483459315448317994 * x1*x2
+                                - 15235.956076189686427824)
+
+        @self.Expression(self.flowsheet().config.time,
+                         doc="Reboiler duty in MW")
+        def reboiler_duty(b, t):   # mol/s/1000 = kgmol/s * 44 kg/kgmol = kg/s
+            return b.SRD[t] * (b.pureCO2_flow_mol_comp[t, "CO2"]
+                               * 44.01 / 1000)
+
+        @self.Expression(self.flowsheet().config.time,
+                         doc="Lean loading flowrate - kmol.hr")
+        def LL_flowrate(b, t):
+            x1 = b.lean_loading[t]
+            x2 = b.Pz_mol[t]
+            return (- 4402167.2503122268244624 * x1
+                    - 4.4210697568168892956919 * x2
+                    + 42394.191672889020992443 * log(x1)
+                    + 4021949.9049337119795382 * exp(x1)
+                    - 1394566.6646474981680512 * x1**2
+                    - 1225122.9083037786185741 * x1**3
+                    - 3898754.8073137737810612)
+
+    def initialize(blk,
+                   outlvl=idaeslog.NOTSET,
+                   solver='ipopt',
+                   optarg={'tol': 1e-6}):
+        '''
+        CO2 pure pyomo block initialization routine
+
+        Keyword Arguments:
+            outlvl : sets output level of initialisation routine
+
+            optarg : solver options dictionary object (default={'tol': 1e-6})
+            solver : str indicating whcih solver to use during
+                     initialization (default = 'ipopt')
+
+        Returns:
+            None
+        '''
+        iscale.calculate_scaling_factors(blk)  # remove to solve using baron
+        init_log = idaeslog.getInitLogger(blk.name, outlvl, tag="unit")
+        solve_log = idaeslog.getSolveLogger(blk.name, outlvl, tag="unit")
+        opt = pyo.SolverFactory(solver)
+        opt.options = optarg
+
+        init_log.info_low("Starting initialization...")
+
+        blk.inlet.flow_mol_comp[0, 'CO2'].fix()
+        blk.inlet.flow_mol_comp[0, 'O2'].fix()
+        blk.inlet.flow_mol_comp[0, 'Ar'].fix()
+        blk.inlet.flow_mol_comp[0, 'H2O'].fix()
+        blk.inlet.flow_mol_comp[0, 'N2'].fix()
+        blk.CO2_capture_rate.fix()
+
+        # solve model
+        with idaeslog.solver_log(solve_log, idaeslog.DEBUG) as slc:
+            res = opt.solve(blk, tee=slc.tee)
+        init_log.info_high(
+                "Initialization Step 1 {}.".format(idaeslog.condition(res))
+            )
+        init_log.info_high("Initialization Step 1 Complete.")
+
+        # ToDo: release state
+        init_log.info("Initialization Complete.")
+
+    def calculate_scaling_factors(self):
+        super().calculate_scaling_factors()
+
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'CO2'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'H2O'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'O2'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'N2'], 1e-3)
+        iscale.set_scaling_factor(self.inlet_flow_mol_comp[0.0, 'Ar'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'CO2'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'H2O'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'O2'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'N2'], 1e-3)
+        iscale.set_scaling_factor(self.pureCO2_flow_mol_comp[0.0, 'Ar'], 1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'CO2'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'H2O'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'O2'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'N2'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.exhaust_gas_flow_mol_comp[0.0, 'Ar'],
+                                  1e-3)
+        iscale.set_scaling_factor(self.inlet_temperature[0.0], 1e-2)
+        iscale.set_scaling_factor(self.pureCO2_temperature[0.0], 1e-2)
+        iscale.set_scaling_factor(self.exhaust_gas_temperature[0.0], 1e-2)
+        iscale.set_scaling_factor(self.inlet_pressure[0.0], 1e-5)
+        iscale.set_scaling_factor(self.pureCO2_pressure[0.0], 1e-5)
+        iscale.set_scaling_factor(self.exhaust_gas_pressure[0.0], 1e-5)
+
+        for t, c in self.exh_pressure_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_pressure[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.pureCO2_pressure_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_pressure[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.pureCO2_temp_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_temperature[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.exh_temp_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_temperature[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.flow_mol_comp_inlet_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_flow_mol_comp[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
+
+        for t, c in self.pureCO2_eqn.items():
+            sf = iscale.get_scaling_factor(
+                self.inlet_flow_mol_comp[t], default=1, warning=True)
+            iscale.constraint_scaling_transform(c, sf)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/discharge_design_ccs.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/discharge_design_ccs.py
new file mode 100644
index 000000000..bf220d365
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/discharge_design_ccs.py
@@ -0,0 +1,1917 @@
+#################################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2022 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+#################################################################################
+
+"""This is a Generalized Disjunctive Programming model for the
+conceptual design of an ultra supercritical coal-fired power plant
+integrated with a discharge storage system
+
+"""
+
+__author__ = "Naresh Susarla and Soraya Rawlings"
+
+import logging
+
+# Import Python libraries
+from math import pi
+from IPython import embed
+
+# Import Pyomo libraries
+import pyomo.environ as pyo
+from pyomo.environ import (Block, Param, Constraint, Objective,
+                           TransformationFactory, SolverFactory,
+                           Expression, value, log, exp, Var)
+from pyomo.environ import units as pyunits
+from pyomo.network import Arc
+from pyomo.util.calc_var_value import calculate_variable_from_constraint
+from pyomo.gdp import Disjunct, Disjunction
+from pyomo.network.plugins import expand_arcs
+from pyomo.contrib.fbbt.fbbt import  _prop_bnds_root_to_leaf_map
+from pyomo.core.expr.numeric_expr import ExternalFunctionExpression
+
+# Import IDAES libraries
+import idaes.logger as idaeslog
+import idaes.core.util.scaling as iscale
+from idaes.core.util.initialization import propagate_state
+from idaes.core.solvers.get_solver import get_solver
+from idaes.core.util.model_statistics import degrees_of_freedom
+from idaes.core import UnitModelCostingBlock
+from idaes.models.unit_models import HeatExchanger, Heater, MomentumMixingType
+from idaes.models.unit_models.heat_exchanger import delta_temperature_underwood_callback
+from idaes.models_extra.power_generation.unit_models.helm import (HelmTurbineStage,
+                                                                  HelmSplitter,
+                                                                  HelmMixer)
+from idaes.models.costing.SSLW import (
+    SSLWCosting,
+    SSLWCostingData
+)
+from idaes.core.util.exceptions import ConfigurationError
+
+# Import ultra supercritical power plant model
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant import (
+    ultra_supercritical_powerplant as usc)
+
+# Import properties package for Solar salt
+from dispatches.properties import solarsalt_properties
+from pyomo.util.infeasible import (log_infeasible_constraints,
+                                    log_close_to_bounds)
+from IPython import embed
+logging.getLogger('pyomo.repn.plugins.nl_writer').setLevel(logging.ERROR)
+logging.basicConfig(level=logging.INFO)
+
+
+scaling_obj = 1
+
+def create_discharge_model(m, add_efficiency=None, power_max=None):
+    """Create flowsheet and add unit models.
+
+    """
+
+    # Create a block to add charge storage model
+    m.fs.discharge = Block()
+
+    # Add model data
+    _add_data(m)
+
+    # Add Solar salt properties
+    m.fs.solar_salt_properties = solarsalt_properties.SolarsaltParameterBlock()
+
+    ###########################################################################
+    #  Add unit models
+    ###########################################################################
+
+    # Declare splitter to divert condensate to discharge storage heat
+    # exchanger
+    m.fs.discharge.es_split = HelmSplitter(
+        property_package=m.fs.prop_water,
+        outlet_list=["to_fwh", "to_hxd"],
+    )
+
+    # Declare discharge storage heat exchanger
+    m.fs.discharge.hxd = HeatExchanger(
+        delta_temperature_callback=delta_temperature_underwood_callback,
+        hot_side_name="shell",
+        cold_side_name="tube",
+        shell={"property_package": m.fs.solar_salt_properties},
+        tube={"property_package": m.fs.prop_water},
+    )
+
+
+    ###########################################################################
+    # Add constraints
+    ###########################################################################
+    _make_constraints(m, add_efficiency=add_efficiency, power_max=power_max)
+
+    _solar_salt_ohtc_calculation(m)
+
+    ###########################################################################
+    #  Declare disjuncts
+    ###########################################################################
+    # Disjunction 1 for the sink of discharge HX consists of 2 disjuncts:
+    #   1. condpump_source_disjunct ======> condensate from condenser pump
+    #   2. fwh4_source_disjunct     ======> condensate from feed water heater 4
+    #   3. booster_source_disjunct  ======> condensate from booster pump
+    #   4. bfp_source_disjunct      ======> condensate from boiler feed water pump
+    #   5. fwh9_source_disjunct     ======> condensate from feed water heater 9
+
+    # Declare disjuncts in disjunction 1
+    m.fs.discharge.condpump_source_disjunct = Disjunct(
+        rule=condpump_source_disjunct_equations)
+    m.fs.discharge.fwh4_source_disjunct = Disjunct(
+        rule=fwh4_source_disjunct_equations)
+    m.fs.discharge.booster_source_disjunct = Disjunct(
+        rule=booster_source_disjunct_equations)
+    m.fs.discharge.bfp_source_disjunct = Disjunct(
+        rule=bfp_source_disjunct_equations)
+    m.fs.discharge.fwh9_source_disjunct = Disjunct(
+        rule=fwh9_source_disjunct_equations)
+
+    # Declare disjuncts in disjunction 2
+    m.fs.discharge.iplp_source_disjunct = Disjunct(
+        rule=iplp_source_disjunct_equations)
+    m.fs.discharge.hxd_source_disjunct = Disjunct(
+        rule=hxd_source_disjunct_equations)
+
+    ###########################################################################
+    #  Create stream arcs
+    ###########################################################################
+
+    m.fs.discharge.essplit_to_hxd = Arc(
+        source=m.fs.discharge.es_split.to_hxd,
+        destination=m.fs.discharge.hxd.tube_inlet,
+        doc="Connection from ES splitter to HXD"
+    )
+    TransformationFactory("network.expand_arcs").apply_to(m.fs.discharge)
+
+    return m
+
+
+def _add_data(m):
+    """Add data to the model
+    """
+
+    # Add Chemical Engineering cost index for 2019
+    m.CE_index = 607.5
+
+    # Add operating hours
+    m.fs.discharge.hours_per_day = pyo.Param(
+        initialize=6,
+        doc='Number of hours of charging per day'
+    )
+
+    # Define number of years over which the costs are annualized
+    m.fs.discharge.num_of_years = pyo.Param(
+        initialize=30,
+        doc='Number of years for cost annualization')
+
+    # Add data to compute overall heat transfer coefficient for the
+    # Solar salt storage heat exchanger using the Sieder-Tate
+    # correlation. Parameters for tube diameter and thickness assumed
+    # from the data in (2017) He et al., Energy Procedia 105, 980-985
+    m.fs.discharge.data_hxd = {
+        'tube_inner_dia': 0.032,
+        'tube_outer_dia': 0.036,
+        'k_steel': 21.5,
+        'number_tubes': 20,
+        'shell_inner_dia': 1
+    }
+    m.fs.discharge.hxd_tube_inner_dia = pyo.Param(
+        initialize=m.fs.discharge.data_hxd['tube_inner_dia'],
+        doc='Tube inner diameter in m')
+    m.fs.discharge.hxd_tube_outer_dia = pyo.Param(
+        initialize=m.fs.discharge.data_hxd['tube_outer_dia'],
+        doc='Tube outer diameter in m')
+    m.fs.discharge.hxd_k_steel = pyo.Param(
+        initialize=m.fs.discharge.data_hxd['k_steel'],
+        doc='Thermal conductivity of steel in W/m.K')
+    m.fs.discharge.hxd_n_tubes = pyo.Param(
+        initialize=m.fs.discharge.data_hxd['number_tubes'],
+        doc='Number of tubes')
+    m.fs.discharge.hxd_shell_inner_dia = pyo.Param(
+        initialize=m.fs.discharge.data_hxd['shell_inner_dia'],
+        doc='Shell inner diameter in m')
+
+    # Calculate sectional area of storage heat exchanger
+    m.fs.discharge.hxd_tube_cs_area = pyo.Expression(
+        expr=(pi / 4) *
+        (m.fs.discharge.hxd_tube_inner_dia**2),
+        doc="Tube inside cross sectional area in m2")
+    m.fs.discharge.hxd_tube_out_area = pyo.Expression(
+        expr=(pi / 4) *
+        (m.fs.discharge.hxd_tube_outer_dia**2),
+        doc="Tube cross sectional area including thickness in m2")
+    m.fs.discharge.hxd_shell_eff_area = pyo.Expression(
+        expr=(
+            (pi / 4) *
+            m.fs.discharge.hxd_shell_inner_dia**2 -
+            m.fs.discharge.hxd_n_tubes *
+            m.fs.discharge.hxd_tube_out_area
+        ),
+        doc="Effective shell cross sectional area in m2")
+
+    m.fs.discharge.hxd_tube_dia_ratio = (
+        m.fs.discharge.hxd_tube_outer_dia / m.fs.discharge.hxd_tube_inner_dia)
+    m.fs.discharge.hxd_log_tube_dia_ratio = log(m.fs.discharge.hxd_tube_dia_ratio)
+
+    # Add fuel cost data
+    m.data_cost = {
+        'coal_price': 2.11e-9,
+    }
+    m.fs.discharge.coal_price = pyo.Param(
+        initialize=m.data_cost['coal_price'],
+        doc='Coal price based on HHV in $/J')
+
+    # Add parameters to calculate the Solar salt pump costing. Since
+    # the unit is not explicitly modeled, the IDAES cost method is not
+    # used for this equipment.  The primary purpose of the salt pump
+    # is to move the molten salt without changing the pressure. Thus,
+    # the pressure head is computed assuming that the salt is moved on
+    # an average of 5m linear distance.
+    m.data_salt_pump = {
+        'FT': 1.5,
+        'FM': 2.0,
+        'head': 3.281*5,
+        'motor_FT': 1,
+        'nm': 1
+    }
+    m.fs.discharge.spump_FT = pyo.Param(
+        initialize=m.data_salt_pump['FT'],
+        doc='Pump Type Factor for vertical split case')
+    m.fs.discharge.spump_FM = pyo.Param(
+        initialize=m.data_salt_pump['FM'],
+        doc='Pump Material Factor Stainless Steel')
+    m.fs.discharge.spump_head = pyo.Param(
+        initialize=m.data_salt_pump['head'],
+        doc='Pump Head 5m in ft.')
+    m.fs.discharge.spump_motorFT = pyo.Param(
+        initialize=m.data_salt_pump['motor_FT'],
+        doc='Motor Shaft Type Factor')
+    m.fs.discharge.spump_nm = pyo.Param(
+        initialize=m.data_salt_pump['nm'],
+        doc='Motor Shaft Type Factor')
+
+
+def _make_constraints(m, add_efficiency=None, power_max=None):
+    """Declare constraints for the discharge model
+
+    """
+
+    m.fs.discharge_power_out = pyo.Var(
+        initialize=30,
+        bounds=(0, 200),
+        doc="Coal heat duty supplied to boiler in MW")
+
+    m.fs.net_power = pyo.Expression(
+        expr=(m.fs.plant_power_out[0] + m.fs.discharge_power_out)
+    )
+
+    m.fs.boiler_efficiency = pyo.Var(initialize=0.9,
+                                     bounds=(0, 1),
+                                     doc="Boiler efficiency")
+    m.fs.boiler_efficiency_eq = pyo.Constraint(
+        expr=m.fs.boiler_efficiency == (
+            0.2143 *
+            (m.fs.net_power / power_max) +
+            0.7357
+        ),
+        doc="Boiler efficiency in fraction"
+    )
+    m.fs.coal_heat_duty = pyo.Var(
+        initialize=1000,
+        bounds=(0, 1e5),
+        doc="Coal heat duty supplied to boiler in MW")
+
+    if add_efficiency:
+        m.fs.coal_heat_duty_eq = pyo.Constraint(
+            expr=m.fs.coal_heat_duty *
+            m.fs.boiler_efficiency ==
+            m.fs.plant_heat_duty[0]
+        )
+    else:
+        m.fs.coal_heat_duty_eq = pyo.Constraint(
+            expr=m.fs.coal_heat_duty == m.fs.plant_heat_duty[0]
+        )
+
+    m.fs.cycle_efficiency = pyo.Var(initialize=0.4,
+                                    bounds=(0, 1),
+                                    doc="Cycle efficiency")
+    m.fs.cycle_efficiency_eq = pyo.Constraint(
+        expr=(
+            m.fs.cycle_efficiency *
+            m.fs.coal_heat_duty
+        ) == m.fs.net_power,
+        doc="Cycle efficiency"
+    )
+
+
+def _solar_salt_ohtc_calculation(m):
+    """Block of equations to compute overall heat transfer coefficient for
+    Solar salt heat exchanger
+
+    """
+
+    # Calculate Reynolds number for the salt
+    m.fs.discharge.hxd.salt_reynolds_number = pyo.Expression(
+        expr=(
+            (m.fs.discharge.hxd.shell_inlet.flow_mass[0] *
+             m.fs.discharge.hxd_tube_outer_dia) /
+            (m.fs.discharge.hxd_shell_eff_area *
+             m.fs.discharge.hxd.hot_side.properties_in[0].visc_d_phase["Liq"])
+        ),
+        doc="Salt Reynolds Number")
+
+    # Calculate Prandtl number for the salt
+    m.fs.discharge.hxd.salt_prandtl_number = pyo.Expression(
+        expr=(
+            m.fs.discharge.hxd.hot_side.properties_in[0].cp_mass["Liq"] *
+            m.fs.discharge.hxd.hot_side.properties_in[0].visc_d_phase["Liq"] /
+            m.fs.discharge.hxd.hot_side.properties_in[0].therm_cond_phase["Liq"]
+        ),
+        doc="Salt Prandtl Number")
+
+    # Calculate Prandtl Wall number for the salt
+    m.fs.discharge.hxd.salt_prandtl_wall = pyo.Expression(
+        expr=(
+            m.fs.discharge.hxd.hot_side.properties_out[0].cp_mass["Liq"] *
+            m.fs.discharge.hxd.hot_side.properties_out[0].visc_d_phase["Liq"] /
+            m.fs.discharge.hxd.hot_side.properties_out[0].therm_cond_phase["Liq"]
+        ),
+        doc="Salt Prandtl Number at wall")
+
+    # Calculate Nusselt number for the salt
+    m.fs.discharge.hxd.salt_nusselt_number = pyo.Expression(
+        expr=(
+            0.35 *
+            (m.fs.discharge.hxd.salt_reynolds_number**0.6) *
+            (m.fs.discharge.hxd.salt_prandtl_number**0.4) *
+            ((m.fs.discharge.hxd.salt_prandtl_number /
+              m.fs.discharge.hxd.salt_prandtl_wall) ** 0.25) *
+            (2**0.2)
+        ),
+        doc="Salt Nusslet Number from 2019, App Ener (233-234), 126")
+
+    # Calculate Reynolds number for the steam
+    m.fs.discharge.hxd.steam_reynolds_number = pyo.Expression(
+        expr=(
+            m.fs.discharge.hxd.tube_inlet.flow_mol[0] *
+            m.fs.discharge.hxd.cold_side.properties_in[0].mw *
+            m.fs.discharge.hxd_tube_inner_dia /
+            (m.fs.discharge.hxd_tube_cs_area *
+             m.fs.discharge.hxd_n_tubes *
+             m.fs.discharge.hxd.cold_side.properties_in[0].visc_d_phase["Vap"])
+        ),
+        doc="Steam Reynolds Number")
+
+    # Calculate Reynolds number for the steam
+    m.fs.discharge.hxd.steam_prandtl_number = pyo.Expression(
+        expr=(
+            (m.fs.discharge.hxd.cold_side.properties_in[0].cp_mol /
+             m.fs.discharge.hxd.cold_side.properties_in[0].mw) *
+            m.fs.discharge.hxd.cold_side.
+            properties_in[0].visc_d_phase["Vap"] /
+            m.fs.discharge.hxd.cold_side.properties_in[0].therm_cond_phase["Vap"]
+        ),
+        doc="Steam Prandtl Number")
+
+    # Calculate Reynolds number for the steam
+    m.fs.discharge.hxd.steam_nusselt_number = pyo.Expression(
+        expr=(
+            0.023 *
+            (m.fs.discharge.hxd.steam_reynolds_number**0.8) *
+            (m.fs.discharge.hxd.steam_prandtl_number**(0.33)) *
+            (
+                (m.fs.discharge.hxd.cold_side.properties_in[0].visc_d_phase["Vap"] /
+                 m.fs.discharge.hxd.cold_side.properties_out[0].visc_d_phase["Liq"]) ** 0.14
+            )
+        ),
+        doc="Steam Nusslet Number from 2001 Zavoico, Sandia")
+
+    # Calculate heat transfer coefficients for the salt and steam
+    # sides of discharge heat exchanger
+    m.fs.discharge.hxd.h_salt = pyo.Expression(
+        expr=(
+            m.fs.discharge.hxd.hot_side.properties_in[0].therm_cond_phase["Liq"] *
+            m.fs.discharge.hxd.salt_nusselt_number /
+            m.fs.discharge.hxd_tube_outer_dia
+        ),
+        doc="Salt side convective heat transfer coefficient in W/m.K")
+    m.fs.discharge.hxd.h_steam = pyo.Expression(
+        expr=(
+            m.fs.discharge.hxd.cold_side.properties_in[0].therm_cond_phase["Vap"] *
+            m.fs.discharge.hxd.steam_nusselt_number /
+            m.fs.discharge.hxd_tube_inner_dia
+        ),
+        doc="Steam side convective heat transfer coefficient in W/m.K")
+
+    # Calculate overall heat transfer coefficient for Solar salt heat
+    # exchanger
+    @m.fs.discharge.hxd.Constraint(m.fs.time)
+    def constraint_hxd_ohtc(b, t):
+        return (
+            m.fs.discharge.hxd.overall_heat_transfer_coefficient[t] * (
+                2 * m.fs.discharge.hxd_k_steel *
+                m.fs.discharge.hxd.h_steam +
+                m.fs.discharge.hxd_tube_outer_dia *
+                m.fs.discharge.hxd_log_tube_dia_ratio *
+                m.fs.discharge.hxd.h_salt *
+                m.fs.discharge.hxd.h_steam +
+                m.fs.discharge.hxd_tube_dia_ratio *
+                m.fs.discharge.hxd.h_salt *
+                2 * m.fs.discharge.hxd_k_steel
+            )
+        ) == (2 * m.fs.discharge.hxd_k_steel *
+              m.fs.discharge.hxd.h_salt *
+              m.fs.discharge.hxd.h_steam)
+
+
+def disconnect_arcs(m):
+    """Disconnect arcs from ultra-supercritical plant base model to
+    connect the Solar salt discharge storage system
+
+    """
+
+    for arc_s in [
+            m.fs.condpump_to_fwh1,
+            m.fs.fwh4_to_fwh5,
+            m.fs.booster_to_fwh6,
+            m.fs.bfp_to_fwh8,
+            m.fs.fwh9_to_boiler,
+            m.fs.t6split_to_bfpt,
+            # m.fs.rh2_to_turb5,
+            # m.fs.cond_to_condpump
+            # m.fs.bfpt_to_condmix
+            ]:
+        arc_s.expanded_block.enth_mol_equality.deactivate()
+        arc_s.expanded_block.flow_mol_equality.deactivate()
+        arc_s.expanded_block.pressure_equality.deactivate()
+
+
+def add_disjunction(m):
+    """Add disjunction for the selection of condensate source to integrate
+    the discharge storage system to the power plant model
+
+    """
+
+    # Add disjunction 2 for ccs steam source selection
+    m.fs.ccs_source_disjunction = Disjunction(
+        expr=[
+            m.fs.discharge.iplp_source_disjunct,
+            m.fs.discharge.hxd_source_disjunct
+            ]
+    )
+
+    # Add disjunction 1 for condensate source selection
+    m.fs.hxd_source_disjunction = Disjunction(
+        expr=[
+            m.fs.discharge.booster_source_disjunct,
+            m.fs.discharge.bfp_source_disjunct,
+            m.fs.discharge.fwh4_source_disjunct,
+            m.fs.discharge.fwh9_source_disjunct,
+            m.fs.discharge.condpump_source_disjunct
+            ]
+    )
+
+    # Expand arcs within the disjuncts
+    expand_arcs.obj_iter_kwds['descend_into'] = (Block, Disjunct)
+    TransformationFactory("network.expand_arcs").apply_to(m.fs.discharge)
+
+    return m
+
+
+# def rh2_source_disjunct_equations(disj):
+#     """Block of equations for disjunct 1 in disjunction 1 for the selection
+#     of condensate water source from condenser pump
+
+#     """
+
+#     m = disj.model()
+
+#     # Declare turbine for storage system
+#     m.fs.discharge.rh2_source_disjunct.es_turbine = HelmTurbineStage(
+#         property_package=m.fs.prop_water
+#     )
+
+#     m.fs.discharge.rh2_source_disjunct.es_turbine.efficiency_isentropic.fix(0.8)
+
+#     for est in [m.fs.discharge.rh2_source_disjunct.es_turbine.control_volume]:
+#         iscale.set_scaling_factor(est.work, 1e-6)
+
+#               # + (m.fs.discharge.es_turbine.control_volume.work[0] * (-1e-6))
+#     # Add a constraint to storage turbine to ensure that the outlet
+#     # temperature is at the saturation temperature
+#     @m.fs.discharge.rh2_source_disjunct.es_turbine.Constraint(
+#         m.fs.time,
+#         doc="Turbine outlet should be a saturated steam")
+#     def constraint_esturbine_temperature_out(b, t):
+#         return (
+#             b.control_volume.properties_out[t].temperature ==
+#             b.control_volume.properties_out[t].temperature_sat + 1
+#         )
+#     @m.fs.discharge.rh2_source_disjunct.Constraint(
+#         m.fs.time,
+#         doc="Turbine outlet should be a saturated steam")
+#     def constraint_storage_power_out(b, t):
+#         return (
+#             m.fs.discharge_power_out ==
+#             (b.es_turbine.control_volume.work[0] * (-1e-6))
+#         )
+
+#     m.fs.discharge.rh2_source_disjunct.hxd_to_esturbine = Arc(
+#         source=m.fs.discharge.hxd.tube_outlet,
+#         destination=m.fs.discharge.rh2_source_disjunct.es_turbine.inlet,
+#         doc="Connection from HXD to ES turbine"
+#     )
+
+#     # Add splitter to send a fraction of steam to the charge storage
+#     # system
+#     m.fs.discharge.rh2_source_disjunct.ccs_split = HelmSplitter(
+#         property_package=m.fs.prop_water,
+#         outlet_list=["to_ccs", "to_turb"],
+#     )
+
+#     m.fs.discharge.rh2_source_disjunct.rh2_to_rhsplit = Arc(
+#         source=m.fs.reheater[2].outlet,
+#         destination=m.fs.discharge.rh2_source_disjunct.ccs_split.inlet,
+#         doc="Connection from RH2 to T5 split"
+#     )
+#     m.fs.discharge.rh2_source_disjunct.rhsplit_to_bfpt = Arc(
+#         source=m.fs.discharge.rh2_source_disjunct.ccs_split.to_turb,
+#         destination=m.fs.turbine[5].inlet,
+#         doc="Connection from RH split to T5"
+#     )
+#     m.fs.discharge.rh2_source_disjunct.rhsplit_to_ccs = Arc(
+#         source=m.fs.discharge.rh2_source_disjunct.ccs_split.to_ccs,
+#         destination=m.fs.ccs_reboiler.inlet,
+#         doc="Connection from bfpt split to ccs reboiler"
+#     )
+
+#     for unit_k in [m.fs.discharge.rh2_source_disjunct.es_turbine]:
+#         unit_k.inlet.flow_mol[:].setlb(0)
+#         unit_k.inlet.flow_mol[:].setub(10000)
+#         unit_k.outlet.flow_mol[:].setlb(0)
+#         unit_k.outlet.flow_mol[:].setub(10000)
+
+#         unit_k.control_volume.work[:].setub(0)
+#         unit_k.control_volume.work[:].setlb(-1e9)
+#         # unit_k.inlet.pressure[:].setlb(0)
+#         # unit_k.inlet.pressure[:].setub(1e12)
+#         # unit_k.outlet.pressure[:].setlb(0)
+#         # unit_k.outlet.pressure[:].setub(1e12)
+#         unit_k.deltaP[:].setlb(0)
+#         unit_k.deltaP[:].setub(1e9)
+
+
+def iplp_source_disjunct_equations(disj):
+    """Block of equations for disjunct 1 in disjunction 1 for the selection
+    of condensate water source from condenser pump
+
+    """
+
+    m = disj.model()
+
+    # Declare turbine for storage system
+    m.fs.discharge.iplp_source_disjunct.es_turbine = HelmTurbineStage(
+        property_package=m.fs.prop_water
+    )
+
+    m.fs.discharge.iplp_source_disjunct.es_turbine.efficiency_isentropic.fix(0.8)
+
+    for est in [m.fs.discharge.iplp_source_disjunct.es_turbine.control_volume]:
+        iscale.set_scaling_factor(est.work, 1e-6)
+
+              # + (m.fs.discharge.es_turbine.control_volume.work[0] * (-1e-6))
+    # Add a constraint to storage turbine to ensure that the outlet
+    # temperature is at the saturation temperature
+    @m.fs.discharge.iplp_source_disjunct.es_turbine.Constraint(
+        m.fs.time,
+        doc="Turbine outlet should be a saturated steam")
+    def constraint_esturbine_temperature_out(b, t):
+        return (
+            b.control_volume.properties_out[t].temperature ==
+            b.control_volume.properties_out[t].temperature_sat + 1
+        )
+    # m.fs.discharge.iplp_source_disjunct.es_turbine.control_volume.properties_out[0].enth_mol.fix(20000)
+    # m.fs.discharge.iplp_source_disjunct.es_turbine.ratioP[0].fix(0.02)
+    @m.fs.discharge.iplp_source_disjunct.Constraint(
+        m.fs.time,
+        doc="Turbine outlet should be a saturated steam")
+    def constraint_storage_power_out(b, t):
+        return (
+            m.fs.discharge_power_out ==
+            (b.es_turbine.control_volume.work[0] * (-1e-6))
+        )
+
+    m.fs.discharge.iplp_source_disjunct.hxd_to_esturbine = Arc(
+        source=m.fs.discharge.hxd.tube_outlet,
+        destination=m.fs.discharge.iplp_source_disjunct.es_turbine.inlet,
+        doc="Connection from HXD to ES turbine"
+    )
+
+    # Add splitter to send a fraction of steam to the charge storage
+    # system
+    m.fs.discharge.iplp_source_disjunct.ccs_split = HelmSplitter(
+        property_package=m.fs.prop_water,
+        outlet_list=["to_ccs", "to_turb"],
+    )
+
+    m.fs.discharge.iplp_source_disjunct.t6split_to_ccsplit = Arc(
+        source=m.fs.turbine_splitter[6].outlet_3,
+        destination=m.fs.discharge.iplp_source_disjunct.ccs_split.inlet,
+        doc="Connection from Turbine 6 outlet 3 to ccs split"
+    )
+    m.fs.discharge.iplp_source_disjunct.bfptsplit_to_bfpt = Arc(
+        source=m.fs.discharge.iplp_source_disjunct.ccs_split.to_turb,
+        destination=m.fs.bfpt.inlet,
+        doc="Connection from ccs split to bfpt"
+    )
+    m.fs.discharge.iplp_source_disjunct.ccsplit_to_ccs = Arc(
+        source=m.fs.discharge.iplp_source_disjunct.ccs_split.to_ccs,
+        destination=m.fs.ccs_reboiler.inlet,
+        doc="Connection from bfpt split to ccs reboiler"
+    )
+
+
+def hxd_source_disjunct_equations(disj):
+    """Block of equations for disjunct 1 in disjunction 1 for the selection
+    of condensate water source from condenser pump
+
+    """
+
+    m = disj.model()
+
+    # Define arcs to connect units within disjunct
+    m.fs.discharge_power_out.fix(0)
+
+    # m.fs.discharge.hxd_source_disjunct.rh2_to_turb5 = Arc(
+    #     source=m.fs.reheater[2].outlet,
+    #     destination=m.fs.turbine[5].inlet
+    # )
+    m.fs.discharge.hxd_source_disjunct.t6split_to_bfpt = Arc(
+        source=m.fs.turbine_splitter[6].outlet_3,
+        destination=m.fs.bfpt.inlet
+    )
+    m.fs.discharge.hxd_source_disjunct.hxd_to_ccs = Arc(
+        source=m.fs.discharge.hxd.tube_outlet,
+        destination=m.fs.ccs_reboiler.inlet,
+        doc="Connection from Turbine 6 outlet 3 to ccs split"
+    )
+
+def condpump_source_disjunct_equations(disj):
+    """Block of equations for disjunct 1 in disjunction 1 for the selection
+    of condensate water source from condenser pump
+
+    """
+
+    m = disj.model()
+
+    # Define arcs to connect units within disjunct
+    m.fs.discharge.condpump_source_disjunct.condpump_to_essplit = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.discharge.es_split.inlet,
+        doc="Connection from Condenser pump to ES splitter"
+    )
+    m.fs.discharge.condpump_source_disjunct.essplit_to_fwh1 = Arc(
+        source=m.fs.discharge.es_split.to_fwh,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from ES splitter to FWH1"
+    )
+
+    m.fs.discharge.condpump_source_disjunct.fwh4_to_fwh5 = Arc(
+        source=m.fs.fwh[4].tube_outlet,
+        destination=m.fs.fwh[5].tube_inlet,
+        doc="Connection from FWH4 to FWH5"
+    )
+
+    m.fs.discharge.condpump_source_disjunct.booster_to_fwh6 = Arc(
+        source=m.fs.booster.outlet,
+        destination=m.fs.fwh[6].tube_inlet,
+        doc="Connection from booster pump to FWH6"
+    )
+
+    m.fs.discharge.condpump_source_disjunct.bfp_to_fwh8 = Arc(
+        source=m.fs.bfp.outlet,
+        destination=m.fs.fwh[8].tube_inlet,
+        doc="Connection from BFP to FWH8"
+    )
+
+    m.fs.discharge.condpump_source_disjunct.fwh9_to_boiler = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from FWH9 to boiler"
+    )
+
+
+def fwh4_source_disjunct_equations(disj):
+    """Block of equations for disjunct 2 in disjunction 1 for the selection
+    of condensate water source from feed water heater 4
+
+    """
+
+    m = disj.model()
+
+    # Define arcs to connect units within disjunct
+    m.fs.discharge.fwh4_source_disjunct.fwh4_to_essplit = Arc(
+        source=m.fs.fwh[4].tube_outlet,
+        destination=m.fs.discharge.es_split.inlet,
+        doc="Connection from FWH4 to ES splitter"
+    )
+    m.fs.discharge.fwh4_source_disjunct.essplit_to_fwh5 = Arc(
+        source=m.fs.discharge.es_split.to_fwh,
+        destination=m.fs.fwh[5].tube_inlet,
+        doc="Connection from ES splitter to FWH5"
+    )
+
+    m.fs.discharge.fwh4_source_disjunct.condpump_to_fwh1 = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump to FWH1"
+    )
+
+    m.fs.discharge.fwh4_source_disjunct.booster_to_fwh6 = Arc(
+        source=m.fs.booster.outlet,
+        destination=m.fs.fwh[6].tube_inlet,
+        doc="Connection from booster pump to FWH6"
+    )
+
+    m.fs.discharge.fwh4_source_disjunct.bfp_to_fwh8 = Arc(
+        source=m.fs.bfp.outlet,
+        destination=m.fs.fwh[8].tube_inlet,
+        doc="Connection from BFP to FWH8"
+    )
+
+    m.fs.discharge.fwh4_source_disjunct.fwh9_to_boiler = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from FWH9 to boiler"
+    )
+
+
+def booster_source_disjunct_equations(disj):
+    """Block of equations for disjunct 3 in disjunction 1 for the
+    selection of condensate water source from booster pump
+
+    """
+
+    m = disj.model()
+
+    # Define arcs to connect units within disjunct
+    m.fs.discharge.booster_source_disjunct.booster_to_essplit = Arc(
+        source=m.fs.booster.outlet,
+        destination=m.fs.discharge.es_split.inlet,
+        doc="Connection from Booster pump to ES splitter"
+    )
+    m.fs.discharge.booster_source_disjunct.essplit_to_fwh6 = Arc(
+        source=m.fs.discharge.es_split.to_fwh,
+        destination=m.fs.fwh[6].tube_inlet,
+        doc="Connection from ES splitter to FWH6"
+    )
+
+    m.fs.discharge.booster_source_disjunct.fwh4_to_fwh5 = Arc(
+        source=m.fs.fwh[4].tube_outlet,
+        destination=m.fs.fwh[5].tube_inlet,
+        doc="Connection from FWH4 to FWH5"
+    )
+
+    m.fs.discharge.booster_source_disjunct.condpump_to_fwh1 = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump to FWH1"
+    )
+
+    m.fs.discharge.booster_source_disjunct.bfp_to_fwh8 = Arc(
+        source=m.fs.bfp.outlet,
+        destination=m.fs.fwh[8].tube_inlet,
+        doc="Connection from BFP to FWH8"
+    )
+
+    m.fs.discharge.booster_source_disjunct.fwh9_to_boiler = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from FWH9 to boiler"
+    )
+
+
+def bfp_source_disjunct_equations(disj):
+    """Block of equations for disjunct 2 in disjunction 1 for the
+    selection of condensate water source from boiler feed water pump
+
+    """
+
+    m = disj.model()
+
+    # Define arcs to connect units within disjunct
+    m.fs.discharge.bfp_source_disjunct.bfp_to_essplit = Arc(
+        source=m.fs.bfp.outlet,
+        destination=m.fs.discharge.es_split.inlet,
+        doc="Connection from BFP to ES splitter"
+    )
+    m.fs.discharge.bfp_source_disjunct.essplit_to_fwh8 = Arc(
+        source=m.fs.discharge.es_split.to_fwh,
+        destination=m.fs.fwh[8].tube_inlet,
+        doc="Connection from ES splitter to FWH8"
+    )
+
+    m.fs.discharge.bfp_source_disjunct.fwh4_to_fwh5 = Arc(
+        source=m.fs.fwh[4].tube_outlet,
+        destination=m.fs.fwh[5].tube_inlet,
+        doc="Connection from FWH4 to FWH5"
+    )
+
+    m.fs.discharge.bfp_source_disjunct.condpump_to_fwh1 = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump to FWH1"
+    )
+
+    m.fs.discharge.bfp_source_disjunct.booster_to_fwh6 = Arc(
+        source=m.fs.booster.outlet,
+        destination=m.fs.fwh[6].tube_inlet,
+        doc="Connection from booster pump to FWH6"
+    )
+
+    m.fs.discharge.bfp_source_disjunct.fwh9_to_boiler = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from FWH9 to boiler"
+    )
+
+
+def fwh9_source_disjunct_equations(disj):
+    """Block of equations for disjunct 2 in disjunction 1 for the
+    selection of condensate water source from feed water heater 9
+
+    """
+
+    m = disj.model()
+
+    # Define arcs to connect units within disjunct
+    m.fs.discharge.fwh9_source_disjunct.fwh9_to_essplit = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.discharge.es_split.inlet,
+        doc="Connection from FWH9 to the ES SPlitter"
+    )
+    m.fs.discharge.fwh9_source_disjunct.essplit_to_boiler = Arc(
+        source=m.fs.discharge.es_split.to_fwh,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from ES splitter to Boiler"
+    )
+
+    m.fs.discharge.fwh9_source_disjunct.fwh4_to_fwh5 = Arc(
+        source=m.fs.fwh[4].tube_outlet,
+        destination=m.fs.fwh[5].tube_inlet,
+        doc="Connection from FWH4 to FWH5"
+    )
+
+    m.fs.discharge.fwh9_source_disjunct.condpump_to_fwh1 = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump to FWH1"
+    )
+
+    m.fs.discharge.fwh9_source_disjunct.booster_to_fwh6 = Arc(
+        source=m.fs.booster.outlet,
+        destination=m.fs.fwh[6].tube_inlet,
+        doc="Connection from booster to FWH6"
+    )
+
+    m.fs.discharge.fwh9_source_disjunct.bfp_to_fwh8 = Arc(
+        source=m.fs.bfp.outlet,
+        destination=m.fs.fwh[8].tube_inlet,
+        doc="Connection from BFP to FWH8"
+    )
+
+
+def set_model_input(m):
+    """Define model inputs such as fixed variables and parameter
+    values. The arameter values in this block, unless otherwise stated
+    explicitly, are either assumed or estimated for a total power out
+    of 437 MW. The inputs fixed in this function are the necessary
+    inputs to obtain a square model (0 degrees of freedom).
+
+    Unless stated otherwise, the units are: temperature in K, pressure
+    in Pa, flow in mol/s, massic flow in kg/s, and heat and heat duty
+    in W
+
+    """
+
+    ###########################################################################
+    # Fix data in discharge system
+    ###########################################################################
+    # Add heat exchanger area from supercritical plant model_input. For
+    # conceptual design optimization, area is unfixed and optimized
+    m.fs.discharge.hxd.area.fix(2000)
+
+    # Define storage fluid conditions. The fluid inlet flow is fixed
+    # during initialization, but is unfixed and determined during
+    # optimization
+    m.fs.discharge.hxd.shell_inlet.flow_mass.fix(200)
+    m.fs.discharge.hxd.shell_inlet.temperature.fix(831.15)
+    m.fs.discharge.hxd.shell_inlet.pressure.fix(101325)
+
+    m.fs.discharge.es_split.inlet.flow_mol.fix(17854)
+    m.fs.discharge.es_split.inlet.enth_mol.fix(52232)
+    m.fs.discharge.es_split.inlet.pressure.fix(3.4958e7)
+
+    ###########################################################################
+    # Fix data in condensate source splitter
+    ###########################################################################
+    # The model is built for a fixed flow of condensate through the
+    # discharge heat exchanger. This condensate flow is unfixed and
+    # determined during design optimization
+    m.fs.discharge.es_split.split_fraction[0, "to_hxd"].fix(0.2)
+
+    ###########################################################################
+    # Inputs for flue gas and capture units
+    ###########################################################################
+    m.fs.fg_to_ccs_splitfraction[:].fix(1)
+    m.fs.ccs_reboiler.inlet.pressure[0].fix(31126000)
+    m.fs.ccs_reboiler.inlet.enth_mol[0].fix(61493)
+    m.fs.ccs_reboiler.inlet.flow_mol[0].fix(10740)
+    m.fs.ccs_reboiler.outlet.pressure[0].fix(7000)
+    # m.fs.ccs_reboiler.outlet.enth_mol[0].fix(3000)
+
+def set_scaling_factors(m):
+    """Scaling factors in the flowsheet
+
+    """
+
+    # Include scaling factors for solar discharge heat exchanger
+    iscale.set_scaling_factor(
+        m.fs.ccs_reboiler.control_volume.heat, 1e-6)
+    for htf in [m.fs.discharge.hxd]:
+        iscale.set_scaling_factor(htf.area, 1e-2)
+        iscale.set_scaling_factor(
+            htf.overall_heat_transfer_coefficient, 1e-3)
+        iscale.set_scaling_factor(htf.tube.heat, 1e-6)
+        iscale.set_scaling_factor(htf.shell.heat, 1e-6)
+
+
+
+def initialize(m, solver=None, optarg=None, outlvl=idaeslog.NOTSET):
+    """Initialize the units included in the discharge model
+
+    """
+    # Include scaling factors
+    iscale.calculate_scaling_factors(m) 
+
+    # Initialize splitter
+    propagate_state(m.fs.discharge.fwh4_source_disjunct.fwh4_to_essplit)
+    propagate_state(m.fs.discharge.condpump_source_disjunct.condpump_to_essplit)
+    propagate_state(m.fs.discharge.booster_source_disjunct.booster_to_essplit)
+    propagate_state(m.fs.discharge.bfp_source_disjunct.bfp_to_essplit)
+    propagate_state(m.fs.discharge.fwh9_source_disjunct.fwh9_to_essplit)
+    m.fs.discharge.es_split.initialize(outlvl=outlvl,
+                                       optarg=optarg)
+
+    # Deactivate feed water heat constraints for initialization
+    for i in m.set_fwh:
+        m.fs.fwh[i].fwh_vfrac_constraint.deactivate()
+        
+    # Initialize feed water heat 1
+    propagate_state(m.fs.discharge.condpump_source_disjunct.essplit_to_fwh1)
+    propagate_state(m.fs.discharge.fwh4_source_disjunct.condpump_to_fwh1)
+    propagate_state(m.fs.discharge.booster_source_disjunct.condpump_to_fwh1)
+    propagate_state(m.fs.discharge.bfp_source_disjunct.condpump_to_fwh1)
+    propagate_state(m.fs.discharge.fwh9_source_disjunct.condpump_to_fwh1)
+    m.fs.fwh[1].initialize(outlvl=outlvl,
+                           optarg=solver.options)
+    
+    # Initialize feed water heat 5
+    propagate_state(m.fs.discharge.condpump_source_disjunct.fwh4_to_fwh5)
+    propagate_state(m.fs.discharge.fwh4_source_disjunct.essplit_to_fwh5)
+    propagate_state(m.fs.discharge.booster_source_disjunct.fwh4_to_fwh5)
+    propagate_state(m.fs.discharge.bfp_source_disjunct.fwh4_to_fwh5)
+    propagate_state(m.fs.discharge.fwh9_source_disjunct.fwh4_to_fwh5)
+    m.fs.fwh[5].initialize(outlvl=outlvl,
+                           optarg=solver.options)
+    
+    # Initialize feed water heat 6
+    propagate_state(m.fs.discharge.condpump_source_disjunct.booster_to_fwh6)
+    propagate_state(m.fs.discharge.fwh4_source_disjunct.booster_to_fwh6)
+    propagate_state(m.fs.discharge.booster_source_disjunct.essplit_to_fwh6)
+    propagate_state(m.fs.discharge.bfp_source_disjunct.booster_to_fwh6)
+    propagate_state(m.fs.discharge.fwh9_source_disjunct.booster_to_fwh6)
+    m.fs.fwh[6].initialize(outlvl=outlvl,
+                           optarg=solver.options)
+    
+    # Initialize feed water heat 8
+    propagate_state(m.fs.discharge.condpump_source_disjunct.bfp_to_fwh8)
+    propagate_state(m.fs.discharge.fwh4_source_disjunct.bfp_to_fwh8)
+    propagate_state(m.fs.discharge.booster_source_disjunct.bfp_to_fwh8)
+    propagate_state(m.fs.discharge.bfp_source_disjunct.essplit_to_fwh8)
+    propagate_state(m.fs.discharge.fwh9_source_disjunct.bfp_to_fwh8)
+    m.fs.fwh[8].initialize(outlvl=outlvl,
+                           optarg=solver.options)
+    
+    # Initialize boiler
+    propagate_state(m.fs.discharge.condpump_source_disjunct.fwh9_to_boiler)
+    propagate_state(m.fs.discharge.fwh4_source_disjunct.fwh9_to_boiler)
+    propagate_state(m.fs.discharge.booster_source_disjunct.fwh9_to_boiler)
+    propagate_state(m.fs.discharge.bfp_source_disjunct.fwh9_to_boiler)
+    propagate_state(m.fs.discharge.fwh9_source_disjunct.essplit_to_boiler)
+    m.fs.boiler.initialize(outlvl=outlvl,
+                           optarg=solver.options)
+    
+    # Initialize discharge heat exchanger
+    propagate_state(m.fs.discharge.essplit_to_hxd)
+    m.fs.discharge.hxd.initialize(outlvl=outlvl,
+                                  optarg=optarg)
+    
+    # Deactivate IP-LP source turbine constraint for initialization
+    m.fs.discharge.iplp_source_disjunct.es_turbine.constraint_esturbine_temperature_out.deactivate()
+    
+    # Initialize IP-LP source turbine
+    propagate_state(m.fs.discharge.iplp_source_disjunct.hxd_to_esturbine)
+    
+    m.fs.discharge.iplp_source_disjunct.es_turbine.initialize(outlvl=outlvl,
+                                                              optarg=optarg)
+    
+    # Initialize turbine splitter 6
+    m.fs.turbine_splitter[6].initialize(outlvl=outlvl,
+                                        optarg=solver.options)
+    
+    # Initialize IP-LP source CCS splitter
+    propagate_state(m.fs.discharge.iplp_source_disjunct.t6split_to_ccsplit)
+    m.fs.discharge.iplp_source_disjunct.ccs_split.initialize(outlvl=outlvl,
+                                                              optarg=solver.options)
+    
+    # Initialize boiler feed pump turbine
+    propagate_state(m.fs.discharge.hxd_source_disjunct.t6split_to_bfpt)
+    propagate_state(m.fs.discharge.iplp_source_disjunct.bfptsplit_to_bfpt)
+    m.fs.bfpt.initialize(outlvl=outlvl,
+                         optarg=solver.options)
+    
+    # Deactivate CCS reboiler constraint for initialization
+    # m.fs.eq_reboiler_heat_duty.deactivate()
+    
+    # Initialize CCS reboiler
+    propagate_state(m.fs.discharge.hxd_source_disjunct.hxd_to_ccs)
+    propagate_state(m.fs.discharge.iplp_source_disjunct.ccsplit_to_ccs)
+    m.fs.ccs_reboiler.initialize(outlvl=outlvl,
+                                 optarg=solver.options)
+    
+    # Reactivate feed water heater and IP-LP source turbine constraints
+    for j in m.set_fwh:
+        m.fs.fwh[j].fwh_vfrac_constraint.activate()
+    m.fs.discharge.iplp_source_disjunct.es_turbine.constraint_esturbine_temperature_out.activate()
+    m.fs.eq_reboiler_heat_duty.activate()
+    
+    # Fix disjuncts for initialization
+    m.fs.discharge.condpump_source_disjunct.indicator_var.fix(False)
+    m.fs.discharge.fwh4_source_disjunct.indicator_var.fix(False)
+    m.fs.discharge.booster_source_disjunct.indicator_var.fix(False)
+    m.fs.discharge.bfp_source_disjunct.indicator_var.fix(False)
+    m.fs.discharge.fwh9_source_disjunct.indicator_var.fix(False)
+
+    m.fs.discharge.iplp_source_disjunct.indicator_var.fix(False)
+    m.fs.discharge.hxd_source_disjunct.indicator_var.fix(False)
+    
+    # Add options to GDPopt
+    m_init = m.clone()
+    m_init_var_names = [v for v in m_init.component_data_objects(Var)]
+    m_orig_var_names = [v for v in m.component_data_objects(Var)]
+
+    TransformationFactory("gdp.fix_disjuncts").apply_to(m_init)
+    
+    # Check and raise an error if the degrees of freedom are not 0
+    if not degrees_of_freedom(m_init) == 0:
+        raise ConfigurationError(
+            "The degrees of freedom after building the model are not 0. "
+            "You have {} degrees of freedom. "
+            "Please check your inputs to ensure a square problem "
+            "before initializing the model.".format(degrees_of_freedom(m))
+            )
+    
+    # Solve initialization
+    init_results = solver.solve(m_init, options=optarg)
+    print("Discharge model initialization solver termination = ",
+          init_results.solver.termination_condition)
+
+    for v1, v2 in zip(m_init_var_names, m_orig_var_names):
+        v2.value == v1.value
+
+
+    print("Discharge model initialization solver termination:",
+          init_results.solver.termination_condition)
+    print("*************   Discharge Model Initialized   ******************")
+
+
+def build_costing(m, solver=None):
+    """Add cost correlations for the storage design analysis
+
+    This function is used to estimate the capital and operating cost
+    of integrating a discharge storage system to the power plant and
+    it contains cost correlations to estimate: (i) the capital cost of
+    discharge heat exchanger and Solar salt pump, and (ii) the
+    operating costs for 1 year
+
+    """
+
+    ###########################################################################
+    # Add capital cost
+    # 1. Calculate discharge heat exchanger cost
+    # 2. Calculate Solar salt pump purchase cost
+    # 3. Calculate total capital cost of discharge system
+    
+    # Main assumptions
+    # 1. Salt life is assumed to outlast the plant life
+    # 2. The economic objective is to minimize total annualized cost. So, cash
+    # flows, discount rate, and NPV are not included in this study.
+    ###########################################################################
+    # Add capital cost: 1. Calculate discharge heat exchanger cost
+    ###########################################################################
+    # Calculate and initialize Solar salt discharge heat exchanger
+    # cost, which is estimated using the IDAES costing method with
+    # default options, i.e. a U-tube heat exchanger, stainless steel
+    # material, and a tube length of 12ft. Refer to costing
+    # documentation to change any of the default options. The purchase
+    # cost of heat exchanger has to be annualized when used
+    m.fs.costing = SSLWCosting()
+
+    m.fs.discharge.hxd.costing = UnitModelCostingBlock(
+        flowsheet_costing_block=m.fs.costing,
+        costing_method=SSLWCostingData.cost_heat_exchanger,
+    )
+
+    ###########################################################################
+    # Add capital cost: 2. Calculate Solar salt pump purchase cost
+    ###########################################################################
+    # Pump for moving Solar salt is not explicity modeled. To compute
+    # the capital costs for this pump the capital cost expressions are
+    # added below.  All cost expressions are from the same reference
+    # as the IDAES costing framework and is given below: Seider,
+    # Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons, Chapter
+    # 22. Cost Accounting and Capital Cost Estimation, Section 22.2 Cost
+    # Indexes and Capital Investment
+
+    # ---------- Solar salt ----------
+    # Calculate purchase cost of Solar salt pump
+    m.fs.discharge.spump_Qgpm = pyo.Expression(
+        expr=(m.fs.discharge.hxd.
+              hot_side.properties_in[0].flow_mass *
+              (264.17 * pyo.units.gallon / pyo.units.m**3) *
+              (60 * pyo.units.s / pyo.units.min) /
+              (m.fs.discharge.hxd.
+               hot_side.properties_in[0].dens_mass["Liq"])),
+        doc="Conversion of Solar salt flow mass to volumetric flow in gallons/min"
+    )
+    m.fs.discharge.dens_lbft3 = pyo.units.convert(
+        m.fs.discharge.hxd.hot_side.properties_in[0].dens_mass["Liq"],
+        to_units=pyo.units.pound / pyo.units.foot**3
+    )
+    m.fs.discharge.spump_sf = pyo.Expression(
+        expr=(m.fs.discharge.spump_Qgpm *
+              (m.fs.discharge.spump_head ** 0.5)),
+        doc="Pump size factor"
+    )
+    # Expression for pump base purchase cost
+    m.fs.discharge.pump_CP = pyo.Expression(
+        expr=(
+            m.fs.discharge.spump_FT * m.fs.discharge.spump_FM *
+            exp(
+                9.7171 -
+                0.6019 * log(m.fs.discharge.spump_sf) +
+                0.0519 * ((log(m.fs.discharge.spump_sf))**2)
+            )
+        ),
+        doc="Base purchase cost of Solar salt pump in $"
+    )
+    # Expression for pump efficiency
+    m.fs.discharge.spump_np = pyo.Expression(
+        expr=(
+            -0.316 +
+            0.24015 * log(m.fs.discharge.spump_Qgpm) -
+            0.01199 * ((log(m.fs.discharge.spump_Qgpm))**2)
+        ),
+        doc="Fractional efficiency of the pump in horsepower"
+    )
+    m.fs.discharge.motor_pc = pyo.Expression(
+        expr=(
+            (m.fs.discharge.spump_Qgpm *
+             m.fs.discharge.spump_head *
+             m.fs.discharge.dens_lbft3) /
+            (33000 *
+             m.fs.discharge.spump_np *
+             m.fs.discharge.spump_nm)
+        ),
+        doc="Power consumption of motor in horsepower"
+    )
+
+    # Defining a local variable for the log of motor's power consumption
+    # This will help writing the motor's purchase cost expressions conciesly
+    _log_motor_pc = log(m.fs.discharge.motor_pc)
+
+    # Expression for motor's purchase cost
+    m.fs.discharge.motor_CP = pyo.Expression(
+        expr=(
+            m.fs.discharge.spump_motorFT *
+            exp(
+                5.8259 +
+                0.13141 * _log_motor_pc +
+                0.053255 * (_log_motor_pc**2) +
+                0.028628 * (_log_motor_pc**3) -
+                0.0035549 * (_log_motor_pc**4)
+            )
+        ),
+        doc="Base cost of Solar salt pump's motor in $"
+    )
+
+    # Calculate and initialize total cost of Solar salt pump
+    m.fs.discharge.spump_purchase_cost = pyo.Var(
+        initialize=100000,
+        bounds=(0, 1e7),
+        doc="Total purchase cost of Solar salt pump in $"
+    )
+
+    def solar_spump_purchase_cost_rule(b):
+        return (
+            m.fs.discharge.spump_purchase_cost == (
+                m.fs.discharge.pump_CP +
+                m.fs.discharge.motor_CP) *
+            (m.CE_index / 394)
+        )
+    m.fs.discharge.spump_purchase_cost_eq = pyo.Constraint(
+        rule=solar_spump_purchase_cost_rule)
+
+    calculate_variable_from_constraint(
+        m.fs.discharge.spump_purchase_cost,
+        m.fs.discharge.spump_purchase_cost_eq)
+
+    ###########################################################################
+    # Add capital cost: 3. Calculate total capital cost for discharge system
+    ###########################################################################
+
+    # Add capital cost variable at flowsheet level to handle the Solar
+    # salt capital cost
+    m.fs.discharge.capital_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e10),
+        doc="Annualized capital cost in $/year")
+
+    # Calculate and initialize annualized capital cost for the Solar
+    # salt discharge storage system
+    def solar_cap_cost_rule(b):
+        return (
+            m.fs.discharge.capital_cost *
+            m.fs.discharge.num_of_years
+        ) == (m.fs.discharge.spump_purchase_cost +
+              m.fs.discharge.hxd.costing.capital_cost)
+    m.fs.discharge.cap_cost_eq = pyo.Constraint(
+        rule=solar_cap_cost_rule)
+
+    calculate_variable_from_constraint(
+        m.fs.discharge.capital_cost,
+        m.fs.discharge.cap_cost_eq)
+
+    ###########################################################################
+    #  Add operating cost
+    ###########################################################################
+    m.fs.discharge.operating_hours = pyo.Expression(
+        expr=365 * 3600 * m.fs.discharge.hours_per_day,
+        doc="Number of operating hours per year")
+    m.fs.discharge.operating_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e11),
+        doc="Operating cost in $/year")
+
+    def op_cost_rule(b):
+        return m.fs.discharge.operating_cost == (
+            m.fs.discharge.operating_hours *
+            m.fs.discharge.coal_price *
+            m.fs.coal_heat_duty * 1e6
+        )
+    m.fs.discharge.op_cost_eq = pyo.Constraint(rule=op_cost_rule)
+
+    # Initialize operating cost
+    calculate_variable_from_constraint(
+        m.fs.discharge.operating_cost,
+        m.fs.discharge.op_cost_eq)
+
+    # Check and raise an error if the degrees of freedom are not 0
+    if not degrees_of_freedom(m) == 0:
+        raise ConfigurationError(
+            "The degrees of freedom after building costing block are not 0. "
+            "You have {} degrees of freedom. "
+            "Please check your inputs to ensure a square problem "
+            "before initializing the model.".format(degrees_of_freedom(m))
+            )
+
+    # Solve cost initialization
+    
+    # Add options to GDPopt
+    m_init = m.clone()
+    m_init_var_names = [v for v in m_init.component_data_objects(Var)]
+    m_orig_var_names = [v for v in m.component_data_objects(Var)]
+
+    TransformationFactory("gdp.fix_disjuncts").apply_to(m_init)
+    
+    # Check and raise an error if the degrees of freedom are not 0
+    if not degrees_of_freedom(m_init) == 0:
+        raise ConfigurationError(
+            "The degrees of freedom after building the model are not 0. "
+            "You have {} degrees of freedom. "
+            "Please check your inputs to ensure a square problem "
+            "before initializing the model.".format(degrees_of_freedom(m))
+            )
+    
+    # Solve initialization
+    # Add options to NLP solver
+    optarg = {"tol": 1e-8,
+              "max_iter": 300}
+    cost_results = solver.solve(m_init, options=optarg)
+    print("Cost pre-initialization solver termination = ",
+          cost_results.solver.termination_condition)
+
+    for v1, v2 in zip(m_init_var_names, m_orig_var_names):
+        v2.value == v1.value
+    print()
+    print("Cost initialization solver termination:",
+          cost_results.solver.termination_condition)
+    print("******************** Costing Initialized *************************")
+    print()
+    print()
+
+
+def add_bounds(m, power_max=None):
+    """Add bounds to all units in discharge model
+
+    """
+
+    m.flow_max = m.main_flow * 3        # Units in mol/s
+    m.storage_flow_max = 0.2 * m.flow_max # Units in mol/s
+    m.salt_flow_max = 1000                # Units in kg/s
+    m.heat_duty_bound = 200e6             # Units in MW
+    m.power_max = power_max               # Units in MW
+
+    # Add bounds to Solar salt discharge heat exchanger
+    for hxd in [m.fs.discharge.hxd]:
+        hxd.tube_inlet.flow_mol.setlb(0)
+        hxd.tube_inlet.flow_mol.setub(m.storage_flow_max)
+        hxd.shell_inlet.flow_mass.setlb(0)
+        hxd.shell_inlet.flow_mass.setub(m.salt_flow_max)
+        hxd.tube_outlet.flow_mol.setlb(0)
+        hxd.tube_outlet.flow_mol.setub(m.storage_flow_max)
+        hxd.shell_outlet.flow_mass.setlb(0)
+        hxd.shell_outlet.flow_mass.setub(m.salt_flow_max)
+        hxd.shell_inlet.pressure.setlb(101320)
+        hxd.shell_inlet.pressure.setub(101330)
+        hxd.shell_outlet.pressure.setlb(101320)
+        hxd.shell_outlet.pressure.setub(101330)
+        hxd.heat_duty.setlb(0)
+        hxd.heat_duty.setub(m.heat_duty_bound)
+        hxd.shell.heat.setlb(-m.heat_duty_bound)
+        hxd.shell.heat.setub(0)
+        hxd.tube.heat.setlb(0)
+        hxd.tube.heat.setub(m.heat_duty_bound)
+        hxd.shell.properties_in[0].enth_mass.setlb(0)
+        hxd.shell.properties_in[0].enth_mass.setub(1.5e6)
+        hxd.shell.properties_out[0].enth_mass.setlb(0)
+        hxd.shell.properties_out[0].enth_mass.setub(1.5e6)
+        hxd.overall_heat_transfer_coefficient.setlb(0)
+        hxd.overall_heat_transfer_coefficient.setub(10000)
+        hxd.area.setlb(0)
+        hxd.area.setub(5000)
+        hxd.costing.pressure_factor.setlb(0)
+        hxd.costing.pressure_factor.setub(1e5)
+        hxd.costing.capital_cost.setlb(0)
+        hxd.costing.capital_cost.setub(1e7)
+        hxd.costing.base_cost_per_unit.setlb(0)
+        hxd.costing.base_cost_per_unit.setub(1e6)
+        hxd.costing.material_factor.setlb(0)
+        hxd.costing.material_factor.setub(10)
+        hxd.delta_temperature_in.setlb(10)
+        hxd.delta_temperature_out.setlb(9)
+        hxd.delta_temperature_in.setub(298)
+        hxd.delta_temperature_out.setub(500)
+
+    # Add bounds needed in units declared in condensate source
+    # disjunction
+    for split in [m.fs.discharge.es_split]:
+        split.inlet.flow_mol[:].setlb(0)
+        split.inlet.flow_mol[:].setub(m.flow_max)
+        split.to_hxd.flow_mol[:].setlb(0)
+        split.to_hxd.flow_mol[:].setub(m.storage_flow_max)
+        split.to_fwh.flow_mol[:].setlb(0)
+        split.to_fwh.flow_mol[:].setub(m.flow_max)
+        split.split_fraction[0.0, "to_hxd"].setlb(0)
+        split.split_fraction[0.0, "to_hxd"].setub(1)
+        split.split_fraction[0.0, "to_fwh"].setlb(0)
+        split.split_fraction[0.0, "to_fwh"].setub(1)
+
+    # m.fs.plant_power_out[0].setlb(300)
+    # m.fs.plant_power_out[0].setub(m.power_max)
+
+    # m.fs.turbine_splitter[6].split_fraction[0.0, "outlet_3"].setlb(0)
+    # m.fs.turbine_splitter[6].split_fraction[0.0, "outlet_3"].setub(1)
+    m.fs.turbine_splitter[6].outlet_3.flow_mol.setlb(0)
+    m.fs.turbine_splitter[6].outlet_3.flow_mol.setub(m.flow_max)
+
+    for unit_k in [m.fs.booster]:
+        unit_k.inlet.flow_mol[:].setlb(0)
+        unit_k.inlet.flow_mol[:].setub(m.flow_max)
+        unit_k.outlet.flow_mol[:].setlb(0)
+        unit_k.outlet.flow_mol[:].setub(m.flow_max)
+
+    for unit_k in [m.fs.discharge.iplp_source_disjunct.es_turbine]:
+        unit_k.inlet.flow_mol[:].setlb(0)
+        unit_k.inlet.flow_mol[:].setub(m.storage_flow_max)
+        unit_k.outlet.flow_mol[:].setlb(0)
+        unit_k.outlet.flow_mol[:].setub(m.storage_flow_max)
+
+    #     unit_k.inlet.pressure[:].setlb(0)
+    #     unit_k.inlet.pressure[:].setub(1e12)
+    #     unit_k.outlet.pressure[:].setlb(0)
+    #     unit_k.outlet.pressure[:].setub(1e12)
+    #     unit_k.ratioP[:].setlb(0)
+    #     unit_k.ratioP[:].setub(1e12)
+
+    for unit_k in [m.fs.ccs_reboiler]:
+        unit_k.inlet.flow_mol[:].setlb(0)
+        unit_k.inlet.flow_mol[:].setub(m.flow_max)
+        unit_k.outlet.flow_mol[:].setlb(0)
+        unit_k.outlet.flow_mol[:].setub(m.flow_max)
+
+    for k in m.set_turbine:
+        m.fs.turbine[k].work.setlb(-1e10)
+        m.fs.turbine[k].work.setub(0)
+
+    for unit_k in [m.fs.discharge.iplp_source_disjunct.es_turbine]:
+        unit_k.inlet.flow_mol[:].setlb(20)
+        unit_k.inlet.flow_mol[:].setub(m.storage_flow_max)
+        unit_k.outlet.flow_mol[:].setlb(20)
+        unit_k.outlet.flow_mol[:].setub(m.storage_flow_max)
+
+        unit_k.control_volume.work[:].setub(0)
+        unit_k.control_volume.work[:].setlb(-1e8)
+        # unit_k.inlet.pressure[:].setlb(0)
+        # unit_k.inlet.pressure[:].setub(1e12)
+        # unit_k.outlet.pressure[:].setlb(0)
+        # unit_k.outlet.pressure[:].setub(1e12)
+        unit_k.deltaP[:].setlb(0)
+        unit_k.deltaP[:].setub(1e12)
+
+    for split1 in [m.fs.discharge.iplp_source_disjunct.ccs_split]:
+        split1.inlet.flow_mol[:].setlb(0)
+        split1.inlet.flow_mol[:].setub(m.flow_max)
+        split1.to_ccs.flow_mol[:].setlb(0)
+        split1.to_ccs.flow_mol[:].setub(m.flow_max)
+        split1.to_turb.flow_mol[:].setlb(0)
+        split1.to_turb.flow_mol[:].setub(m.flow_max)
+        split1.split_fraction[0.0, "to_ccs"].setlb(0)
+        split1.split_fraction[0.0, "to_ccs"].setub(1)
+        split1.split_fraction[0.0, "to_turb"].setlb(0)
+        split1.split_fraction[0.0, "to_turb"].setub(1)
+
+    # for split1 in [m.fs.discharge.iplp_source_disjunct.bfpt_split]:
+    #     split1.inlet.flow_mol[:].setlb(0)
+    #     split1.inlet.flow_mol[:].setub(m.flow_max)
+    #     split1.to_ccs.flow_mol[:].setlb(0)
+    #     split1.to_ccs.flow_mol[:].setub(m.flow_max)
+    #     split1.to_bfpt.flow_mol[:].setlb(0)
+    #     split1.to_bfpt.flow_mol[:].setub(m.flow_max)
+    #     split1.split_fraction[0.0, "to_ccs"].setlb(0)
+    #     split1.split_fraction[0.0, "to_ccs"].setub(1)
+    #     split1.split_fraction[0.0, "to_bfpt"].setlb(0)
+    #     split1.split_fraction[0.0, "to_bfpt"].setub(1)
+
+    # for split2 in [m.fs.discharge.hxd_source_disjunct.ccs_split]:
+    #     split2.inlet.flow_mol[:].setlb(0)
+    #     split2.inlet.flow_mol[:].setub(m.storage_flow_max)
+    #     split2.to_ccs.flow_mol[:].setlb(0)
+    #     split2.to_ccs.flow_mol[:].setub(m.storage_flow_max)
+    #     split2.to_esturb.flow_mol[:].setlb(0)
+    #     split2.to_esturb.flow_mol[:].setub(m.storage_flow_max)
+    #     split2.split_fraction[0.0, "to_ccs"].setlb(0)
+    #     split2.split_fraction[0.0, "to_ccs"].setub(1)
+    #     split2.split_fraction[0.0, "to_esturb"].setlb(0)
+    #     split2.split_fraction[0.0, "to_esturb"].setub(1)
+
+
+def main(m_usc, solver=None, optarg=None):
+
+    # Add boiler and cycle efficiencies to the model
+    add_efficiency = True
+
+    # Add maximum power produced by power plant in MW. For this
+    # analysis, the maximum power is fixed to 436 MW
+    power_max = 436
+
+    # Create a flowsheet, add properties, unit models, and arcs
+    m = create_discharge_model(m_usc,
+                               add_efficiency=add_efficiency,
+                               power_max=power_max)
+
+    # Give all the required inputs to the model
+    set_model_input(m)
+
+    # Add scaling factor
+    set_scaling_factors(m)
+
+    # Initialize the model with a sequential initialization
+    initialize(m, solver=solver, optarg=optarg)
+
+    # Add cost correlations
+    build_costing(m, solver=solver)
+
+    # Unfix disjuncts after initialization
+    m.fs.discharge.condpump_source_disjunct.indicator_var.unfix()
+    m.fs.discharge.fwh4_source_disjunct.indicator_var.unfix()
+    m.fs.discharge.booster_source_disjunct.indicator_var.unfix()
+    m.fs.discharge.bfp_source_disjunct.indicator_var.unfix()
+    m.fs.discharge.fwh9_source_disjunct.indicator_var.unfix()
+
+    m.fs.discharge.iplp_source_disjunct.indicator_var.unfix()
+    m.fs.discharge.hxd_source_disjunct.indicator_var.unfix()
+
+    # Add bounds
+    add_bounds(m, power_max=power_max)
+
+    # Disconnect arcs to include discharge storage system
+    disconnect_arcs(m)
+
+    # Add disjunction
+    add_disjunction(m)
+
+    return m
+
+
+def print_model(_, nlp_model, nlp_data):
+    """Print the disjunction selected during the solution of the NLP
+    subproblem
+
+    """
+
+    nlp = nlp_model.fs.discharge
+    print('    ___________________________________________')
+    print('     Disjunction 1:')
+    if nlp.condpump_source_disjunct.binary_indicator_var.value == 1:
+        print('      Condensate from condenser pump is selected')
+    elif nlp.booster_source_disjunct.binary_indicator_var.value == 1:
+        print('      Condensate from booster pump is selected')
+    elif nlp.bfp_source_disjunct.binary_indicator_var.value == 1:
+        print('      Condensate from boiler feed pump is selected')
+    elif nlp.fwh9_source_disjunct.binary_indicator_var.value == 1:
+        print('      Condensate from FWH9 is selected')
+    elif nlp.fwh4_source_disjunct.binary_indicator_var.value == 1:
+        print('      Condensate from FWH4 is selected')
+    else:
+        print('      Error: There are no more alternatives')
+    print('    ___________________________________________')
+    print('     Disjunction 2:')
+    if nlp.iplp_source_disjunct.binary_indicator_var.value == 1:
+        print('      CCS Steam from IP LP crossover is selected')
+    elif nlp.hxd_source_disjunct.binary_indicator_var.value == 1:
+        print('      CCS Steam from HXD is selected')
+    else:
+        print('      Error: There are no more alternatives')
+    print('    ___________________________________________')
+    print()
+
+
+def run_nlps(m,
+             solver=None,
+             fluid=None,
+             source=None):
+    """This function fixes the indicator variables of the disjuncts so to
+    solve NLP problems
+
+    """
+
+    # Disjunction 1 for the water source selection
+    if fluid == "cond_pump":
+        m.fs.discharge.condpump_source_disjunct.indicator_var.fix(1)
+        m.fs.discharge.fwh4_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.booster_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.bfp_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh9_source_disjunct.indicator_var.fix(0)
+    elif fluid == "fwh4":
+        m.fs.discharge.condpump_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh4_source_disjunct.indicator_var.fix(1)
+        m.fs.discharge.booster_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.bfp_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh9_source_disjunct.indicator_var.fix(0)
+    elif fluid == "booster":
+        m.fs.discharge.condpump_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh4_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.booster_source_disjunct.indicator_var.fix(1)
+        m.fs.discharge.bfp_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh9_source_disjunct.indicator_var.fix(0)
+    elif fluid == "bfp":
+        m.fs.discharge.condpump_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh4_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.booster_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.bfp_source_disjunct.indicator_var.fix(1)
+        m.fs.discharge.fwh9_source_disjunct.indicator_var.fix(0)
+    elif fluid == "fwh9":
+        m.fs.discharge.condpump_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh4_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.booster_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.bfp_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.fwh9_source_disjunct.indicator_var.fix(1)
+    else:
+        print('Unrecognized storage fluid name!')
+
+    # Disjunction 2 for the ccs source selection
+    if source == "iplp":
+        m.fs.discharge.iplp_source_disjunct.indicator_var.fix(1)
+        m.fs.discharge.hxd_source_disjunct.indicator_var.fix(0)
+    elif source == "hxd":
+        m.fs.discharge.iplp_source_disjunct.indicator_var.fix(0)
+        m.fs.discharge.hxd_source_disjunct.indicator_var.fix(1)
+    else:
+        print('Unrecognized source unit name!')
+
+    TransformationFactory('gdp.fix_disjuncts').apply_to(m)
+    print("The degrees of freedom after gdp transformation ",
+          degrees_of_freedom(m))
+
+    results = solver.solve(
+        m,
+        tee=True,
+        symbolic_solver_labels=True,
+        options={
+            "linear_solver": "ma27",
+            "max_iter": 150
+        }
+    )
+    log_close_to_bounds(m)
+
+
+    return m, results
+
+def run_gdp(m):
+    """Declare solver GDPopt and its options
+    """
+
+    # Add options to GDPopt
+    opt = SolverFactory('gdpopt')
+    _prop_bnds_root_to_leaf_map[ExternalFunctionExpression] = lambda x, y, z: None
+
+    # Solve model
+    results = opt.solve(
+        m,
+        tee=True,
+        algorithm='LOA',
+        init_algorithm="no_init",
+        subproblem_presolve=False,
+        mip_solver='gurobi',
+        nlp_solver='ipopt',
+        call_after_subproblem_solve=print_model,
+        nlp_solver_args=dict(
+            tee=True,
+            options={
+                "max_iter": 150}
+        )
+    )
+
+    return results
+
+
+def print_results(m, results):
+
+    print('====================================================')
+    print('Results ')
+    print()
+    print('Obj (M$/year): {:.2f}'.format(
+        (pyo.value(m.obj) / scaling_obj) * 1e-6))
+    print('Discharge capital cost (M$/y): {:.2f}'.format(
+        pyo.value(m.fs.discharge.capital_cost) * 1e-6))
+    print('Net Power (MW): {:.2f}'.format(
+        pyo.value(m.fs.net_power)))
+    print('Plant Power (MW): {:.2f}'.format(
+        pyo.value(m.fs.plant_power_out[0])))
+    print('Discharge Turbine Power (MW): {:.2f}'.format(
+        pyo.value(m.fs.discharge.iplp_source_disjunct.es_turbine.control_volume.work[0]) * (-1e-6)))
+    print('Boiler Efficiency (%): {:.2f}'.format(
+        pyo.value(m.fs.boiler_efficiency) * 100))
+    print('Boiler Steam Flow: {:.2f}'.format(
+        pyo.value(m.fs.boiler.inlet.flow_mol[0])))
+    print('Makeup Water Flow: {:.2f}'.format(
+        pyo.value(m.fs.condenser_mix.makeup.flow_mol[0])))
+    print('CCS Reboiler Steam Flow: {:.2f}'.format(
+        pyo.value(m.fs.ccs_reboiler.inlet.flow_mol[0])))
+    print('Flue Gas Flow: {:.2f}'.format(
+        pyo.value(m.fs.fg_flow_mol[0])))
+    print('CO2 captured: {:.2f}'.format(
+        pyo.value(m.fs.co2_captured)))
+    print('CCS Inlet Steam Temperature: {:.2f}'.format(
+        pyo.value(m.fs.ccs_reboiler.control_volume.properties_in[0].temperature)))
+    print('CCS Outlet Steam Temperature: {:.2f}'.format(
+        pyo.value(m.fs.ccs_reboiler.control_volume.properties_out[0].temperature)))
+    print('HXD Inlet Water Temperature: {:.2f}'.format(
+        pyo.value(m.fs.discharge.hxd.cold_side.properties_in[0].temperature)))
+    print('HXD Outlet Water Temperature: {:.2f}'.format(
+        pyo.value(m.fs.discharge.hxd.cold_side.properties_out[0].temperature)))
+    print()
+    print("**Discrete design decisions (Disjunction)")
+    for d in m.component_data_objects(ctype=Disjunct,
+                                      active=True,
+                                      sort=True, descend_into=True):
+        if abs(d.binary_indicator_var.value - 1) < 1e-6:
+            print(d.name, ' should be selected!')
+    print('Discharge heat exchanger area (m2): {:.2f}'.format(
+        pyo.value(m.fs.discharge.hxd.area)))
+    print('Discharge heat exchanger heat duty (MW): {:.2f}'.format(
+        pyo.value(m.fs.discharge.hxd.heat_duty[0]) * 1e-6))
+    print('====================================================')
+    print()
+    print('Solver details')
+    print(results)
+    print()
+
+
+def add_flugas_calculations(m):
+    m.fs.co2_mw = Param(
+        initialize=44.01,
+        doc='molar weight of CO2 in g/mol'
+    )
+    m.fs.SR = Param(
+        initialize=1.2,
+        doc='Stoichiometric ratio used to calculate coal flow rate'
+    )
+    m.fs.coal_LHV = Param(
+        initialize=29544,
+        doc='LHV for Illinois no. 6 Bituminous coal from Baseline Rev 4, J/g'
+    )
+    m.fs.carbon_wt = Param(
+        initialize=0.4972,
+        doc='Carbon in dry weight basis of the coal from Baseline Rev 4'
+    )
+    m.fs.co2_molefrac = Param(
+        initialize=0.14,
+        doc='Mole fraction of CO2 in the flue gas of USC, 1999 DOE report'
+    )
+    m.fs.fg_flow_mol = Var(
+        m.fs.config.time,
+        initialize=1000,
+        bounds=(0, 1e6),
+        doc='Molar flow rate of flue gas')
+
+    @m.fs.Constraint(m.fs.time,
+                     doc="Mole flow of flue gas constraint")
+    def eq_fluegas_flow(b, t):
+        return (
+            m.fs.fg_flow_mol[t] * (m.fs.co2_mw *
+                                   m.fs.coal_LHV *
+                                   m.fs.co2_molefrac) ==
+            (m.fs.carbon_wt * m.fs.SR * (m.fs.reheater[2].heat_duty[t] +
+                                          m.fs.reheater[1].heat_duty[t] +
+                                          m.fs.boiler.heat_duty[t]))
+            )
+    
+    return m
+    
+def add_capture_calculations(m):
+
+    m.fs.fg_to_ccs_splitfraction = Var(
+        m.fs.config.time,
+        initialize=0.5,
+        bounds=(0, 1),
+        doc='Split of total flue gas sent to CCS')
+    
+    # m.fs.co2_captured = Var(
+    #     m.fs.config.time,
+    #     initialize=500,
+    #     bounds=(0, 1e5),
+    #     doc='Weight of CO2 captured CCS')
+
+    # A specific reboiler duty of 3 GJ/tonne of CO2 captured is assumed for the
+    # CCS unit performing at 95% capture rate and using an advanced solvent
+    # such as Gen2
+    # Units: 3 GJ/tonne = 3000 J/g
+    m.fs.SRD = Param(
+        initialize=3000,
+        doc='Specific Reboiler Duty for 95% Capture in J/g CO2 captured'
+    )
+
+    m.fs.emission_tax = Param(
+        initialize=150e-6,
+        doc='Assumed carbon emission tax of $150/tonne or $150e-6/g of CO2'
+    )
+
+    m.fs.ccs_reboiler = Heater(
+        property_package=m.fs.prop_water,
+        has_pressure_change=True,
+    )
+
+    m.fs.co2_captured = Expression(
+        expr=(m.fs.co2_molefrac * m.fs.fg_flow_mol[0]
+        * m.fs.fg_to_ccs_splitfraction[0] * m.fs.co2_mw),
+        doc="Weight of CO2 captured")
+
+    m.fs.co2_emitted = Expression(
+        expr=(m.fs.co2_molefrac * m.fs.fg_flow_mol[0]
+        * (1 - m.fs.fg_to_ccs_splitfraction[0]) * m.fs.co2_mw),
+        doc="Weight of CO2 captured")
+
+    # @m.fs.Constraint(m.fs.time,
+    #                  doc="Weight of CO2 captured")
+    # def eq_min_co2_captured(b, t):
+    #     return (
+    #         m.fs.fg_to_ccs_splitfraction[t] >= 0.5)
+
+    @m.fs.Constraint(m.fs.time,
+                     doc="Reboiler heat duty constraint")
+    def eq_reboiler_heat_duty(b, t):
+        return (
+            m.fs.ccs_reboiler.heat_duty[t] ==
+            -1 * m.fs.co2_captured * m.fs.SRD)
+
+    return m
+    
+def model_analysis(m, heat_duty=None):
+    """Solve the conceptual design optimization problem
+
+    """
+
+    # Fix variables in the flowsheet
+    # m.fs.net_power.fix(400)
+    m.fs.plant_power_out.fix(400)
+    m.fs.boiler.outlet.pressure.fix(m.main_steam_pressure)
+    m.fs.discharge.hxd.heat_duty.fix(heat_duty * 1e6)
+
+    # Unfix variables that were fixed iduring initialization
+    m.fs.boiler.inlet.flow_mol.unfix()
+    m.fs.discharge.es_split.split_fraction[0, "to_hxd"].unfix()
+    m.fs.discharge.es_split.inlet.unfix()
+    m.fs.discharge.hxd.shell_inlet.flow_mass.unfix()
+    m.fs.discharge.hxd.area.unfix()
+
+    # m.fs.fg_to_ccs_splitfraction[:].fix(1)
+    m.fs.fg_to_ccs_splitfraction[:].unfix()
+    m.fs.ccs_reboiler.inlet.pressure[0].unfix()
+    m.fs.ccs_reboiler.inlet.enth_mol[0].unfix()
+    m.fs.ccs_reboiler.inlet.flow_mol[0].unfix()
+    # m.fs.ccs_reboiler.inlet.enth_mol[0].fix(3000)
+    # m.fs.ccs_reboiler.outlet.enth_mol[0].unfix()
+    # m.fs.discharge_power_out.fix(10)
+    # Add total cost as the objective function
+    m.obj = Objective(
+        expr=(
+            m.fs.discharge.capital_cost +
+            m.fs.discharge.operating_cost +
+            m.fs.co2_emitted * m.fs.discharge.operating_hours * m.fs.emission_tax
+            # - m.fs.net_power * 20
+        ) * scaling_obj
+    )
+
+
+if __name__ == "__main__":
+
+    # optarg = {"max_iter": 300}
+    optarg = {"tol": 1e-8,
+              "max_iter": 300,
+              "halt_on_ampl_error": "yes"}
+    solver = get_solver('ipopt', optarg)
+
+    # Build ultra-supercritical plant base model
+    m_usc = usc.build_plant_model()
+
+    # Initialize ultra-supercritical plant base model
+    usc.initialize(m_usc)
+
+    # Add boiler fireside
+    m_usc = add_flugas_calculations(m_usc)
+    m_usc = add_capture_calculations(m_usc)
+
+    # Build discharge model
+    m = main(m_usc, solver=solver, optarg=optarg)
+    
+    # Solve design model optimization problem
+    heat_duty_data = 148.5
+    model_analysis(m, heat_duty=heat_duty_data)
+    
+    # Solve model using GDPopt
+    print()
+    print('**********Start solution of GDP discharge model using GDPopt')
+    print('DOFs before GDP discharge model solution: ', degrees_of_freedom(m))
+    print()
+    # results = run_gdp(m)
+
+    # fluid = "cond_pump"
+    # fluid = "fwh4"
+    # fluid = "booster"
+    fluid = "bfp"
+    # fluid = "fwh9"
+
+    source = "iplp"
+    # source = "hxd"
+    
+    results = run_nlps(m,
+                        solver=solver,
+                        fluid=fluid,
+                        source=source)
+    
+    # Print results
+    print_results(m, results)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/ultra_supercritical_powerplant_w_ccs.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/ultra_supercritical_powerplant_w_ccs.py
new file mode 100644
index 000000000..a0f96809f
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/fe_ccs/ultra_supercritical_powerplant_w_ccs.py
@@ -0,0 +1,388 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program
+# (DISPATCHES), and is copyright (c) 2021 by the software owners:
+# The Regents of the University of California, through Lawrence Berkeley
+# National Laboratory, National Technology & Engineering Solutions of Sandia,
+# LLC, Alliance for Sustainable Energy, LLC, Battelle Energy Alliance, LLC,
+# University of Notre Dame du Lac, et al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and
+# license information, respectively. Both files are also available online
+# at the URL: "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+"""
+This is a simple model for an ultrasupercritical coal-fired power plant
+integrated with a boiler fire-side unit model and a CO2 capture system.
+The boiler fire-side model accounts for flue-gas generation.
+The CO2 capture system uses surrogates for a solvent-based piperazine system.
+The capture rate is fixed to 90%.
+"""
+
+from pyomo.network import Arc
+from pyomo.environ import TransformationFactory, value
+# IDAES Imports
+from idaes.core.util.model_statistics import degrees_of_freedom
+from idaes.core.util.initialization import propagate_state
+from idaes.models_extra.power_generation.unit_models.boiler_fireside import BoilerFireside
+from idaes.models.unit_models import Heater, MomentumMixingType
+from idaes.core.solvers import get_solver
+import idaes.core.util.scaling as iscale
+import idaes.logger as idaeslog
+from idaes.models_extra.power_generation.unit_models.helm import HelmMixer, HelmSplitter
+from idaes.models_extra.power_generation.properties import FlueGasParameterBlock
+# Dispatches Imports
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant import (
+    ultra_supercritical_powerplant as usc)
+# from idaes.power_generation.carbon_capture.piperazine_surrogates.\
+#     co2_capture_system import CO2Capture
+from co2_capture_system import CO2Capture
+
+
+def add_fireside(m):
+
+    m.fs.prop_fluegas = FlueGasParameterBlock()
+
+    boiler_input_dict = {1: '614623556',  # replaced later by boiler
+                         'pl': '126983008',  # replaced later by reheater[1]
+                         'roof': '96959378',  # replaced later by reheater[2]
+                         'flyash': '0.0001',  # flyash mass fraction
+                         'NOx': '140'}  # NOx PPM
+
+    m.fs.boiler_fireside = BoilerFireside(
+        dynamic=False,
+        property_package=m.fs.prop_fluegas,
+        calculate_PA_SA_flows=True,
+        number_of_zones=1,
+        has_platen_superheater=True,
+        has_roof_superheater=True,
+        surrogate_dictionary=boiler_input_dict)
+    m.fs.boiler_fireside.eq_surr_waterwall_heat.deactivate()
+    m.fs.boiler_fireside.eq_surr_platen_heat.deactivate()
+    m.fs.boiler_fireside.eq_surr_roof_heat.deactivate()
+
+    @m.fs.boiler_fireside.Constraint(m.fs.time,
+                                     m.fs.boiler_fireside.zones,
+                                     doc="Waterwall heat duty")
+    def eq_main_boiler_heat(b, t, z):
+        return (
+            b.waterwall_heat[t, z] *
+            b.fcorrection_heat_ww[t] ==
+            m.fs.boiler.heat_duty[0])
+
+    @m.fs.boiler_fireside.Constraint(m.fs.time,
+                                     doc="Reheater heat duty")
+    def eq_reheater_1_heat(b, t):
+        return (
+            b.platen_heat[t] *
+            b.fcorrection_heat_platen[t] ==
+            m.fs.reheater[1].heat_duty[0])
+
+    @m.fs.boiler_fireside.Constraint(m.fs.time,
+                                     doc="Additional reheater heat duty")
+    def eq_reheater_2_heat(b, t):
+        return (
+            b.roof_heat[t] *
+            b.fcorrection_heat_ww[t] ==
+            m.fs.reheater[2].heat_duty[0])
+
+    # Fixing Coal composition
+    m.fs.boiler_fireside.mf_C_coal_dry.fix(0.718471768285811)
+    m.fs.boiler_fireside.mf_H_coal_dry.fix(0.0507156542319396)
+    m.fs.boiler_fireside.mf_O_coal_dry.fix(0.0791164206018258)
+    m.fs.boiler_fireside.mf_N_coal_dry.fix(0.0140876817310943)
+    m.fs.boiler_fireside.mf_S_coal_dry.fix(0.0282880649160374)
+    m.fs.boiler_fireside.mf_Ash_coal_dry.fix(0.109320410233292)
+    m.fs.boiler_fireside.hhv_coal_dry.fix(2.581e+007)
+    m.fs.boiler_fireside.frac_moisture_vaporized[:].fix(0.6)
+    m.fs.boiler_fireside.mf_H2O_coal_raw[:].fix(0.111367051)  # moisture
+    m.fs.boiler_fireside.flowrate_coal_raw[:].fix(100.0)  # kg/s
+
+    m.fs.boiler_fireside.wall_temperature_waterwall[:, :].fix(690)
+    m.fs.boiler_fireside.wall_temperature_platen[:].fix(750)
+    m.fs.boiler_fireside.wall_temperature_roof[:].fix(650)
+    m.fs.boiler_fireside.fcorrection_heat_ww.fix(0.95)
+    m.fs.boiler_fireside.fcorrection_heat_platen.fix(0.95)
+
+    # -----------------------------------------------------------
+    # Estimates for unit model initialization only
+    # -----------------------------------------------------------
+    # Approximated flue gas = 21290.6999  # mol/s
+    flow_mol_pa = 21290.6999*0.34  # approx. 1/3 as Primary air
+    flow_mol_sa = 21290.6999*0.66  # approx. 2/3 as Secondary air
+
+    m.fs.state_args_PA = {
+        "flow_mol_comp": {
+            "H2O": 0.0078267*flow_mol_pa,
+            "CO2": 0.000337339*flow_mol_pa,
+            "N2": 0.783994*flow_mol_pa,
+            "O2": 0.20784*flow_mol_pa,
+            "SO2": 1e-5*flow_mol_pa,
+            "NO": 1e-5*flow_mol_pa
+        },
+        "temperature": 333.15,
+        "pressure": 101325.00
+    }
+
+    m.fs.state_args_SA = {
+        "flow_mol_comp": {
+            "H2O": 0.0078267*flow_mol_sa,
+            "CO2": 0.000337339*flow_mol_sa,
+            "N2": 0.783994*flow_mol_sa,
+            "O2": 0.20784*flow_mol_sa,
+            "SO2": 1e-5*flow_mol_sa,
+            "NO": 1e-5*flow_mol_sa
+        },
+        "temperature": 650.15,
+        "pressure": 101325.00
+    }
+    # -----------------------------------------------------------
+
+    m.fs.boiler_fireside.primary_air_inlet.pressure[:].fix(101325.00)
+    m.fs.boiler_fireside.secondary_air_inlet.pressure[:].fix(101325.00)
+    m.fs.boiler_fireside.primary_air_inlet.temperature[:].fix(333.15)
+    m.fs.boiler_fireside.secondary_air_inlet.temperature[:].fix(650.15)
+    m.fs.boiler_fireside.temperature_coal[:].fix(335.15)
+    m.fs.boiler_fireside.flue_gas_outlet.temperature.setub(5000)
+    m.fs.boiler_fireside.SR.fix(1.2)
+    m.fs.boiler_fireside.ratio_PA2coal.fix(2.45)
+    m.fs.boiler_fireside.SR_lf.fix(1.0)
+    m.fs.boiler_fireside.deltaP.fix(1000)
+    return m
+
+
+def add_co2capture(m):
+    m.fs.co2_capture_unit = CO2Capture()
+
+    # Adding constraints to connect flue_gas_outlet to co2_capture_unit_inlet
+    # An arc cannot be used because of the fixed components with capture unit
+    # The following are equality constraints for component flows, pressure,
+    # and temperature.
+    m.fg_comp_list = ['CO2', 'H2O', 'N2', 'O2', 'NO', 'SO2']
+    m.css_comp_list = ['CO2', 'H2O', 'N2', 'O2']
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      m.css_comp_list,
+                                      doc="Flow equality constraints")
+    def eq_flow_mol_comp(b, t, c):
+        return (
+            m.fs.boiler_fireside.flue_gas_outlet.flow_mol_comp[t, c] ==
+            m.fs.co2_capture_unit.inlet.flow_mol_comp[t, c])
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      doc="Fixing Ar flow")
+    def eq_ar_flow_mol_comp(b, t):
+        return (
+            m.fs.co2_capture_unit.inlet.flow_mol_comp[t, 'Ar'] == 0.0089 *
+            sum(m.fs.boiler_fireside.flue_gas_outlet.flow_mol_comp[t, c]
+                for c in m.fg_comp_list))
+
+    # The Flue gas is assumed to be stripped out of NOx and SOx before capture
+    # The inlet to the CO2 capture unit the flue gas temperature is at 303.15 K
+    m.fs.co2_capture_unit.inlet.temperature.fix(303.15)  # K
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      doc="Pressure equality constraints")
+    def eq_pressure(b, t):
+        return (
+            m.fs.boiler_fireside.flue_gas_outlet.pressure[t] ==
+            m.fs.co2_capture_unit.inlet.pressure[t])
+
+    m.fs.co2_capture_unit.CO2_capture_rate.fix(0.9)  # 90 % CO2 Capture
+    m.fs.co2_capture_unit.Pz_mol.fix(5)
+    m.fs.co2_capture_unit.lean_loading.fix(0.25)
+
+    # Add a dummy heater block to extract steam for specific reformer duty
+    m.fs.ccs_reformer = Heater(
+        dynamic=False,
+        property_package=m.fs.prop_water,
+        has_pressure_change=True
+    )
+    # Add a splitter to take the main steam from boiler for CO2 Capture
+    m.fs.ccs_splitter = HelmSplitter(
+        property_package=m.fs.prop_water
+    )
+    # Add a mixer to add the ccs exhaust to condenser
+    m.fs.ccs_mix = HelmMixer(
+        momentum_mixing_type=MomentumMixingType.minimize,
+        inlet_list=["bfpt", "ccs"],
+        property_package=m.fs.prop_water,
+    )
+
+    # Add constraint to equate the co2 capture reformer duty with ccs heater
+    @m.fs.Constraint(m.fs.time,
+                      doc="Reformer duty equality constraint")
+    def eq_reformerduty(b, t):
+        return (
+            0 == m.fs.ccs_reformer.heat_duty[t] +
+            m.fs.co2_capture_unit.reboiler_duty[t]*1e6)
+
+    @m.fs.Constraint(m.fs.time)
+    def constraint_reformer_out_pressure(b, t):
+        return (
+            b.ccs_reformer.control_volume.properties_out[t].pressure ==
+            b.condenser_mix.main_state[t].pressure
+        )
+
+    @m.fs.Constraint(m.fs.time)
+    def constraint_reformer_out_enthalpy(b, t):
+        return (
+            b.ccs_reformer.control_volume.properties_out[t].enth_mol ==
+            b.ccs_reformer.control_volume.properties_out[t].
+            enth_mol_sat_phase["Liq"]
+        )
+
+    # Deactivate the connection from boiler to Turbine 1 and add splitter
+    # Also, deactvate the bfpt outlet to condesner to add a mixer
+    # for ccs sexhaust steam#
+    for arc_s in [m.fs.boiler_to_turb1, m.fs.bfpt_to_condmix]:
+        arc_s.expanded_block.enth_mol_equality.deactivate()
+        arc_s.expanded_block.flow_mol_equality.deactivate()
+        arc_s.expanded_block.pressure_equality.deactivate()
+
+    m.fs.boiler_to_ccsplitter = Arc(
+        source=m.fs.boiler.outlet,
+        destination=m.fs.ccs_splitter.inlet,
+        doc="Connection from boiler to ccs splitter"
+    )
+    m.fs.ccsplitter_to_turb1 = Arc(
+        source=m.fs.ccs_splitter.outlet_1,
+        destination=m.fs.turbine[1].inlet,
+        doc="Connection from boiler to ccs splitter"
+    )
+    m.fs.ccsplitter_to_capture = Arc(
+        source=m.fs.ccs_splitter.outlet_2,
+        destination=m.fs.ccs_reformer.inlet,
+        doc="Connection from boiler to ccs splitter"
+    )
+    # add ccs exhaust steam to condenser
+    m.fs.bfpt_to_ccsmix = Arc(
+        source=m.fs.bfpt.outlet,
+        destination=m.fs.ccs_mix.bfpt
+    )
+    m.fs.capture_to_ccsmix = Arc(
+        source=m.fs.ccs_reformer.outlet,
+        destination=m.fs.ccs_mix.ccs
+    )
+    m.fs.ccsmix_to_condmix = Arc(
+        source=m.fs.ccs_mix.outlet,
+        destination=m.fs.condenser_mix.bfpt
+    )
+    TransformationFactory("network.expand_arcs").apply_to(m.fs)
+
+    return m
+
+
+def initialize_usc_w_capture(m, fileinput=None, outlvl=idaeslog.NOTSET,
+                             solver=None, optarg={}):
+
+    iscale.calculate_scaling_factors(m)
+
+    m.fs.boiler.heat_duty[0].fix()
+    m.fs.reheater[1].heat_duty[0].fix()
+    m.fs.reheater[2].heat_duty[0].fix()
+
+    m.fs.boiler_fireside.initialize(
+        state_args_PA=m.fs.state_args_PA,
+        state_args_SA=m.fs.state_args_SA)
+
+    m.fs.boiler.heat_duty[0].unfix()
+    m.fs.reheater[1].heat_duty[0].unfix()
+    m.fs.reheater[2].heat_duty[0].unfix()
+
+    # The initialize method in CO2Capture unit model fixes inlet state
+    # and does not unfix it. So, to use the initialize method, the
+    # constraints are deactivated before initializing and activated later.
+    # The inlet state is unfixed before activating the constraints.
+    # TODO: update this section when the initialize method in CO2Capture model
+    # is updated.
+    m.fs.co2_capture_unit.eq_flow_mol_comp.deactivate()
+    m.fs.co2_capture_unit.eq_ar_flow_mol_comp.deactivate()
+    m.fs.co2_capture_unit.eq_pressure.deactivate()
+    m.fs.co2_capture_unit.inlet.pressure[:].fix(101325)  # Pa (1 atm)
+
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[0, 'CO2'].fix(5328)
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[0, 'H2O'].fix(3138)
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[0, 'Ar'].fix(341)
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[0, 'O2'].fix(1256)
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[0, 'N2'].fix(28524)
+
+    m.fs.co2_capture_unit.initialize(outlvl=idaeslog.INFO)
+
+    m.fs.co2_capture_unit.inlet.pressure[:].unfix()  # Pa (1 atm)
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'CO2'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'O2'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'Ar'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'H2O'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'N2'].unfix()
+    m.fs.co2_capture_unit.eq_flow_mol_comp.activate()
+    m.fs.co2_capture_unit.eq_ar_flow_mol_comp.activate()
+    m.fs.co2_capture_unit.eq_pressure.activate()
+
+    propagate_state(m.fs.boiler_to_ccsplitter)
+    m.fs.ccs_splitter.split_fraction[:, "outlet_2"].fix(0.24)
+    m.fs.ccs_splitter.initialize()
+    m.fs.ccs_splitter.split_fraction[:, "outlet_2"].unfix()
+
+    propagate_state(m.fs.ccsplitter_to_capture)
+    m.fs.constraint_reformer_out_pressure.deactivate()
+    m.fs.eq_reformerduty.deactivate()
+    m.fs.constraint_reformer_out_enthalpy.deactivate()
+    m.fs.ccs_reformer.heat_duty[0].fix(-853190892.2399481)
+    m.fs.ccs_reformer.outlet.pressure[:].fix(6000)
+    m.fs.ccs_reformer.inlet.flow_mol.fix(14541.6)
+    m.fs.ccs_reformer.initialize()
+
+    m.fs.eq_reformerduty.activate()
+    m.fs.constraint_reformer_out_pressure.activate()
+    m.fs.constraint_reformer_out_enthalpy.activate()
+    m.fs.ccs_reformer.outlet.pressure[:].unfix()
+    m.fs.ccs_reformer.heat_duty[0].unfix()
+    m.fs.ccs_reformer.inlet.flow_mol.unfix()
+
+    propagate_state(m.fs.bfpt_to_ccsmix)
+    propagate_state(m.fs.capture_to_ccsmix)
+    m.fs.ccs_mix.initialize()
+
+    propagate_state(m.fs.ccsmix_to_condmix)
+    m.fs.condenser_mix.initialize()
+
+    # Increasing the flow through boiler to account for parasitic power
+    # required for CO2 capture
+    m.fs.boiler.inlet.flow_mol.fix(m.main_flow*2.015)
+    
+    res = solver.solve(m)
+
+    print("Model Initialization = ",
+          res.solver.termination_condition)
+    print("**************  USC model w Capture Initialized   ***************")
+
+
+def build_usc_w_ccs(solver):
+
+    m = usc.build_plant_model()
+    usc.initialize(m)
+    m = add_fireside(m)
+    m = add_co2capture(m)
+    assert degrees_of_freedom(m) == 0
+
+    initialize_usc_w_capture(m, solver=solver)
+    assert degrees_of_freedom(m) == 0
+
+    return m
+
+
+if __name__ == "__main__":
+
+    optarg = {
+        "max_iter": 300,
+        "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver("ipopt", optarg)
+
+    m = build_usc_w_ccs(solver)
+
+    results = solver.solve(m, tee=True)
+    print('Plant Power (MW) =', value(m.fs.plant_power_out[0]))
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/gdp_multiperiod_usc_pricetaker_unfixed_area.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/gdp_multiperiod_usc_pricetaker_unfixed_area.py
new file mode 100644
index 000000000..671bfa847
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/gdp_multiperiod_usc_pricetaker_unfixed_area.py
@@ -0,0 +1,317 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2021 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+"""This script uses the IDAES multiperiod class to create a steady
+state GDP multiperiod model for the integrated ultra-supercritical
+power plant GDP model. The purpose of this script is to create a GDP
+multiperiod model that can be use for market analysis using a
+pricetaker assumption. The integrated storage with ultra-supercritical
+power plant model is used a steady state model for creating the
+multiperiod model.
+
+"""
+
+import json
+
+import pyomo.environ as pyo
+from pyomo.environ import units as pyunits
+from pyomo.environ import (Constraint, NonNegativeReals, Var)
+
+from idaes.apps.grid_integration.multiperiod.multiperiod import MultiPeriodModel
+from idaes.core.util.model_statistics import degrees_of_freedom
+import idaes.core.util.scaling as iscale
+from idaes.core.solvers.get_solver import get_solver
+
+
+__author__ = "Soraya Rawlings"
+
+# Use GDP design for charge and discharge heat exchanger from 4-12
+# disjunctions model when True. If False, use the GDP design from 4-5
+# disjunctions model.
+new_design = True
+
+if new_design:
+    print('>>>>> Solving for new storage design')
+    import usc_storage_gdp_mp_unfixed_area_new_storage_design as usc_gdp
+    # Add design data from .json file
+    data_path = 'uscp_design_data_new_storage_design.json'
+else:
+    print('>>>>> Solving for original storage design')
+    import usc_storage_gdp_mp_unfixed_area as usc_gdp
+    # Add design data from .json file
+    data_path = 'uscp_design_data.json'
+
+
+with open(data_path) as design_data:
+    design_data_dict = json.load(design_data)
+
+min_power = design_data_dict["plant_min_power"] # in MW
+max_power = design_data_dict["plant_max_power"] # in MW
+
+
+def create_ss_model():
+
+    optarg = {
+        "max_iter": 300,
+        # "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver('ipopt', optarg)
+
+    # Add options to model
+    deact_arcs_after_init = True # needed for GDP model
+    method = "with_efficiency" # adds boiler and cycle efficiencies
+    load_init_file = False
+
+    # Add data from .json data file
+    cold_salt_temp = design_data_dict["cold_salt_temperature"] # in K
+    min_storage_heat_duty = design_data_dict["min_storage_heat_duty"] # in MW
+    max_storage_heat_duty = design_data_dict["max_storage_heat_duty"] # in MW
+    path_init_file = design_data_dict["gdp_init_file_path"]
+
+    m = pyo.ConcreteModel()
+    m.usc = usc_gdp.main(method=method,
+                         max_power=max_power,
+                         load_init_file=load_init_file,
+                         path_init_file=path_init_file,
+                         deact_arcs_after_init=deact_arcs_after_init,
+                         solver=solver)
+
+
+    # Set bounds for power produced by the plant alone
+    m.usc.fs.plant_min_power_eq = pyo.Constraint(
+        expr=m.usc.fs.plant_power_out[0] >= min_power
+    )
+    m.usc.fs.plant_max_power_eq = pyo.Constraint(
+        expr=m.usc.fs.plant_power_out[0] <= max_power
+    )
+
+    # Set bounds in charge and discharge heat exchangers
+    charge_mode = m.usc.fs.charge_mode_disjunct
+    discharge_mode = m.usc.fs.discharge_mode_disjunct
+    hxc_heat_duty = (1e-6) * (pyunits.MW / pyunits.W) * charge_mode.hxc.heat_duty[0]
+    hxd_heat_duty = (1e-6) * (pyunits.MW / pyunits.W) * discharge_mode.hxd.heat_duty[0]
+    m.usc.fs.charge_mode_disjunct.storage_heat_duty_lb = pyo.Constraint(
+        expr=hxc_heat_duty >= min_storage_heat_duty + 40
+    )
+    m.usc.fs.discharge_mode_disjunct.storage_heat_duty_lb = pyo.Constraint(
+        expr=hxd_heat_duty >= min_storage_heat_duty + 40
+    )
+    m.usc.fs.charge_mode_disjunct.storage_heat_duty_ub = pyo.Constraint(
+        expr=hxc_heat_duty <= max_storage_heat_duty
+    )
+    m.usc.fs.discharge_mode_disjunct.storage_heat_duty_ub = pyo.Constraint(
+        expr=hxd_heat_duty <= max_storage_heat_duty * (1 - 0.01)
+    )
+
+    # Unfix boiler data fixed during initialization
+    m.usc.fs.boiler.inlet.flow_mol[0].unfix()
+
+    if not deact_arcs_after_init:
+        m.usc.fs.turbine[3].inlet.unfix()
+        m.usc.fs.fwh[8].tube_inlet.unfix()
+
+    # Unfix global variables fixed during initialization
+    m.usc.fs.hx_pump_work.unfix()
+    m.usc.fs.discharge_turbine_work.unfix()
+
+    # Unfix storage system data. Note that the area of the charge and
+    # discharge heat exchangers is unfixed and calculated during the
+    # solution of the model.
+    # m.usc.fs.fuel_cost.unfix()
+    m.usc.fs.charge_mode_disjunct.ess_charge_split.split_fraction[0, "to_hxc"].unfix()
+    m.usc.fs.discharge_mode_disjunct.ess_discharge_split.split_fraction[0, "to_hxd"].unfix()
+    for salt_hxc in [charge_mode.hxc]:
+        salt_hxc.shell_inlet.unfix()
+        salt_hxc.tube_inlet.flow_mass.unfix()
+        salt_hxc.area.unfix()
+
+    for salt_hxd in [discharge_mode.hxd]:
+        salt_hxd.tube_inlet.unfix()
+        salt_hxd.shell_inlet.flow_mass.unfix()
+        salt_hxd.area.unfix()
+
+    if not new_design:
+        for unit in [charge_mode.cooler]:
+            unit.inlet.unfix()
+            m.usc.fs.charge_mode_disjunct.cooler.outlet.enth_mol[0].unfix()
+
+    # Fix molten salt cold temperature in the discharge heat exchanger.
+    # charge_mode.hxc.area.fix(hxc_area)
+    # charge_mode.hxc.outlet_2.temperature[0].fix(hot_salt_temp)
+    # discharge_mode.hxd.area.fix(hxd_area)
+    # discharge_mode.hxd.shell_inlet.temperature[0].fix(hot_salt_temp)
+    discharge_mode.hxd.shell_outlet.temperature[0].fix(cold_salt_temp)
+
+    return m
+
+
+def create_mp_block():
+    """Create ultra-supercritical plant model and initialization for each
+    time period
+
+    """
+
+    print('>>> Creating USC model and initialization for each time period')
+
+    m = create_ss_model()
+    b1 = m.usc
+
+    # print('DOFs within mp create 1 =', degrees_of_freedom(m))
+
+    # Add data for .json data file
+    ramp_rate = design_data_dict["ramp_rate"]
+    factor_mton = design_data_dict["factor_mton"] # factor for conversion kg to metric ton
+    max_power_total = 700 # random high value
+
+    # Add coupling variables
+    b1.previous_power = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=400,
+        bounds=(min_power, max_power_total),
+        doc="Previous period power in MW"
+        )
+
+    max_inventory = 1e7 * factor_mton # in mton
+    min_inventory = 75000 * factor_mton # in mton
+    max_salt_amount = design_data_dict["max_salt_amount"] * factor_mton # in mton
+    tank_max = max_salt_amount
+
+    b1.previous_salt_inventory_hot = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=min_inventory,
+        bounds=(0, max_inventory),
+        doc="Hot salt at the beginning of the period in mton"
+        )
+    b1.salt_inventory_hot = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=min_inventory,
+        bounds=(0, max_inventory),
+        doc="Hot salt inventory at the end of the period in mton"
+        )
+    b1.previous_salt_inventory_cold = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=tank_max - min_inventory,
+        bounds=(0, max_inventory),
+        doc="Cold salt at the beginning of the period in mton"
+        )
+    b1.salt_inventory_cold = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=tank_max - min_inventory,
+        bounds=(0, max_inventory),
+        doc="Cold salt inventory at the end of the in mton"
+        )
+
+    @b1.fs.Constraint(doc="Plant ramping down constraint")
+    def constraint_ramp_down(b):
+        return (
+            b1.previous_power - ramp_rate <=
+            b.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Plant ramping up constraint")
+    def constraint_ramp_up(b):
+        return (
+            b1.previous_power + ramp_rate >=
+            b.plant_power_out[0])
+
+    @b1.fs.Constraint(doc="Inventory balance at the end of the time period")
+    def constraint_salt_inventory_hot(b):
+        return (
+            1e-3 * b1.salt_inventory_hot == (
+                b1.previous_salt_inventory_hot
+                + (3600 * b.salt_storage) * factor_mton # in mton
+            ) * 1e-3
+        )
+
+    @b1.fs.Constraint(doc="Maximum salt inventory at any time")
+    def constraint_salt_inventory(b):
+        return (
+            1e-3 * b.salt_amount == (
+                b1.salt_inventory_hot
+                + b1.salt_inventory_cold
+            ) * 1e-3
+        )
+
+    # Scale variables and constraints
+    # iscale.set_scaling_factor(b1.fs.fuel_cost, 1e-3)
+    # iscale.set_scaling_factor(b1.fs.plant_fixed_operating_cost, 1e-3)
+    # iscale.set_scaling_factor(b1.fs.plant_variable_operating_cost, 1e-3)
+    # # iscale.set_scaling_factor(b1.fs.plant_capital_cost, 1e-3)
+
+    iscale.set_scaling_factor(b1.fs.salt_amount, 1e-3)
+    iscale.set_scaling_factor(b1.salt_inventory_hot, 1e-3)
+    iscale.set_scaling_factor(b1.salt_inventory_cold, 1e-3)
+    iscale.set_scaling_factor(b1.previous_salt_inventory_hot, 1e-3)
+    iscale.set_scaling_factor(b1.previous_salt_inventory_cold, 1e-3)
+    # iscale.set_scaling_factor(b1.fs.constraint_salt_inventory_hot, 1e-3)
+
+    # iscale.set_scaling_factor(b1.fs.charge_mode_disjunct.capital_cost, 1e-3)
+    # iscale.set_scaling_factor(b1.fs.discharge_mode_disjunct.capital_cost, 1e-3)
+    # iscale.set_scaling_factor(b1.fs.storage_capital_cost, 1e-3)
+
+    # Calculate scaling factors
+    iscale.calculate_scaling_factors(m)
+
+    return m
+
+
+# The tank level and power output are linked between contiguous time
+# periods.
+def get_usc_link_variable_pairs(b1, b2):
+    """
+        b1: current time block
+        b2: next time block
+    """
+    return [
+        (b1.usc.salt_inventory_hot, b2.usc.previous_salt_inventory_hot),
+        (b1.usc.fs.plant_power_out[0], b2.usc.previous_power)
+    ]
+
+
+# The tank level at the end of the last period must be the same as the
+# level at the beginning of the first period and power output must be
+# the same as the initial tank level.
+def get_usc_periodic_variable_pairs(b1, b2):
+    """
+        b1: final time block
+        b2: first time block
+    """
+
+    return [(b1.usc.salt_inventory_hot, b2.usc.previous_salt_inventory_hot)]
+
+
+# Create the multiperiod model object. You can pass arguments to your
+# "process_model_func" for each time period using a dict of dicts as
+# shown here.  In this case, it is setting up empty dictionaries for
+# each time period.
+def create_gdp_multiperiod_usc_model(n_time_points=None, pmin=None, pmax=None):
+    """Create a multiperiod usc_mp cycle object. This object contains a
+    Pyomo model with a block for each time instance
+
+    n_time_points: Number of time blocks to create
+
+    """
+
+    multiperiod_usc = MultiPeriodModel(
+        n_time_points=n_time_points,
+        process_model_func=create_mp_block,
+        linking_variable_func=get_usc_link_variable_pairs,
+        periodic_variable_func=get_usc_periodic_variable_pairs
+    )
+
+    # If you have no arguments, you don't actually need to pass in
+    # anything. NOTE: building the model will initialize each time block
+    multiperiod_usc.build_multi_period_model()
+
+    return multiperiod_usc
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/pricetaker_with_gdp_multiperiod_integrated_storage_usc.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/pricetaker_with_gdp_multiperiod_integrated_storage_usc.py
new file mode 100644
index 000000000..05baf6fea
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/pricetaker_with_gdp_multiperiod_integrated_storage_usc.py
@@ -0,0 +1,1080 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2021 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+"""This script uses the multiperiod model in the GDP integrated
+ultra-supercritical power plant model with energy storage and performs
+market analysis using the pricetaker assumption. The electricity
+prices or LMP (locational marginal prices) are assumed to not
+change. The prices used in this study are obtained from a synthetic
+database.
+
+"""
+
+__author__ = "Soraya Rawlings"
+
+import csv
+import json
+import os
+import copy
+import numpy as np
+import logging
+
+import pyomo.environ as pyo
+from pyomo.environ import (Constraint, Expression,
+                           Var, Objective,
+                           SolverFactory,
+                           value, RangeSet, maximize)
+from pyomo.contrib.fbbt.fbbt import _prop_bnds_root_to_leaf_map
+from pyomo.core.expr.numeric_expr import ExternalFunctionExpression
+from pyomo.util.infeasible import (log_infeasible_constraints,
+                                   log_close_to_bounds)
+
+from idaes.core.solvers.get_solver import get_solver
+
+from gdp_multiperiod_usc_pricetaker_unfixed_area import create_gdp_multiperiod_usc_model
+
+# For plots
+from matplotlib import pyplot as plt
+import matplotlib
+matplotlib.rc('font', size=24)
+font = {'size':16}
+plt.rc('axes', titlesize=24)
+plt.rc('font', **font)
+
+
+def _get_lmp(hours_per_day=None, nhours=None):
+
+    # Select lmp source data and scaling factor according to that
+    use_rts_data = False
+    if use_rts_data:
+        use_mod_rts_data = False
+    else:
+        use_mod_rts_data = True
+
+    nhigh_lmp = 0
+    nlow_lmp = 0
+    if use_rts_data:
+        print('>> Using RTS lmp data')
+        with open('rts_results_all_prices_base_case.npy', 'rb') as f:
+            dispatch = np.load(f)
+            price = np.load(f)
+        lmp = price[0:nhours].tolist()
+    elif use_mod_rts_data:
+        # price = [22.9684, 21.1168, 20.4, 20.419, 0, 0, 200, 200]
+        price = [
+            # 22.9684, 21.1168, 20.4, 20.419,
+            52.9684, 21.1168, 10.4, 5.419,
+            # 20.419, 21.2877, 23.07, 25,
+            # 18.4634, 0, 0, 0,
+            0, 0, 0, 0,
+            # 19.0342, 23.07, 200, 200,
+            200, 200, 200, 200,
+        ]
+        max_lmp = max(price)
+        min_lmp = min(price)
+        for i in price:
+            if i >= (max_lmp - 5):
+                nhigh_lmp += 1
+            elif  i <= (min_lmp + 5):
+                nlow_lmp += 1
+        print('** {} lmp prices ($/MWh) between [max_lmp, max_lmp - 5]: [{}, {}]'.format(
+            nhigh_lmp, max_lmp, max_lmp - 5))
+        print('   {} lmp prices ($/MWh) between [min_lmp, min_lmp + 5]: [{}, {}]'.format(
+            nlow_lmp, min_lmp, min_lmp + 5))
+        print()
+
+        if len(price) < hours_per_day:
+            print()
+            print('**ERROR: I need more LMP data!')
+            raise Exception
+        lmp = price
+    else:
+        print('>> Using NREL lmp data')
+        price = np.load("nrel_scenario_average_hourly.npy")
+        # print(lmp)
+
+    return lmp, nhigh_lmp, nlow_lmp
+
+
+def print_model(solver_obj,
+                mdl,
+                mdl_data,
+                csvfile,
+                lmp=None,
+                nweeks=None,
+                nhours=None,
+                n_time_points=None):
+
+    # m_iter = mdl_data.master_iteration
+    m_iter = solver_obj.iteration
+
+    mdl.disjunction1_selection = {}
+    hot_tank_level_iter = []
+    cold_tank_level_iter = []
+    boiler_heat_duty_iter = []
+    hxc_duty_iter = []
+    hxd_duty_iter = []
+   
+    print('       ___________________________________________')
+    print('        Schedule')
+    print('         Obj ($): {:.4f}'.format(
+        (value(mdl.obj) / scaling_cost) / scaling_obj))
+    print('         Cycles: {} charge, {} discharge, {} no storage'.format(
+        sum(mdl.blocks[blk].process.usc.fs.charge_mode_disjunct.binary_indicator_var.value
+            for blk in mdl.blocks),
+        sum(mdl.blocks[blk].process.usc.fs.discharge_mode_disjunct.binary_indicator_var.value
+            for blk in mdl.blocks),
+        sum(mdl.blocks[blk].process.usc.fs.no_storage_mode_disjunct.binary_indicator_var.value
+            for blk in mdl.blocks)))
+
+    for blk in mdl.blocks:
+        blk_process_charge = mdl.blocks[blk].process.usc.fs.charge_mode_disjunct
+        blk_process_discharge = mdl.blocks[blk].process.usc.fs.discharge_mode_disjunct
+        blk_process_no_storage = mdl.blocks[blk].process.usc.fs.no_storage_mode_disjunct
+        if blk_process_charge.binary_indicator_var.value == 1:
+            print('         Period {}: Charge (HXC: {:.2f} MW, {:.2f} m2)'.format(
+                blk,
+                value(blk_process_charge.hxc.heat_duty[0]) * 1e-6,
+                value(blk_process_charge.hxc.area)))
+        if blk_process_discharge.binary_indicator_var.value == 1:
+            print('         Period {}: Discharge (HXD: {:.2f} MW, {:.2f} m2)'.format(
+                blk,
+                value(blk_process_discharge.hxd.heat_duty[0]) * 1e-6,
+                value(blk_process_discharge.hxd.area)))
+        if blk_process_no_storage.binary_indicator_var.value == 1:
+            print('         Period {}: No storage'.format(blk))
+
+    print()
+    for blk in mdl.blocks:
+        blk_process = mdl.blocks[blk].process.usc
+        blk_process_charge = mdl.blocks[blk].process.usc.fs.charge_mode_disjunct
+        blk_process_discharge = mdl.blocks[blk].process.usc.fs.discharge_mode_disjunct
+        blk_process_no_storage = mdl.blocks[blk].process.usc.fs.no_storage_mode_disjunct
+        print('       Time period {} '.format(blk+1))
+        print('        Charge: {}'.format(
+            blk_process_charge.binary_indicator_var.value))
+        print('        Discharge: {}'.format(
+            blk_process_discharge.binary_indicator_var.value))
+        print('        No storage: {}'.format(
+            blk_process_no_storage.binary_indicator_var.value))
+        if blk_process_charge.binary_indicator_var.value == 1:
+            mdl.disjunction1_selection[m_iter] = 'Charge'
+            print('         HXC area (m2): {:.4f}'.format(
+                value(blk_process_charge.hxc.area)))
+            print('         HXC Duty (MW): {:.4f}'.format(
+                value(blk_process_charge.hxc.heat_duty[0]) * 1e-6))
+            print('         HXC Delta temperature in/out (K): {:.4f}/{:.4f}'.format(
+                value(blk_process_charge.hxc.delta_temperature_in[0]),
+                value(blk_process_charge.hxc.delta_temperature_out[0])))
+            print('         HXC salt temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(blk_process_charge.hxc.tube_inlet.temperature[0]),
+                value(blk_process_charge.hxc.tube_outlet.temperature[0])))
+            print('         Salt flow HXC (kg/s): {:.4f}'.format(
+                value(blk_process_charge.hxc.tube_outlet.flow_mass[0])))
+            print('         HXC steam temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(blk_process_charge.hxc.hot_side.properties_in[0].temperature),
+                value(blk_process_charge.hxc.hot_side.properties_out[0].temperature)
+            ))
+            print('         Steam flow HXC (mol/s): {:.4f}'.format(
+                value(blk_process_charge.hxc.shell_outlet.flow_mol[0])))
+            if not new_design:
+                print('         Cooling heat duty (MW): {:.4f}'.format(
+                    value(blk_process_charge.cooler.heat_duty[0]) * 1e-6))
+        elif blk_process_discharge.binary_indicator_var.value == 1:
+            mdl.disjunction1_selection[m_iter] = 'Discharge'
+            print('         HXD area (m2): {:.4f}'.format(
+                value(blk_process_discharge.hxd.area)))
+            print('         HXD Duty (MW): {:.4f}'.format(
+                value(blk_process_discharge.hxd.heat_duty[0]) * 1e-6))
+            print('         HXD Delta temperature in/out (K): {:.4f}/{:.4f}'.format(
+                value(blk_process_discharge.hxd.delta_temperature_in[0]),
+                value(blk_process_discharge.hxd.delta_temperature_out[0])))
+            print('         HXD salt temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(blk_process_discharge.hxd.shell_inlet.temperature[0]),
+                value(blk_process_discharge.hxd.shell_outlet.temperature[0])))
+            print('         Salt flow HXD (kg/s): {:.4f}'.format(
+                value(blk_process_discharge.hxd.shell_outlet.flow_mass[0])))
+            print('         HXD steam temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(blk_process_discharge.hxd.cold_side.properties_in[0].temperature),
+                value(blk_process_discharge.hxd.cold_side.properties_out[0].temperature)
+            ))
+            print('         Steam flow HXD (mol/s): {:.4f}'.format(
+                value(blk_process_discharge.hxd.tube_outlet.flow_mol[0])))
+            print('         ES turbine work (MW): {:.4f}'.format(
+                value(blk_process_discharge.es_turbine.work_mechanical[0]) * -1e-6))
+        elif blk_process_no_storage.binary_indicator_var.value == 1:
+            mdl.disjunction1_selection[m_iter] = 'No_storage'
+            print('         Salt flow (kg/s): {:.4f}'.format(
+                value(blk_process.fs.salt_storage)))
+        else:
+            print('        No other operation mode is available!')
+        print('        Net power: {:.4f}'.format(value(blk_process.fs.net_power)))
+        print('        Coal heat duty: {:.4f}'.format(value(blk_process.fs.coal_heat_duty)))
+        print('        Discharge turbine work (MW): {:.4f}'.format(
+            value(blk_process.fs.discharge_turbine_work)))
+        if not new_design:
+            print('        Cooler heat duty: {:.4f}'.format(
+                value(blk_process.fs.cooler_heat_duty)))
+        print('        Efficiencies (%): boiler: {:.4f}, cycle: {:.4f}'.format(
+            value(blk_process.fs.boiler_efficiency) * 100,
+            value(blk_process.fs.cycle_efficiency) * 100))
+        print('        Boiler heat duty: {:.4f}'.format(
+            value(blk_process.fs.boiler.heat_duty[0]) * 1e-6))
+        print('        Boiler flow mol (mol/s): {:.4f}'.format(
+            value(blk_process.fs.boiler.outlet.flow_mol[0])))
+        print('        Salt to storage (kg/s) [mton]: {:.4f} [{:.4f}]'.format(
+            value(blk_process.fs.salt_storage),
+            value(blk_process.fs.salt_storage) * 3600 * factor_mton))
+        print('        Hot salt inventory (mton): {:.4f}, previous: {:.4f}'.format(
+            value(blk_process.salt_inventory_hot),
+            value(blk_process.previous_salt_inventory_hot)))
+        print('        Makeup water flow (mol/s): {:.4f}'.format(
+            value(blk_process.fs.condenser_mix.makeup.flow_mol[0])))
+        # print('        Revenue (M$/year): {:.4f}'.format(
+        #     value(mdl.blocks[blk].process.revenue)))
+        # print('        Total op cost ($/h): {:.4f}'.format(
+        #     value(mdl.blocks[blk].process.total_cost)))
+        print('        Storage cost ($/h): {:.4f}'.format(
+            value(blk_process.fs.storage_capital_cost)))
+        print('        Fuel cost ($/h): {:.4f}'.format(
+            value(blk_process.fs.fuel_cost)))
+        print('        Plant fixed op cost ($/h): {:.4f}'.format(
+            value(blk_process.fs.plant_fixed_operating_cost)))
+        print('        Plant variable op cost ($/h): {:.4f}'.format(
+            value(blk_process.fs.plant_variable_operating_cost)))
+        print()
+
+        # Save data for each NLP subproblem and plot results
+        mdl.objective_val = {}
+        mdl.boiler_heat_duty_val = {}
+        mdl.discharge_turbine_work_val = {}
+        mdl.hxc_area_val = {}
+        mdl.hxd_area_val = {}
+        mdl.hot_salt_temp_val = {}
+        mdl.objective_val[m_iter] = (value(mdl.obj) / scaling_cost) / scaling_obj
+        mdl.period = blk
+        mdl.boiler_heat_duty_val[m_iter] = 1e-6 * value(blk_process.fs.boiler.heat_duty[0])
+        mdl.discharge_turbine_work_val[m_iter] = value(blk_process.fs.discharge_turbine_work)
+        mdl.hxc_area_val[m_iter] = value(blk_process_charge.hxc.area)
+        mdl.hxd_area_val[m_iter] = value(blk_process_discharge.hxd.area)
+        mdl.hot_salt_temp_val[m_iter] = value(blk_process_charge.hxc.tube_outlet.temperature[0])
+
+        if save_results:
+            writer = csv.writer(csvfile)
+            writer.writerow(
+                (m_iter,
+                 mdl.period,
+                 mdl.disjunction1_selection[m_iter],
+                 mdl.boiler_heat_duty_val[m_iter],
+                 mdl.discharge_turbine_work_val[m_iter],
+                 mdl.hxc_area_val[m_iter],
+                 mdl.hxd_area_val[m_iter],
+                 mdl.hot_salt_temp_val[m_iter],
+                 mdl.objective_val[m_iter])
+            )
+            csvfile.flush()
+
+    print('       ___________________________________________')
+
+    hot_tank_level_iter.append(
+        [(pyo.value(mdl.blocks[i].process.usc.salt_inventory_hot)) # in mton
+         for i in range(n_time_points)])
+    cold_tank_level_iter.append(
+        [(pyo.value(mdl.blocks[i].process.usc.salt_inventory_cold)) # in mton
+         for i in range(n_time_points)])
+    boiler_heat_duty_iter.append([pyo.value(mdl.blocks[i].process.usc.fs.boiler.heat_duty[0]) * 1e-6
+                                  for i in range(n_time_points)])
+    hxc_duty_iter.append([pyo.value(mdl.blocks[i].process.usc.fs.charge_mode_disjunct.indicator_var) *
+                          pyo.value(mdl.blocks[i].process.usc.fs.charge_mode_disjunct.hxc.heat_duty[0]) * 1e-6
+                          for i in range(n_time_points)])
+    hxd_duty_iter.append([pyo.value(mdl.blocks[i].process.usc.fs.discharge_mode_disjunct.indicator_var) *
+                          pyo.value(mdl.blocks[i].process.usc.fs.discharge_mode_disjunct.hxd.heat_duty[0]) * 1e-6
+                          for i in range(n_time_points)])
+
+    # Save list of colors to be used in plots
+    c = ['darkred', 'midnightblue', 'tab:green', 'k', 'gray']
+
+    # Save and convert array to list to include values at time zero
+    hours = np.arange(n_time_points * nweeks)
+    lmp_array = np.asarray(lmp[0:n_time_points])
+    hot_tank_array = np.asarray(hot_tank_level_iter[0:nweeks]).flatten()
+    cold_tank_array = np.asarray(cold_tank_level_iter[0:nweeks]).flatten()
+    hot_tank_array0 = value(mdl.blocks[0].process.usc.previous_salt_inventory_hot)
+    cold_tank_array0 = value(mdl.blocks[0].process.usc.previous_salt_inventory_cold)
+    hours_list = hours.tolist() + [nhours]
+    hot_tank_list = [hot_tank_array0] + hot_tank_array.tolist()
+    cold_tank_list = [cold_tank_array0] + cold_tank_array.tolist()
+    hxc_array = np.asarray(hxc_duty_iter[0:nweeks]).flatten()
+    hxd_array = np.asarray(hxd_duty_iter[0:nweeks]).flatten()
+    hxc_duty_list = [0] + hxc_array.tolist()
+    hxd_duty_list = [0] + hxd_array.tolist()
+    boiler_heat_duty_array = np.asarray(boiler_heat_duty_iter[0:nweeks]).flatten()
+    boiler_heat_duty_list = [0] + boiler_heat_duty_array.tolist()
+
+    # Plot salt tank profiles and heat duty of boiler and storage heat
+    # exchangers at each master iteration.
+    fig1, ax1 = plt.subplots(figsize=(12, 8))
+    ax1.set_xlabel('Time Period (hr)')
+    ax1.set_ylabel('Salt Amount (metric ton)', color=c[3])
+    ax1.spines["top"].set_visible(False)
+    ax1.spines["right"].set_visible(False)
+    ax1.grid(linestyle=':', which='both', color=c[4], alpha=0.40)
+    # ax1.set_ylim((0, 7000))
+    plt.axhline(tank_max, ls=':', lw=1.5, color=c[4])
+    ax1.step(hours_list, hot_tank_list, marker='o', ms=8, lw=1.5, color=c[0], alpha=0.85,
+             label='Hot Tank')
+    ax1.fill_between(hours_list, hot_tank_list, step="pre", color=c[0], alpha=0.35)
+    ax1.step(hours_list, cold_tank_list, marker='o', ms=8, lw=1.5, color=c[1], alpha=0.65,
+             label='Cold Tank')
+    ax1.fill_between(hours_list, cold_tank_list, step="pre", color=c[1], alpha=0.10)
+    ax1.legend(loc="upper left", frameon=False)
+    ax1.tick_params(axis='y')
+    ax1.set_xticks(np.arange(0, n_time_points*nweeks + 1, step=1))
+    ax2 = ax1.twinx()
+    ax2.set_ylim((-25, 225))
+    ax2.set_ylabel('Locational Marginal Price ($/MWh)', color=c[2])
+    ax2.step([x + 1 for x in hours], lmp_array, marker='o', ms=8, alpha=0.5, ls='-', lw=1.5, color=c[2])
+    ax2.tick_params(axis='y', labelcolor=c[2])
+    plt.savefig('results/gdp_mp_unfixed_area_{}h/salt_tank_level_master_iter{}.png'.format(
+        nhours, m_iter))
+    plt.close(fig1)
+
+    fig2, ax3 = plt.subplots(figsize=(12, 8))
+    ax3.set_xlabel('Time Period (hr)')
+    ax3.set_ylabel('Heat Duty (MW)', color=c[3])
+    ax3.spines["top"].set_visible(False)
+    ax3.spines["right"].set_visible(False)
+    ax3.grid(linestyle=':', which='both', color=c[4], alpha=0.40)
+    ax3.set_ylim((-25, 825))
+    ax3.step(hours_list, boiler_heat_duty_list, marker='o', ms=8, ls='-', lw=1.5, alpha=0.55, color=c[3],
+             label='Boiler')
+    ax3.fill_between(hours_list, boiler_heat_duty_list, step="pre", color=c[3], alpha=0.15)
+    plt.axhline(max_storage_heat_duty, ls=':', lw=1.5, color=c[4])
+    plt.axhline(min_storage_heat_duty, ls=':', lw=1.5, color=c[4])
+    ax3.step(hours_list, hxc_duty_list, marker='o', ms=8, color=c[0], alpha=0.75,
+             label='Charge')
+    ax3.fill_between(hours_list, hxc_duty_list, step="pre", color=c[0], alpha=0.25)
+    ax3.step(hours_list, hxd_duty_list, marker='o', ms=8, color=c[1], alpha=0.75,
+             label='Discharge')
+    ax3.fill_between(hours_list, hxd_duty_list, step="pre", color=c[1], alpha=0.25)
+    ax3.legend(loc="center right", frameon=False)
+    ax3.tick_params(axis='y')
+    ax3.set_xticks(np.arange(0, n_time_points*nweeks + 1, step=1))
+    ax4 = ax3.twinx()
+    ax4.set_ylim((-25, 225))
+    ax4.set_ylabel('Locational Marginal Price ($/MWh)', color=c[2])
+    ax4.step([x + 1 for x in hours], lmp_array, marker='o', ms=8, alpha=0.7, ls='-', lw=1.5, color=c[2])
+    ax4.tick_params(axis='y', labelcolor=c[2])
+    plt.savefig('results/gdp_mp_unfixed_area_{}h/heat_duty_master_iter{}.png'.format(
+        nhours, m_iter))
+    plt.close(fig2)
+
+    log_close_to_bounds(mdl)
+    log_infeasible_constraints(mdl)
+
+
+def create_csv_header(nhours):
+
+    csvfile = open('results/gdp_mp_unfixed_area_{}h/results_subnlps_master_iter.csv'.format(nhours),
+                   'w', newline='')
+    writer = csv.writer(csvfile)
+    writer.writerow(
+        ('Iteration',
+         'TimePeriod(hr)',
+         'OperationMode',
+         'BoilerHeatDuty(MW)',
+         'DischargeWork(MW)',
+         'HXCArea',
+         'HXDArea',
+         'SaltHotTemp',
+         'Obj($/hr)')
+    )
+    return csvfile
+
+
+def run_pricetaker_analysis(hours_per_day=None,
+                            nhours=None,
+                            ndays=None,
+                            nweeks=None,
+                            n_time_points=None,
+                            pmin=None,
+                            tank_status=None,
+                            tank_min=None,
+                            tank_max=None):
+
+    # Get LMP data
+    lmp, nhigh_lmp, nlow_lmp = _get_lmp(hours_per_day=hours_per_day, nhours=nhours)
+
+    # Create the multiperiod model object. You can pass arguments to
+    # the "process_model_func" for each time period using a dict of
+    # dicts as shown here.  In this case, it is setting up empty
+    # dictionaries for each time period.
+    gdp_multiperiod_usc = create_gdp_multiperiod_usc_model(
+        n_time_points=n_time_points,
+        pmin=pmin,
+        pmax=None
+    )
+
+    # Retrieve pyomo model and active process blocks
+    m = gdp_multiperiod_usc.pyomo_model
+    blks = gdp_multiperiod_usc.get_active_process_blocks()
+
+    ##################################################################
+    # Add nonanticipativity constraints
+    ##################################################################
+    m.hours_set = RangeSet(0, nhours - 1)
+    m.hours_set2 = RangeSet(0, nhours - 2)
+
+    # Add constraint to save calculate charge and discharge area in a
+    # global variable
+    @m.Constraint(m.hours_set2)
+    def constraint_charge_previous_area(b, h):
+        return (
+            b.blocks[h + 1].process.usc.fs.charge_area ==
+            b.blocks[h].process.usc.fs.charge_area
+        )
+
+    @m.Constraint(m.hours_set2)
+    def constraint_discharge_previous_area(b, h):
+        return (
+            b.blocks[h + 1].process.usc.fs.discharge_area ==
+            b.blocks[h].process.usc.fs.discharge_area
+        )
+
+    # @m.Constraint(m.hours_set)
+    # def constraint_charge_area_lb(b, h):
+    #     return (
+    #         b.blocks[h].process.usc.fs.charge_area >= 1000
+    #     )
+    # @m.Constraint(m.hours_set)
+    # def constraint_discharge_area_lb(b, h):
+    #     return (
+    #         b.blocks[h].process.usc.fs.discharge_area >= 1000
+    #     )
+
+    # Declare constraint to ensure that the discharge heat exchanger
+    # has the same temperature for the hot salt than the one obtained
+    # during charge cycle.
+    @m.Constraint(m.hours_set)
+    def constraint_discharge_hot_salt_temperature(b, h):
+        return (
+            b.blocks[h].process.usc.fs.discharge_mode_disjunct.hxd.shell_inlet.temperature[0] ==
+            b.blocks[h].process.usc.fs.hot_salt_temp
+        )
+
+    ##################################################################
+    # Add logical constraints
+    ##################################################################
+    discharge_min_salt = 379 # in mton, 8MW min es turbine
+    # discharge_min_salt = 1 # in mton, 8MW min es turbine
+    min_hot_salt = 2000
+    @m.Constraint(m.hours_set)
+    def _constraint_no_discharge_with_min_hot_tank(b, h):
+        if h <= 2:
+            a = min_hot_salt
+        else:
+            a = discharge_min_salt
+        return (
+            (b.blocks[h].process.usc.fs.discharge_mode_disjunct.binary_indicator_var * a) <=
+            blks[h].usc.previous_salt_inventory_hot
+        )
+
+    # Add a minimum number of charge, discharge, and no storage
+    # operation modes. Note: For charge, the minimum number of cycles
+    # is based on the number of low lmp values obtained above, while
+    # for discharge, the minimum number of cycles is based on the
+    # number of high lmp values.
+    @m.Constraint()
+    def _constraint_min_charge(b):
+        return sum(b.blocks[h].process.usc.fs.charge_mode_disjunct.binary_indicator_var
+                   for h in b.hours_set) >= nlow_lmp
+    @m.Constraint()
+    def _constraint_min_discharge(b):
+        return sum(b.blocks[h].process.usc.fs.discharge_mode_disjunct.binary_indicator_var
+                   for h in b.hours_set) >= nhigh_lmp - 1
+    # @m.Constraint()
+    # def _constraint_min_no_storage(b):
+    #     return sum(b.blocks[h].process.usc.fs.no_storage_mode_disjunct.binary_indicator_var
+    #                for h in b.hours_set) >= 1
+
+
+    # if tank_status == "hot_empty":
+    #     # Add logical constraint to help reduce the alternatives to explore
+    #     # when periodic behavior is expected
+    #     @m.Constraint()
+    #     def _logic_constraint_no_discharge_time0(b):
+    #         return b.blocks[0].process.usc.fs.discharge_mode_disjunct.binary_indicator_var == 0
+    #     @m.Constraint()
+    #     def _logic_constraint_no_charge_at_timen(b):
+    #         return (
+    #             (b.blocks[0].process.usc.fs.charge_mode_disjunct.binary_indicator_var
+    #              + b.blocks[nhours - 1].process.usc.fs.charge_mode_disjunct.binary_indicator_var) <= 1
+    #         )
+    #     # @m.Constraint()
+    #     # def _logic_constraint_no_storage_time0_no_charge_at_timen(m):
+    #     #     return (
+    #     #         (m.blocks[0].process.usc.fs.no_storage_mode_disjunct.binary_indicator_var
+    #     #          + m.blocks[nhours - 1].process.usc.fs.charge_mode_disjunct.binary_indicator_var) <= 1
+    #     #     )
+    # elif tank_status == "hot_full":
+    #     @m.Constraint()
+    #     def _logic_constraint_no_discharge_at_timen(m):
+    #         return (
+    #             (m.blocks[0].process.usc.fs.discharge_mode_disjunct.binary_indicator_var
+    #              + m.blocks[nhours - 1].process.usc.fs.discharge_mode_disjunct.binary_indicator_var) <= 1
+    #         )
+
+    # Add lmp market data for each block
+    count = 0
+    for blk in blks:
+        blk.revenue = pyo.Expression(
+            expr=(lmp[count] * blk.usc.fs.net_power)
+        )
+
+        # # Add expression to calculate total operating costs. Note that
+        # # these costs are scaled using a scaling cost factor
+        # blk.total_cost = pyo.Expression(
+        #     expr=(
+        #         blk.usc.fs.fuel_cost +
+        #         blk.usc.fs.plant_fixed_operating_cost +
+        #         blk.usc.fs.plant_variable_operating_cost +
+        #         blk.usc.fs.storage_capital_cost
+        #     )
+        # )
+
+        # # Declare expression to calculate the total profit. All the
+        # # costs are in $ per hour
+        # blk.profit = pyo.Expression(
+        #     expr=(
+        #         blk.revenue -
+        #         blk.total_cost
+        #         # (lmp[count] * blk.usc.fs.net_power) -
+        #         # (
+        #         #     blk.usc.fs.fuel_cost +
+        #         #     blk.usc.fs.plant_fixed_operating_cost +
+        #         #     blk.usc.fs.plant_variable_operating_cost +
+        #         #     blk.usc.fs.storage_capital_cost
+        #         # )
+        #     ) * scaling_cost
+        # )
+        count += 1
+
+    # m.obj = pyo.Objective(
+    #     expr=sum([blk.profit for blk in blks]) * scaling_obj,
+    #     sense=maximize
+    # )
+    m.obj = pyo.Objective(
+        expr=sum(
+            [blk.revenue -
+             (blk.usc.fs.fuel_cost +
+              blk.usc.fs.plant_fixed_operating_cost +
+              blk.usc.fs.plant_variable_operating_cost) -
+             blk.usc.fs.storage_capital_cost
+             for blk in blks]
+        ) * scaling_obj,
+        sense=maximize
+    )
+
+    # Initial state for linking variables: power and salt
+    # tank. Different tank scenarios are included for the Solar salt
+    # tank levels and the previous tank level of the tank is based on
+    # that.
+    if tank_status == "hot_empty":
+        blks[0].usc.previous_salt_inventory_hot.fix(tank_min)
+        blks[0].usc.previous_salt_inventory_cold.fix(tank_max-tank_min)
+    elif tank_status == "hot_half_full":
+        blks[0].usc.previous_salt_inventory_hot.fix(tank_max/2)
+        blks[0].usc.previous_salt_inventory_cold.fix(tank_max/2)
+    elif tank_status == "hot_full":
+        blks[0].usc.previous_salt_inventory_hot.fix(tank_max-tank_min)
+        blks[0].usc.previous_salt_inventory_cold.fix(tank_min)
+    else:
+        print("Unrecognized scenario! Try hot_empty, hot_full, or hot_half_full")
+
+    blks[0].usc.previous_power.fix(447.66)
+
+    # Initialize disjunctions
+    print()
+    print()
+    print('>>Initializing disjuncts')
+    if tank_status == "hot_empty":
+        for k in range(nhours):
+            # if k <= (nhours / 3) - 1:
+            #     blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(0)
+            #     blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+            #     blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(1)
+            # elif k <= 2 * (nhours / 3) - 1:
+            #     blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(1)
+            #     blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+            #     blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(0)
+            if k <= (nhours / 2) - 1:
+                blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(1)
+                blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+                blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(0)
+            # if k >= (nhours / 2) - 1:
+            #     blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(1)
+            #     blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+            #     blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(0)
+            else:
+                blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(0)
+                blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(1)
+                blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(0)
+    elif tank_status == "hot_full":
+        blks[0].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(0)
+        blks[0].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(1)
+        blks[0].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(0)
+        for k in range(nhours):
+            if k >= 1:
+                blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(0)
+                blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+                blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(1)
+    else:
+        blks[0].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(1)
+        blks[0].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+        blks[0].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(0)
+        for k in range(nhours):
+            if k >= 1:
+                blks[k].usc.fs.charge_mode_disjunct.binary_indicator_var.set_value(0)
+                blks[k].usc.fs.discharge_mode_disjunct.binary_indicator_var.set_value(0)
+                blks[k].usc.fs.no_storage_mode_disjunct.binary_indicator_var.set_value(1)
+
+    # Select solver and solve the model
+    csvfile = create_csv_header(nhours=nhours)
+
+    opt = pyo.SolverFactory('gdpopt')
+    _prop_bnds_root_to_leaf_map[ExternalFunctionExpression] = lambda x, y, z: None
+
+    net_power = []
+    hot_tank_level = []
+    cold_tank_level = []
+    hxc_duty = []
+    hxd_duty = []
+    boiler_heat_duty = []
+    discharge_work = []
+    for week in range(nweeks):
+        print()
+        print(">> Solving for week {}: {} hours of operation in {} day(s) ".
+              format(week + 1, nhours, ndays))
+        results = opt.solve(
+            m,
+            tee=True,
+            algorithm='RIC',
+            mip_solver='gurobi_direct',
+            nlp_solver='ipopt',
+            # # OA_penalty_factor=1e4,
+            # # max_slack=1e4,
+            # zero_tolerance=1e-10,
+            # integer_tolerance=1e-4,
+            # variable_tolerance=1e-6,
+            init_algorithm="no_init",
+            subproblem_presolve=False,
+            time_limit="56000",
+            iterlim=500,
+            call_after_subproblem_solve=(
+                lambda c, a, b: print_model(c, a, b,
+                                            csvfile, nweeks=nweeks,
+                                            nhours=nhours, lmp=lmp,
+                                            n_time_points=n_time_points)
+            ),
+            nlp_solver_args=dict(
+                tee=True,
+                symbolic_solver_labels=True,
+                options={"linear_solver": "ma27",
+                         "max_iter": 150,
+                         "halt_on_ampl_error": "yes"
+                }
+            )
+        )
+
+        hot_tank_level.append([pyo.value(blks[i].usc.salt_inventory_hot) # in mton
+                               for i in range(n_time_points)])
+        cold_tank_level.append([pyo.value(blks[i].usc.salt_inventory_cold) # in mton
+                                for i in range(n_time_points)])
+        net_power.append([pyo.value(blks[i].usc.fs.net_power)
+                          for i in range(n_time_points)])
+        boiler_heat_duty.append([pyo.value(blks[i].usc.fs.boiler.heat_duty[0]) * 1e-6
+                                 for i in range(n_time_points)])
+        discharge_work.append([pyo.value(blks[i].usc.fs.discharge_turbine_work)
+                               for i in range(n_time_points)])
+        hxc_duty.append([pyo.value(blks[i].usc.fs.charge_mode_disjunct.indicator_var) *
+                         pyo.value(blks[i].usc.fs.charge_mode_disjunct.hxc.heat_duty[0]) * 1e-6
+                         for i in range(n_time_points)])
+        hxd_duty.append([pyo.value(blks[i].usc.fs.discharge_mode_disjunct.indicator_var) *
+                         pyo.value(blks[i].usc.fs.discharge_mode_disjunct.hxd.heat_duty[0]) * 1e-6
+                         for i in range(n_time_points)])
+
+    csvfile.close()
+
+    return (m,
+            blks,
+            lmp,
+            net_power,
+            results,
+            hot_tank_level,
+            cold_tank_level,
+            hxc_duty,
+            hxd_duty,
+            boiler_heat_duty,
+            discharge_work)
+
+def print_results(m, blks, results):
+    # Print and plot results
+    c = 0
+    print('Objective: {:.4f}'.format(value(m.obj) / scaling_obj))
+    for blk in blks:
+        print()
+        print('Period {}'.format(c+1))
+        storage_work = blks[c].usc.fs.discharge_turbine_work
+        charge_mode = blks[c].usc.fs.charge_mode_disjunct
+        discharge_mode = blks[c].usc.fs.discharge_mode_disjunct
+        perc = 100
+        factor = 1
+
+        print(' Charge mode: {}'.format(
+            blks[c].usc.fs.charge_mode_disjunct.binary_indicator_var.value))
+        print(' Discharge mode: {}'.format(
+            blks[c].usc.fs.discharge_mode_disjunct.binary_indicator_var.value))
+        print(' No storage mode: {}'.format(
+            blks[c].usc.fs.no_storage_mode_disjunct.binary_indicator_var.value))
+        if blks[c].usc.fs.charge_mode_disjunct.binary_indicator_var.value == 1:
+            print('  HXC area (m2): {:.4f}'.format(
+                value(charge_mode.hxc.area)))
+            print('  HXC Duty (MW): {:.4f}'.format(
+                value(charge_mode.hxc.heat_duty[0]) * 1e-6))
+            print('  HXC salt temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(charge_mode.hxc.tube_inlet.temperature[0]),
+                value(charge_mode.hxc.tube_outlet.temperature[0])))
+            print('  HXC steam temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(charge_mode.hxc.hot_side.properties_in[0].temperature),
+                value(charge_mode.hxc.hot_side.properties_out[0].temperature)))
+            print('  HXC salt flow (kg/s) [mton/h]: {:.4f} [{:.4f}]'.format(
+                value(charge_mode.hxc.tube_outlet.flow_mass[0]),
+                value(charge_mode.hxc.tube_outlet.flow_mass[0]) * 3600 * factor_mton))
+            print('  HXC steam flow (mol/s): {:.4f}'.format(
+                value(charge_mode.hxc.shell_outlet.flow_mol[0])))
+            print('  HXC Delta T (K): in: {:.4f}, out: {:.4f}'.format(
+                value(charge_mode.hxc.delta_temperature_in[0]),
+                value(charge_mode.hxc.delta_temperature_out[0])))
+        elif blks[c].usc.fs.discharge_mode_disjunct.binary_indicator_var.value == 1:
+            print('  HXD area (m2): {:.4f}'.format(
+                value(discharge_mode.hxd.area)))
+            print('  HXD Duty (MW): {:.4f}'.format(
+                value(discharge_mode.hxd.heat_duty[0]) * 1e-6))
+            print('  HXD salt temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(discharge_mode.hxd.shell_inlet.temperature[0]),
+                value(discharge_mode.hxd.shell_outlet.temperature[0])))
+            print('  HXD steam temperature (K) in/out: {:.4f}/{:.4f}'.format(
+                value(discharge_mode.hxd.cold_side.properties_in[0].temperature),
+                value(discharge_mode.hxd.cold_side.properties_out[0].temperature)))
+            print('  HXD salt flow (kg/s) [mton/h]: {:.4f} [{:.4f}]'.format(
+                value(discharge_mode.hxd.shell_outlet.flow_mass[0]),
+                value(discharge_mode.hxd.shell_outlet.flow_mass[0]) * 3600 * factor_mton))
+            print('  HXD steam flow (mol/s): {:.4f}'.format(
+                value(discharge_mode.hxd.tube_outlet.flow_mol[0])))
+            print('  HXD Delta T (K): in: {:.4f}, out: {:.4f}'.format(
+                value(discharge_mode.hxd.delta_temperature_in[0]),
+                value(discharge_mode.hxd.delta_temperature_out[0])))
+            print('  ES turbine work (MW): {:.4f}'.format(
+                value(discharge_mode.es_turbine.work_mechanical[0]) * -1e-6))
+        elif blks[c].usc.fs.no_storage_mode_disjunct.binary_indicator_var.value == 1:
+            print('  **Note: no storage heat exchangers exist, so the units have the init values ')
+            print('  HXC area (m2): {:.4f}'.format(
+                value(charge_mode.hxc.area)))
+            print('  HXC Duty (MW): {:.4f}'.format(
+                value(charge_mode.hxc.heat_duty[0]) * 1e-6))
+            print('  HXC salt flow (kg/s): {:.4f} '.format(
+                value(charge_mode.hxc.tube_outlet.flow_mass[0])))
+            print('  HXD area (m2): {:.4f}'.format(
+                value(discharge_mode.hxd.area)))
+            print('  HXD Duty (MW): {:.4f}'.format(
+                value(discharge_mode.hxd.heat_duty[0]) * 1e-6))
+            print('  HXD salt flow (kg/s): {:.4f}'.format(
+                value(discharge_mode.hxd.shell_outlet.flow_mass[0])))
+        else:
+            print('  No other operation modes!')
+
+        print(' Net power: {:.4f}'.format(
+            value(blks[c].usc.fs.net_power)))
+        print(' Plant Power Out: {:.4f}'.format(
+            value(blks[c].usc.fs.plant_power_out[0])))
+        print(' Discharge turbine work (MW): {:.4f}'.format(
+            value(storage_work) * factor))
+        # print(' Profit ($): {:.4f}'.format(
+        #     value(blks[c].profit) / scaling_cost))
+        # print(' Revenue ($): {:.4f}'.format(
+        #     value(blks[c].revenue)))
+        # print(' Operating cost ($): {:.4f}'.format(
+        #     value(blks[c].total_cost)))
+        print(' Efficiencies (%): boiler: {:.4f}, cycle: {:.4f}'.format(
+            value(blks[c].usc.fs.boiler_efficiency) * 100,
+            value(blks[c].usc.fs.cycle_efficiency) * perc))
+        print(' Boiler heat duty: {:.4f}'.format(
+            value(blks[c].usc.fs.boiler.heat_duty[0]) * 1e-6))
+        print(' Boiler flow mol (mol/s): {:.4f}'.format(
+            value(blks[c].usc.fs.boiler.outlet.flow_mol[0])))
+        print(' Hot salt inventory (mton): previous: {:.4f}, current: {:.4f}'.format(
+            value(blks[c].usc.previous_salt_inventory_hot),
+            value(blks[c].usc.salt_inventory_hot)))
+        print(' Cold salt inventory (mton): previous: {:.4f}, current: {:.4f}'.format(
+            value(blks[c].usc.previous_salt_inventory_cold),
+            value(blks[c].usc.salt_inventory_cold)))
+        c += 1
+
+    print(results)
+
+def plot_results(m,
+                 blks,
+                 lmp,
+                 ndays=None,
+                 nweeks=None,
+                 n_time_points=None,
+                 net_power=None,
+                 tank_max=None,
+                 hot_tank_level=None,
+                 cold_tank_level=None,
+                 hxc_duty=None,
+                 hxd_duty=None,
+                 boiler_heat_duty=None,
+                 discharge_work=None):
+
+
+    c = ['darkred', 'midnightblue', 'tab:green', 'k', 'gray']
+    hours = np.arange(n_time_points * nweeks)
+    lmp_array = np.asarray(lmp[0:n_time_points])
+    hot_tank_array = np.asarray(hot_tank_level[0:nweeks]).flatten()
+    cold_tank_array = np.asarray(cold_tank_level[0:nweeks]).flatten()
+
+    # First, convert array to list to include the
+    # value at period zero, which for this analysis is zero since the
+    # plant is not operating.
+    hot_tank_array0 = value(blks[0].usc.previous_salt_inventory_hot)
+    cold_tank_array0 = value(blks[0].usc.previous_salt_inventory_cold)
+    hours_list = hours.tolist() + [nhours]
+    hot_tank_list = [hot_tank_array0] + hot_tank_array.tolist()
+    cold_tank_list = [cold_tank_array0] + cold_tank_array.tolist()
+    hxc_array = np.asarray(hxc_duty[0:nweeks]).flatten()
+    hxd_array = np.asarray(hxd_duty[0:nweeks]).flatten()
+    hxc_duty_list = [0] + hxc_array.tolist()
+    hxd_duty_list = [0] + hxd_array.tolist()
+    boiler_heat_duty_array = np.asarray(boiler_heat_duty[0:nweeks]).flatten()
+    boiler_heat_duty_list = [0] + boiler_heat_duty_array.tolist()
+    power_array = np.asarray(net_power[0:nweeks]).flatten()
+    power_array0 = value(blks[0].usc.previous_power)
+    power_list = [power_array0] + power_array.tolist()
+    discharge_work_array = np.asarray(discharge_work[0:nweeks]).flatten()
+    discharge_work_list = [0] + discharge_work_array.tolist()
+
+    # Plot molten salt tank levels for each period. First, convert
+    # array to list to include hot tank level at initial period zero.
+    fig3, ax1 = plt.subplots(figsize=(12, 8))
+    ax1.set_xlabel('Time Period (hr)')
+    ax1.set_ylabel('Salt Amount (metric ton)', color=c[3])
+    ax1.spines["top"].set_visible(False)
+    ax1.spines["right"].set_visible(False)
+    ax1.grid(linestyle=':', which='both', color=c[4], alpha=0.40)
+    plt.axhline(tank_max, ls=':', lw=1.5, color=c[4])
+    ax1.step(hours_list, hot_tank_list, marker='o', ms=8, lw=1.5, color=c[0], alpha=0.85,
+             label='Hot Tank')
+    ax1.fill_between(hours_list, hot_tank_list, step="pre", color=c[0], alpha=0.35)
+    ax1.step(hours_list, cold_tank_list, marker='o', ms=8, lw=1.5, color=c[1], alpha=0.65,
+             label='Cold Tank')
+    ax1.fill_between(hours_list, cold_tank_list, step="pre", color=c[1], alpha=0.10)
+    ax1.legend(loc="upper left", frameon=False)
+    ax1.tick_params(axis='y')
+    ax1.set_xticks(np.arange(0, n_time_points*nweeks + 1, step=1))
+    ax2 = ax1.twinx()
+    ax2.set_ylim((-25, 225))
+    ax2.set_ylabel('Locational Marginal Price ($/MWh)', color=c[2])
+    ax2.step([x + 1 for x in hours], lmp_array, marker='o', ms=8, alpha=0.7, ls='-', lw=1.5, color=c[2])
+    ax2.tick_params(axis='y', labelcolor=c[2])
+    plt.savefig('results/gdp_mp_unfixed_area_{}h/final_salt_tank_level.png'.format(
+        nhours))
+
+    # Plot charge and discharge heat exchangers heat duty values for
+    # each time period.
+    fig4, ax3 = plt.subplots(figsize=(12, 8))
+    ax3.set_xlabel('Time Period (hr)')
+    ax3.set_ylabel('Heat Duty (MW)', color=c[3])
+    ax3.spines["top"].set_visible(False)
+    ax3.spines["right"].set_visible(False)
+    ax3.grid(linestyle=':', which='both', color=c[4], alpha=0.40)
+    ax3.set_ylim((-25, 825))
+    ax3.step(hours_list, boiler_heat_duty_list, marker='o', ms=8, ls='-', lw=1.5, alpha=0.85, color=c[3],
+             label='Boiler')
+    ax3.fill_between(hours_list, boiler_heat_duty_list, step="pre", color=c[3], alpha=0.15)
+    plt.axhline(max_storage_heat_duty, ls=':', lw=1.5, color=c[4])
+    # plt.axhline(min_storage_heat_duty, ls=':', lw=1.5, color=c[4])
+    ax3.step(hours_list, hxc_duty_list, marker='o', ms=8, color=c[0], alpha=0.75,
+             label='Charge')
+    ax3.fill_between(hours_list, hxc_duty_list, step="pre", color=c[0], alpha=0.25)
+    ax3.step(hours_list, hxd_duty_list, marker='o', ms=8, color=c[1], alpha=0.75,
+             label='Discharge')
+    ax3.fill_between(hours_list, hxd_duty_list, step="pre", color=c[1], alpha=0.25)
+    ax3.legend(loc="center left", frameon=False)
+    ax3.tick_params(axis='y')
+    ax3.set_xticks(np.arange(0, n_time_points*nweeks + 1, step=1))
+    ax4 = ax3.twinx()
+    ax4.set_ylim((-25, 225))
+    ax4.set_ylabel('Locational Marginal Price ($/MWh)', color=c[2])
+    ax4.step([x + 1 for x in hours], lmp_array, marker='o', ms=8, alpha=0.7, ls='-', lw=1.5, color=c[2])
+    ax4.tick_params(axis='y', labelcolor=c[2])
+    plt.savefig('results/gdp_mp_unfixed_area_{}h/final_heat_duty.png'.format(nhours))
+
+    # Plot net power and discharge power production for each period.
+    fig4, ax5 = plt.subplots(figsize=(12, 8))
+    ax5.set_xlabel('Time Period (hr)')
+    ax5.set_ylabel('Power Output (MW)', color=c[1])
+    ax5.spines["top"].set_visible(False)
+    ax5.spines["right"].set_visible(False)
+    ax5.grid(linestyle=':', which='both', color=c[4], alpha=0.40)
+    plt.axhline(max_power, ls=':', lw=1.5, color=c[4])
+    ax5.step(hours_list, power_list, marker='o', ms=8, lw=1.5, color=c[3],
+             label='Plant Net Power')
+    ax5.fill_between(hours_list, power_list, step="pre", color=c[3], alpha=0.15)
+    ax5.step(hours_list, discharge_work_list, marker='o', ms=8, color=c[1], alpha=0.85,
+             label='Discharge Turbine')
+    ax5.fill_between(hours_list, discharge_work_list, step="pre", color=c[1], alpha=0.15)
+    ax5.tick_params(axis='y', labelcolor=c[1])
+    ax5.set_xticks(np.arange(0, n_time_points*nweeks + 1, step=2))
+    ax6 = ax5.twinx()
+    ax2.set_ylim((-25, 225))
+    ax6.set_ylabel('Locational Marginal Price ($/MWh)', color=c[2])
+    ax6.step([x + 1 for x in hours], lmp_array, marker='o', ms=8, alpha=0.7, ls='-', lw=1.5, color=c[2])
+    ax6.tick_params(axis='y', labelcolor=c[2])
+    plt.savefig('results/gdp_mp_unfixed_area_{}h/final_power.png'.format(nhours))
+
+    plt.show()
+
+def _mkdir(dir):
+    """Create directory to save results
+
+    """
+
+    try:
+        os.mkdir(dir)
+        print('Directory {} created'.format(dir))
+    except:
+        print('Directory {} not created because it already exists!'.format(dir))
+        pass
+
+
+if __name__ == '__main__':
+
+    optarg = {
+        "max_iter": 300,
+        # "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver('ipopt', optarg)
+
+    # Save results in a .csv file for each master iteration
+    save_results = True
+
+    # Use GDP design for charge and discharge heat exchanger from 4-12
+    # disjunctions model when True. If False, use the GDP design from
+    # 4-5 disjunctions model. **Note** When changing this, make sure
+    # to change it in the GDP multiperiod python script too.
+    new_design = True
+
+    lx = True
+    if lx:
+        if new_design:
+            # scaling_obj = 1e-2
+            # # scaling_cost = 1e-3 # before changing the obj function
+            # scaling_cost = 1e-3
+            scaling_obj = 1e-5
+            # scaling_cost = 1e-3 # before changing the obj function
+            scaling_cost = 1
+        else:
+            # scaling_obj = 1e-2 # 6h, 12h
+            scaling_obj = 1e-4 # 12h
+            scaling_cost = 1e-3
+    else:
+        scaling_obj = 1
+        scaling_cost = 1
+    print()
+    print('Scaling cost:', scaling_cost)
+    print('Scaling obj:', scaling_obj)
+
+    # Add design data from .json file
+    if new_design:
+        data_path = 'uscp_design_data_new_storage_design.json'
+    else:
+        data_path = 'uscp_design_data.json'
+
+    with open(data_path) as design_data:
+        design_data_dict = json.load(design_data)
+
+    max_salt_amount = design_data_dict["max_salt_amount"] # in kg
+    max_storage_heat_duty = design_data_dict["max_storage_heat_duty"] # in MW
+    min_storage_heat_duty = design_data_dict["min_storage_heat_duty"] # in MW
+    factor_mton = design_data_dict["factor_mton"] # factor for conversion kg to metric ton
+    max_power = design_data_dict["plant_max_power"] # in MW
+    pmin = design_data_dict["plant_min_power"] # in MW
+
+    hours_per_day = 12
+    ndays = 1
+    nhours = hours_per_day * ndays
+    nweeks = 1
+
+    # Add number of hours per week
+    n_time_points = nweeks * nhours
+
+    tank_status = "hot_empty"
+    tank_min = 1 * factor_mton # in mton
+    tank_max = max_salt_amount * factor_mton # in mton
+
+    # Create a directory to save the results for each NLP sbproblem
+    # and plots
+    _mkdir('results')
+    _mkdir('results/gdp_mp_unfixed_area_{}h'.format(nhours))
+
+    (m,
+     blks,
+     lmp,
+     net_power,
+     results,
+     hot_tank_level,
+     cold_tank_level,
+     hxc_duty,
+     hxd_duty,
+     boiler_heat_duty,
+     discharge_work) = run_pricetaker_analysis(hours_per_day=hours_per_day,
+                                               nhours=nhours,
+                                               ndays=ndays,
+                                               nweeks=nweeks,
+                                               n_time_points=n_time_points,
+                                               pmin=pmin,
+                                               tank_status=tank_status,
+                                               tank_min=tank_min,
+                                               tank_max=tank_max)
+
+    print_results(m,
+                  blks,
+                  results)
+
+    plot_results(m,
+                 blks,
+                 lmp,
+                 ndays=ndays,
+                 nweeks=nweeks,
+                 n_time_points=n_time_points,
+                 hot_tank_level=hot_tank_level,
+                 cold_tank_level=cold_tank_level,
+                 net_power=net_power,
+                 hxc_duty=hxc_duty,
+                 hxd_duty=hxd_duty,
+                 tank_max=tank_max,
+                 boiler_heat_duty=boiler_heat_duty,
+                 discharge_work=discharge_work)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/ultra_supercritical_powerplant_w_ccs.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/ultra_supercritical_powerplant_w_ccs.py
new file mode 100644
index 000000000..108ade9c9
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/ultra_supercritical_powerplant_w_ccs.py
@@ -0,0 +1,268 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program
+# (DISPATCHES), and is copyright (c) 2021 by the software owners:
+# The Regents of the University of California, through Lawrence Berkeley
+# National Laboratory, National Technology & Engineering Solutions of Sandia,
+# LLC, Alliance for Sustainable Energy, LLC, Battelle Energy Alliance, LLC,
+# University of Notre Dame du Lac, et al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and
+# license information, respectively. Both files are also available online
+# at the URL: "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+"""
+This is a simple model for an ultrasupercritical coal-fired power plant
+integrated with a boiler fire-side unit model and a CO2 capture system.
+The boiler fire-side model accounts for flue-gas generation.
+The CO2 capture system uses surrogates for a solvent-based piperazine system.
+The capture rate is fixed to 90%.
+"""
+
+# IDAES Imports
+from idaes.core.util.model_statistics import degrees_of_freedom
+from idaes.power_generation.unit_models.boiler_fireside import BoilerFireside
+from idaes.core.util import get_solver
+import idaes.core.util.scaling as iscale
+import idaes.logger as idaeslog
+
+import ultra_supercritical_powerplant as usc
+
+from idaes.power_generation.carbon_capture.piperazine_surrogates.\
+    co2_capture_system import CO2Capture
+from idaes.power_generation.properties import FlueGasParameterBlock
+
+
+def add_fireside(m):
+
+    m.fs.prop_fluegas = FlueGasParameterBlock()
+
+    boiler_input_dict = {1: '614623556',
+                         'pl': '126983008',
+                         'roof': '96959378',
+                         'flyash': '0.0001',  # flyash mass fraction
+                         'NOx': '140'}  # NOx PPM
+
+    m.fs.boiler_fireside = BoilerFireside(
+            default={"dynamic": False,
+                     "property_package": m.fs.prop_fluegas,
+                     "calculate_PA_SA_flows": True,
+                     "number_of_zones": 1,
+                     "has_platen_superheater": True,
+                     "has_roof_superheater": True,
+                     "surrogate_dictionary": boiler_input_dict})
+    m.fs.boiler_fireside.eq_surr_waterwall_heat.deactivate()
+    m.fs.boiler_fireside.eq_surr_platen_heat.deactivate()
+    m.fs.boiler_fireside.eq_surr_roof_heat.deactivate()
+
+    @m.fs.boiler_fireside.Constraint(m.fs.time,
+                                     m.fs.boiler_fireside.zones,
+                                     doc="Waterwall heat duty")
+    def eq_main_boiler_heat(b, t, z):
+        return (
+            b.waterwall_heat[t, z] *
+            b.fcorrection_heat_ww[t] ==
+            m.fs.boiler.heat_duty[0])
+
+    @m.fs.boiler_fireside.Constraint(m.fs.time,
+                                     doc="Reheater heat duty")
+    def eq_reheater_1_heat(b, t):
+        return (
+            b.platen_heat[t] *
+            b.fcorrection_heat_platen[t] ==
+            m.fs.reheater[1].heat_duty[0])
+
+    @m.fs.boiler_fireside.Constraint(m.fs.time,
+                                     doc="Additional reheater heat duty")
+    def eq_reheater_2_heat(b, t):
+        return (
+            b.roof_heat[t] *
+            b.fcorrection_heat_ww[t] ==
+            m.fs.reheater[2].heat_duty[0])
+
+    # Fixing Coal composition
+    m.fs.boiler_fireside.mf_C_coal_dry.fix(0.718471768285811)
+    m.fs.boiler_fireside.mf_H_coal_dry.fix(0.0507156542319396)
+    m.fs.boiler_fireside.mf_O_coal_dry.fix(0.0791164206018258)
+    m.fs.boiler_fireside.mf_N_coal_dry.fix(0.0140876817310943)
+    m.fs.boiler_fireside.mf_S_coal_dry.fix(0.0282880649160374)
+    m.fs.boiler_fireside.mf_Ash_coal_dry.fix(0.109320410233292)
+    m.fs.boiler_fireside.hhv_coal_dry.fix(2.581e+007)
+    m.fs.boiler_fireside.frac_moisture_vaporized[:].fix(0.6)
+    m.fs.boiler_fireside.mf_H2O_coal_raw[:].fix(0.111367051)  # moisture
+    m.fs.boiler_fireside.flowrate_coal_raw[:].fix(100.0)  # kg/s
+
+    m.fs.boiler_fireside.wall_temperature_waterwall[:, :].fix(690)
+    m.fs.boiler_fireside.wall_temperature_platen[:].fix(750)
+    m.fs.boiler_fireside.wall_temperature_roof[:].fix(650)
+    m.fs.boiler_fireside.fcorrection_heat_ww.fix(0.95)
+    m.fs.boiler_fireside.fcorrection_heat_platen.fix(0.95)
+
+    # SCPC simulation approx flue gas = 21290.6999  # mol/s
+    flow_mol_pa = 21290.6999*0.34  # approx. 1/3 as Primary air
+    flow_mol_sa = 21290.6999*0.66  # approx. 2/3 as Secondary air
+
+    m.fs.state_args_PA = {
+        "flow_mol_comp": {
+            "H2O": 0.0078267*flow_mol_pa,
+            "CO2": 0.000337339*flow_mol_pa,
+            "N2": 0.783994*flow_mol_pa,
+            "O2": 0.20784*flow_mol_pa,
+            "SO2": 1e-5*flow_mol_pa,
+            "NO": 1e-5*flow_mol_pa
+        },
+        "temperature": 333.15,
+        "pressure": 101325.00
+    }
+
+    m.fs.state_args_SA = {
+        "flow_mol_comp": {
+            "H2O": 0.0078267*flow_mol_sa,
+            "CO2": 0.000337339*flow_mol_sa,
+            "N2": 0.783994*flow_mol_sa,
+            "O2": 0.20784*flow_mol_sa,
+            "SO2": 1e-5*flow_mol_sa,
+            "NO": 1e-5*flow_mol_sa
+        },
+        "temperature": 650.15,
+        "pressure": 101325.00
+    }
+
+    m.fs.boiler_fireside.primary_air_inlet.pressure[:].fix(101325.00)
+    m.fs.boiler_fireside.secondary_air_inlet.pressure[:].fix(101325.00)
+    m.fs.boiler_fireside.primary_air_inlet.temperature[:].fix(333.15)
+    m.fs.boiler_fireside.secondary_air_inlet.temperature[:].fix(650.15)
+    m.fs.boiler_fireside.temperature_coal[:].fix(335.15)
+    m.fs.boiler_fireside.flue_gas_outlet.temperature.setub(5000)
+    m.fs.boiler_fireside.SR.fix(1.2)
+    m.fs.boiler_fireside.ratio_PA2coal.fix(2.45)
+    m.fs.boiler_fireside.SR_lf.fix(1.0)
+    m.fs.boiler_fireside.deltaP.fix(1000)
+    return m
+
+
+def add_co2capture(m):
+    m.fs.co2_capture_unit = CO2Capture()
+
+    # Adding constraints to connect flue_gas_outlet to co2_capture_unit_inlet
+    # An arc cannot be used because of the fixed components with capture unit
+    # The following are equality constraints for component flows, pressure,
+    # and temperature.
+    # TODO: update the model with a translator block to update components
+    m.fg_comp_list = ['CO2', 'H2O', 'N2', 'O2', 'NO', 'SO2']
+    m.css_comp_list = ['CO2', 'H2O', 'N2', 'O2']
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      m.css_comp_list,
+                                      doc="Flow equality constraints")
+    def eq_flow_mol_comp(b, t, c):
+        return (
+            m.fs.boiler_fireside.flue_gas_outlet.flow_mol_comp[t, c] ==
+            m.fs.co2_capture_unit.inlet.flow_mol_comp[t, c])
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      doc="Fixing Ar flow")
+    def eq_ar_flow_mol_comp(b, t):
+        return (
+            m.fs.co2_capture_unit.inlet.flow_mol_comp[t, 'Ar'] == 0.0089 *
+            sum(m.fs.boiler_fireside.flue_gas_outlet.flow_mol_comp[t, c]
+                for c in m.fg_comp_list))
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      doc="Temperature equality constraints")
+    def eq_temperature(b, t):
+        return (
+            m.fs.boiler_fireside.flue_gas_outlet.temperature[t] ==
+            m.fs.co2_capture_unit.inlet.temperature[t])
+
+    @m.fs.co2_capture_unit.Constraint(m.fs.time,
+                                      doc="Pressure equality constraints")
+    def eq_pressure(b, t):
+        return (
+            m.fs.boiler_fireside.flue_gas_outlet.pressure[t] ==
+            m.fs.co2_capture_unit.inlet.pressure[t])
+
+    m.fs.co2_capture_unit.CO2_capture_rate.fix(0.9)  # 90 % CO2 Capture
+    m.fs.co2_capture_unit.Pz_mol.fix(5)
+    m.fs.co2_capture_unit.lean_loading.fix(0.25)
+
+    return m
+
+
+def initialize_usc_w_capture(m, fileinput=None, outlvl=idaeslog.NOTSET,
+                             solver=None, optarg={}):
+
+    iscale.calculate_scaling_factors(m)
+
+    m.fs.boiler.heat_duty[0].fix()
+    m.fs.reheater[1].heat_duty[0].fix()
+    m.fs.reheater[2].heat_duty[0].fix()
+
+    m.fs.boiler_fireside.initialize(
+        state_args_PA=m.fs.state_args_PA,
+        state_args_SA=m.fs.state_args_SA)
+
+    m.fs.boiler.heat_duty[0].unfix()
+    m.fs.reheater[1].heat_duty[0].unfix()
+    m.fs.reheater[2].heat_duty[0].unfix()
+
+    # The initialize method in CO2Capture unit model fixes inlet state
+    # and does not unfix it. So, to use the initialize method, the
+    # constraints are deactivated before initializing and activated later.
+    # The inlet state is unfixed before activating the constraints.
+    # TODO: update this section when the initialize method in CO2Capture model
+    # is updated.
+    m.fs.co2_capture_unit.eq_flow_mol_comp.deactivate()
+    m.fs.co2_capture_unit.eq_ar_flow_mol_comp.deactivate()
+    m.fs.co2_capture_unit.eq_temperature.deactivate()
+    m.fs.co2_capture_unit.eq_pressure.deactivate()
+    m.fs.co2_capture_unit.inlet.temperature[:].fix(303.1)  # K (30 C)
+    m.fs.co2_capture_unit.inlet.pressure[:].fix(101325)  # Pa (1 atm)
+
+    m.fs.co2_capture_unit.initialize(outlvl=idaeslog.INFO)
+
+    m.fs.co2_capture_unit.inlet.temperature[:].unfix()  # K (30 C)
+    m.fs.co2_capture_unit.inlet.pressure[:].unfix()  # Pa (1 atm)
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'CO2'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'O2'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'Ar'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'H2O'].unfix()
+    m.fs.co2_capture_unit.inlet.flow_mol_comp[:, 'N2'].unfix()
+    m.fs.co2_capture_unit.eq_flow_mol_comp.activate()
+    m.fs.co2_capture_unit.eq_ar_flow_mol_comp.activate()
+    m.fs.co2_capture_unit.eq_temperature.activate()
+    m.fs.co2_capture_unit.eq_pressure.activate()
+
+    res = solver.solve(m)
+    print("Model Initialization = ",
+          res.solver.termination_condition)
+    print("**************  USC model w Capture Initialized   ***************")
+
+
+def build_usc_w_ccs():
+
+    m = usc.build_plant_model()
+    solver = usc.initialize(m)
+    m = add_fireside(m)
+    m = add_co2capture(m)
+    assert degrees_of_freedom(m) == 0
+
+    initialize_usc_w_capture(m, solver=solver)
+    assert degrees_of_freedom(m) == 0
+
+    return m
+
+
+if __name__ == "__main__":
+
+    optarg = {
+        "max_iter": 300,
+        "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver("ipopt", optarg)
+
+    m = build_usc_w_ccs()
+
+    results = solver.solve(m, tee=True)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/usc_storage_gdp_mp_unfixed_area_new_storage_design.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/usc_storage_gdp_mp_unfixed_area_new_storage_design.py
new file mode 100644
index 000000000..98546d4c0
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/usc_storage_gdp_mp_unfixed_area_new_storage_design.py
@@ -0,0 +1,2123 @@
+##############################################################################
+# DISPATCHES was produced under the DOE Design Integration and Synthesis
+# Platform to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES),
+# and is copyright (c) 2021 by the software owners: The Regents of the University
+# of California, through Lawrence Berkeley National Laboratory, National
+# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
+# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
+# al. All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. Both files are also available online at the URL:
+# "https://github.com/gmlc-dispatches/dispatches".
+#
+##############################################################################
+
+"""This is a GDP model for the conceptual design of an ultra
+supercritical coal-fired power plant based on a flowsheet presented in
+1999 USDOE Report #DOE/FE-0400
+
+This model uses some of the simpler unit models from the power
+generation unit model library.
+
+Some of the parameters in the model such as feed water heater areas,
+overall heat transfer coefficient, turbine efficiencies at multiple
+stages have all been estimated for a total power out of 437 MW.
+
+Additional main assumptions are as follows:
+(1) The flowsheet and main steam conditions, i. e. pressure &
+    temperature are adopted from the aforementioned DOE report
+(2) Heater unit models are used to model main steam boiler, reheater,
+    and condenser.
+(3) Multi-stage turbines are modeled as multiple lumped single stage
+    turbines
+
+"""
+
+__author__ = "Soraya Rawlings"
+
+# Import Python libraries
+from math import pi
+import logging
+import json
+
+# Import Pyomo libraries
+import pyomo.environ as pyo
+from pyomo.environ import (Block, Param, Constraint, Objective, Reals,
+                           NonNegativeReals, TransformationFactory, Expression,
+                           maximize, RangeSet, value, log, Var, SolverFactory)
+from pyomo.gdp import Disjunct, Disjunction
+from pyomo.environ import units as pyunits
+from pyomo.network import Arc
+from pyomo.network.plugins import expand_arcs
+from pyomo.util.calc_var_value import calculate_variable_from_constraint
+from pyomo.util.infeasible import (log_infeasible_constraints,
+                                   log_close_to_bounds)
+from pyomo.contrib.fbbt.fbbt import _prop_bnds_root_to_leaf_map
+from pyomo.core.expr.numeric_expr import ExternalFunctionExpression
+
+# Import IDAES libraries
+import idaes.logger as idaeslog
+import idaes.core.util.scaling as iscale
+from idaes.core import MaterialBalanceType
+from idaes.core.util.initialization import propagate_state
+from idaes.core.solvers.get_solver import get_solver
+from idaes.core.util import model_serializer as ms
+from idaes.core.util.model_statistics import degrees_of_freedom
+from idaes.models.unit_models import (HeatExchanger,
+                                      MomentumMixingType,
+                                      Heater)
+from idaes.models.unit_models import PressureChanger
+from idaes.models.unit_models.heat_exchanger import delta_temperature_underwood_callback
+from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption
+from idaes.models_extra.power_generation.unit_models.helm import (HelmMixer,
+                                                                  HelmTurbineStage,
+                                                                  HelmSplitter)
+from idaes.core import UnitModelCostingBlock
+from idaes.models.costing.SSLW import SSLWCosting, SSLWCostingData
+from idaes.core.util.exceptions import ConfigurationError
+
+# Import ultra supercritical power plant model
+# from dispatches.models.fossil_case.ultra_supercritical_plant import (
+#     ultra_supercritical_powerplant_mixcon as usc)
+from dispatches.case_studies.fossil_case.ultra_supercritical_plant import (
+    ultra_supercritical_powerplant as usc)
+
+# Import Solar salt property package
+from dispatches.properties import solarsalt_properties
+
+from IPython import embed
+logging.basicConfig(level=logging.INFO)
+
+
+# Open json file to add data to the model
+with open('uscp_design_data_new_storage_design.json') as design_data:
+    design_data_dict = json.load(design_data)
+
+
+def create_gdp_model(m,
+                     method=None,
+                     max_power=None,
+                     deact_arcs_after_init=None,
+                     energy_loss=None):
+    """Create flowsheet and add unit models.
+    """
+
+    # Add data to the model
+    add_data(m)
+
+    # Add molten salt properties (Solar and Hitec salt)
+    m.fs.solar_salt_properties = solarsalt_properties.SolarsaltParameterBlock()
+
+    ###########################################################################
+    # Add global variables
+    ###########################################################################
+
+    m.fs.salt_amount = pyo.Param(
+        initialize=m.max_salt_amount,
+        doc="Solar salt amount in mton"
+    )
+
+    m.fs.salt_storage = pyo.Var(
+        bounds=(-m.max_salt_flow, m.max_salt_flow),
+        initialize=1,
+        doc="Solar salt amount in storage"
+    )
+    m.fs.hx_pump_work = pyo.Var(
+        bounds=(0, 1e3),
+        initialize=1
+    )
+    m.fs.discharge_turbine_work = pyo.Var(
+        bounds=(0, 1e3),
+        initialize=1
+    )
+
+    if energy_loss:
+        energy_loss_val = 1.5
+    else:
+        energy_loss_val = 0
+    m.fs.energy_loss = pyo.Param(
+        initialize=energy_loss_val,
+        doc="Discharge energy loss in MW"
+    )
+
+    m.fs.charge_area = pyo.Var(
+        initialize=m.hxc_area_init,
+        bounds=(m.min_area, m.max_area),
+        doc="Charge heat exchanger area in m2"
+    )
+    m.fs.hot_salt_temp = pyo.Var(
+        initialize=m.hot_salt_temp,
+        bounds=(m.min_salt_temp, m.max_salt_temp),
+        doc="Hot salt temperature from charge heat exchanger in K"
+    )
+    m.fs.discharge_area = pyo.Var(
+        initialize=m.hxd_area_init,
+        bounds=(m.min_area, m.max_area),
+        doc="Discharge heat exchanger area in m2"
+    )
+    ###########################################################################
+    # Add disjunction
+    ###########################################################################
+
+    m.fs.discharge_mode_disjunct = Disjunct(rule=discharge_mode_disjunct_equations)
+    m.fs.charge_mode_disjunct = Disjunct(rule=charge_mode_disjunct_equations)
+    m.fs.no_storage_mode_disjunct = Disjunct(rule=no_storage_mode_disjunct_equations)
+
+    ###########################################################################
+    # Add constraints
+    ###########################################################################
+
+    if deact_arcs_after_init:
+        print('           **Arcs from reheater 1 to turbine 3 and BFP to FWH8 are deactivated after initialization')
+    else:
+        print('           **Arcs from reheater 1 to turbine 3 and BFP to FWH8 are deactivated in create_gdp_model before initialization')
+        _deactivate_arcs(m)
+
+    _make_constraints(m, method=method, max_power=max_power)
+    return m
+
+def add_data(m):
+
+    # Add global data
+    m.hxc_area = design_data_dict["hxc_area"] # in MW
+    m.hxd_area = design_data_dict["hxd_area"] # in MW
+    m.min_power = design_data_dict["plant_min_power"] # in MW
+    m.max_power = design_data_dict["plant_max_power"] # in MW
+    m.ramp_rate = design_data_dict["ramp_rate"]
+    m.min_power_storage = design_data_dict["min_discharge_turbine_power"] # in MW
+    m.max_power_storage = design_data_dict["max_discharge_turbine_power"] # in MW
+    m.hot_salt_temp = design_data_dict["hot_salt_temperature"] # in K
+    # m.min_area = design_data_dict["min_storage_area_design"] # in m2
+    m.min_area = 1000
+    m.max_area = design_data_dict["max_storage_area_design"] # in m2
+    m.cold_salt_temp = design_data_dict["cold_salt_temperature"] # in K
+    m.min_storage_heat_duty = design_data_dict["min_storage_heat_duty"] # in MW
+    m.max_storage_heat_duty = design_data_dict["max_storage_heat_duty"] # in MW
+    m.min_salt_temp = design_data_dict["min_solar_salt_temperature"] # in K
+    m.max_salt_temp = design_data_dict["max_solar_salt_temperature"] # in K
+    m.max_salt_flow = design_data_dict["max_salt_flow"] # in kg/s
+    m.factor_mton = design_data_dict["factor_mton"] # factor to convert kg to metric ton
+    m.max_salt_amount = design_data_dict["max_salt_amount"] * m.factor_mton # in mton
+
+    # Add initial values
+    m.hxc_area_init = m.hxc_area
+    m.hxd_area_init = m.hxd_area
+
+    # Chemical engineering cost index for 2019
+    m.CE_index = 607.5
+
+    # Define the number of hours per day to operate the storage system
+    # and the number of years over which the capital costs are
+    # annualized
+    m.fs.hours_per_day = pyo.Param(
+        initialize=design_data_dict["operating_hours_per_day"],
+        doc='Estimated number of hours of charging per day'
+    )
+    m.fs.num_of_years = pyo.Param(
+        initialize=design_data_dict["number_of_years"],
+        doc='Number of years for capital cost annualization')
+
+    #  Define the data for the design of the storage heat
+    # exchangers. The design is: Shell-n-tube counter-flow heat
+    # exchanger design parameters. Data to compute overall heat
+    # transfer coefficient for the charge heat exchanger using the
+    # Sieder-Tate Correlation. Parameters for tube diameter and
+    # thickness assumed from the data in (2017) He et al., Energy
+    # Procedia 105, 980-985.
+    m.fs.data_storage_hx = {
+        'tube_inner_dia': 0.032,
+        'tube_outer_dia': 0.036,
+        'k_steel': 21.5,
+        'number_tubes': 20,
+        'shell_inner_dia': 1
+    }
+
+    m.fs.tube_inner_dia = pyo.Param(
+        initialize=m.fs.data_storage_hx['tube_inner_dia'],
+        units=pyunits.m,
+        doc='Tube inner diameter in m')
+    m.fs.tube_outer_dia = pyo.Param(
+        initialize=m.fs.data_storage_hx['tube_outer_dia'],
+        units=pyunits.m,
+        doc='Tube outer diameter in m')
+    m.fs.k_steel = pyo.Param(
+        initialize=m.fs.data_storage_hx['k_steel'],
+        units=pyunits.W / (pyunits.m * pyunits.K),
+        doc='Thermal conductivity of steel in W/m.K')
+    m.fs.n_tubes = pyo.Param(
+        initialize=m.fs.data_storage_hx['number_tubes'],
+        doc='Number of tubes')
+    m.fs.shell_inner_dia = pyo.Param(
+        initialize=m.fs.data_storage_hx['shell_inner_dia'],
+        units=pyunits.m,
+        doc='Shell inner diameter in m')
+
+    # Calculate sectional area of storage heat exchangers
+    m.fs.tube_cs_area = pyo.Expression(
+        expr=(pi / 4) *
+        (m.fs.tube_inner_dia ** 2),
+        doc="Tube cross sectional area")
+    m.fs.tube_out_area = pyo.Expression(
+        expr=(pi / 4) *
+        (m.fs.tube_outer_dia ** 2),
+        doc="Tube cross sectional area including thickness in m2")
+    m.fs.shell_eff_area = pyo.Expression(
+        expr=(
+            (pi / 4) *
+            (m.fs.shell_inner_dia ** 2) -
+            m.fs.n_tubes *
+            m.fs.tube_out_area),
+        doc="Effective shell cross sectional area in m2")
+
+    # Calculate ratios for overall heat transfer coefficients
+    m.fs.tube_dia_ratio = (m.fs.tube_outer_dia / m.fs.tube_inner_dia)
+    m.fs.log_tube_dia_ratio = log(m.fs.tube_dia_ratio)
+
+    # Data for main flowsheet operation. The q baseline_charge
+    # corresponds to heat duty of a plant with no storage and
+    # producing 400 MW power
+    m.data_cost = {
+        'coal_price': 2.11e-9,
+        'solar_salt_price': 0.49,
+    }
+    m.fs.coal_price = pyo.Param(
+        initialize=m.data_cost['coal_price'],
+        doc='Coal price based on HHV for Illinois No.6 (NETL Report) in $/J')
+    m.fs.solar_salt_price = pyo.Param(
+        initialize=m.data_cost['solar_salt_price'],
+        doc='Solar salt price in $/kg')
+
+
+def _make_constraints(m, method=None, max_power=None):
+
+    m.fs.production_cons.deactivate()
+    @m.fs.Constraint(m.fs.time)
+    def production_cons_with_storage(b, t):
+        return (
+            (
+                (-1e-6) * (pyunits.MW / pyunits.W) *
+                sum(b.turbine[p].work_mechanical[t] for p in m.set_turbine)
+                - b.hx_pump_work
+            ) ==
+            b.plant_power_out[t]
+        )
+
+    m.fs.net_power = pyo.Expression(
+        expr=(
+            m.fs.plant_power_out[0]
+            + m.fs.discharge_turbine_work
+        )
+    )
+
+    m.fs.boiler_efficiency = pyo.Var(
+        initialize=0.9,
+        bounds=(0, 1),
+        doc="Boiler efficiency in fraction"
+    )
+    m.fs.boiler_efficiency_eq = pyo.Constraint(
+        expr=m.fs.boiler_efficiency == (
+            0.2143 * (m.fs.net_power / max_power)
+            + 0.7357
+        ),
+        doc="Boiler efficiency in fraction"
+    )
+
+    # m.fs.coal_heat_duty = pyo.Var(
+    #     initialize=1000,
+    #     bounds=(0, 1e5),
+    #     doc="Coal heat duty supplied to boiler in MW")
+
+    if method == "with_efficiency":
+        # m.fs.coal_heat_duty_eq = pyo.Constraint(
+        #     expr=m.fs.coal_heat_duty * m.fs.boiler_efficiency ==
+        #     m.fs.plant_heat_duty[0]
+        # )
+        m.fs.coal_heat_duty = pyo.Expression(
+            expr=m.fs.plant_heat_duty[0] / m.fs.boiler_efficiency
+        )
+    else:
+        # m.fs.coal_heat_duty_eq = pyo.Constraint(
+        #     expr=m.fs.coal_heat_duty ==
+        #     m.fs.plant_heat_duty[0]
+        # )
+        m.fs.coal_heat_duty = pyo.Expression(
+            expr=m.fs.plant_heat_duty[0]
+        )
+
+    m.fs.cycle_efficiency = pyo.Var(
+        initialize=0.4,
+        bounds=(0, 1),
+        doc="Cycle efficiency in fraction"
+    )
+    m.fs.cycle_efficiency_eq = pyo.Constraint(
+        expr=m.fs.cycle_efficiency * m.fs.coal_heat_duty == m.fs.net_power,
+        doc="Cycle efficiency in fraction"
+    )
+
+
+def add_disjunction(m):
+    """Add storage fluid selection and steam source disjunctions to the
+    model
+    """
+
+    m.fs.operation_mode_disjunction = Disjunction(
+        expr=[m.fs.no_storage_mode_disjunct,
+              m.fs.charge_mode_disjunct,
+              m.fs.discharge_mode_disjunct])
+
+    # Expand arcs within the disjuncts
+    expand_arcs.obj_iter_kwds['descend_into'] = (Block, Disjunct)
+    TransformationFactory("network.expand_arcs").apply_to(m.fs)
+
+    return m
+
+
+def no_storage_mode_disjunct_equations(disj):
+    m = disj.model()
+
+    # Connect cycle
+    m.fs.no_storage_mode_disjunct.rh1_to_turb3 = Arc(
+        source=m.fs.reheater[1].outlet,
+        destination=m.fs.turbine[3].inlet,
+        doc="Connection from reheater 1 to turbine 3"
+    )
+    m.fs.no_storage_mode_disjunct.fwh9_to_boiler = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from FWH9 to boiler"
+    )
+    m.fs.no_storage_mode_disjunct.condpump_to_fwh1 = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump to FWH1"
+    )
+
+    # Set global constraints that depend on the charge and discharge
+    # operation modes to zero since the units do not exist in no
+    # storage mode
+    m.fs.no_storage_mode_disjunct.eq_salt_amount_in_storage = pyo.Constraint(
+        expr=m.fs.salt_storage == 0
+    )
+    # m.fs.no_storage_mode_disjunct.eq_cooler_heat_duty = pyo.Constraint(
+    #     expr=m.fs.cooler_heat_duty == 0
+    # )
+    m.fs.no_storage_mode_disjunct.eq_hx_pump_work = pyo.Constraint(
+        expr=m.fs.hx_pump_work == 0
+    )
+    m.fs.no_storage_mode_disjunct.eq_discharge_turbine_work = pyo.Constraint(
+        expr=m.fs.discharge_turbine_work == 0
+    )
+
+
+def charge_mode_disjunct_equations(disj):
+    m = disj.model()
+
+    # Declare units for the charge storage system: A splitter to
+    # divert some steam from high pressure inlet and intermediate
+    # pressure inlet to charge the storage heat exchanger, a charge
+    # heat exchanger, a pump, and a mixer. A pump is used to increase
+    # the pressure of the water to allow mixing it at a desired
+    # location within the plant.
+    m.fs.charge_mode_disjunct.ess_charge_split = HelmSplitter(
+        property_package=m.fs.prop_water,
+        outlet_list=["to_hxc", "to_turbine"]
+    )
+
+    m.fs.charge_mode_disjunct.hxc = HeatExchanger(
+        delta_temperature_callback=delta_temperature_underwood_callback,
+        hot_side_name="shell",
+        cold_side_name="tube",
+        shell={"property_package": m.fs.prop_water},
+        tube={"property_package": m.fs.solar_salt_properties}
+    )
+
+    m.fs.charge_mode_disjunct.hx_pump = PressureChanger(
+        property_package=m.fs.prop_water,
+        material_balance_type=MaterialBalanceType.componentTotal,
+        thermodynamic_assumption=ThermodynamicAssumption.pump,
+    )
+
+    m.fs.charge_mode_disjunct.mixer2 = HelmMixer(
+        momentum_mixing_type=MomentumMixingType.none,
+        inlet_list=["from_fwh9", "from_hx_pump"],
+        property_package=m.fs.prop_water,
+    )
+
+    # Calculate the overall heat transfer coefficient for the Solar
+    # salt charge heat exchanger. For that, first calculate Reynolds
+    # number, Prandtl number, and Nusselt number.
+    solar_hxc = m.fs.charge_mode_disjunct.hxc
+    solar_hxc.salt_reynolds_number = pyo.Expression(
+        expr=(
+            (solar_hxc.tube_inlet.flow_mass[0] *
+             m.fs.tube_outer_dia) /
+            (m.fs.shell_eff_area *
+             solar_hxc.cold_side.properties_in[0].visc_d_phase["Liq"])
+        ),
+        doc="Salt Reynolds Number")
+    solar_hxc.salt_prandtl_number = pyo.Expression(
+        expr=(
+            solar_hxc.cold_side.properties_in[0].cp_mass["Liq"] *
+            solar_hxc.cold_side.properties_in[0].visc_d_phase["Liq"] /
+            solar_hxc.cold_side.properties_in[0].therm_cond_phase["Liq"]
+        ),
+        doc="Salt Prandtl Number")
+    solar_hxc.salt_prandtl_wall = pyo.Expression(
+        expr=(
+            solar_hxc.cold_side.properties_out[0].cp_mass["Liq"] *
+            solar_hxc.cold_side.properties_out[0].visc_d_phase["Liq"] /
+            solar_hxc.cold_side.properties_out[0].therm_cond_phase["Liq"]
+        ),
+        doc="Salt Prandtl Number at wall")
+    solar_hxc.salt_nusselt_number = pyo.Expression(
+        expr=(
+            0.35 *
+            (solar_hxc.salt_reynolds_number**0.6) *
+            (solar_hxc.salt_prandtl_number**0.4) *
+            ((solar_hxc.salt_prandtl_number /
+              solar_hxc.salt_prandtl_wall) ** 0.25) *
+            (2**0.2)
+        ),
+        doc="Salt Nusslet Number from 2019, App Ener (233-234), 126")
+    solar_hxc.steam_reynolds_number = pyo.Expression(
+        expr=(
+            solar_hxc.shell_inlet.flow_mol[0] *
+            solar_hxc.hot_side.properties_in[0].mw *
+            m.fs.tube_inner_dia /
+            (m.fs.tube_cs_area *
+             m.fs.n_tubes *
+             solar_hxc.hot_side.properties_in[0].visc_d_phase["Vap"])
+        ),
+        doc="Steam Reynolds Number")
+    solar_hxc.steam_prandtl_number = pyo.Expression(
+        expr=(
+            (solar_hxc.hot_side.properties_in[0].cp_mol /
+             solar_hxc.hot_side.properties_in[0].mw) *
+            solar_hxc.hot_side.properties_in[0].visc_d_phase["Vap"] /
+            solar_hxc.hot_side.properties_in[0].therm_cond_phase["Vap"]
+        ),
+        doc="Steam Prandtl Number")
+    solar_hxc.steam_nusselt_number = pyo.Expression(
+        expr=(
+            0.023 *
+            (solar_hxc.steam_reynolds_number**0.8) *
+            (solar_hxc.steam_prandtl_number**(0.33)) *
+            ((solar_hxc.hot_side.properties_in[0].visc_d_phase["Vap"] /
+              solar_hxc.hot_side.properties_out[0].visc_d_phase["Liq"]) ** 0.14)
+        ),
+        doc="Steam Nusslet Number from 2001 Zavoico, Sandia")
+
+    # Calculate heat transfer coefficients for the salt and steam
+    # sides of charge heat exchanger
+    solar_hxc.h_salt = pyo.Expression(
+        expr=(
+            solar_hxc.cold_side.properties_in[0].therm_cond_phase["Liq"] *
+            solar_hxc.salt_nusselt_number /
+            m.fs.tube_outer_dia
+        ),
+        doc="Salt side convective heat transfer coefficient [W/mK]")
+    solar_hxc.h_steam = pyo.Expression(
+        expr=(
+            solar_hxc.hot_side.properties_in[0].therm_cond_phase["Vap"] *
+            solar_hxc.steam_nusselt_number /
+            m.fs.tube_inner_dia
+        ),
+        doc="Steam side convective heat transfer coefficient [W/mK]")
+
+    @m.fs.charge_mode_disjunct.hxc.Constraint(
+        doc="Solar salt charge heat exchanger overall heat transfer coefficient")
+    def constraint_hxc_ohtc(b):
+        return (
+            b.overall_heat_transfer_coefficient[0] *
+            (2 * m.fs.k_steel *
+             b.h_steam +
+             m.fs.tube_outer_dia *
+             m.fs.log_tube_dia_ratio *
+             b.h_salt *
+             b.h_steam +
+             m.fs.tube_dia_ratio *
+             b.h_salt *
+             2 * m.fs.k_steel)
+        ) == (2 * m.fs.k_steel *
+              b.h_salt *
+              b.h_steam)
+
+
+    # Add constraint to ensure that the mixer 2 outlet is at the
+    # minimum pressure
+    m.fs.charge_mode_disjunct.mixer2_pressure_constraint = pyo.Constraint(
+        expr=m.fs.charge_mode_disjunct.mixer2.from_fwh9_state[0].pressure ==
+        m.fs.charge_mode_disjunct.mixer2.mixed_state[0].pressure,
+        doc="Mixer 2 outlet pressure equal to min inlet pressure")
+
+    # Add pump pressure constraint
+    m.fs.charge_mode_disjunct.constraint_hxpump_presout = pyo.Constraint(
+        expr=m.fs.charge_mode_disjunct.hx_pump.outlet.pressure[0] >=
+        m.main_steam_pressure * 1.1231
+        # expr=m.fs.charge_mode_disjunct.hx_pump.outlet.pressure[0] ==
+        # m.main_steam_pressure * 1.1231
+    )
+
+    # Reconnect condenser pump to FWH1
+    m.fs.charge_mode_disjunct.condpump_to_fwh1 = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump to FWH1"
+    )
+
+    # Declare arcs to connect storage charge system to the plant
+    m.fs.charge_mode_disjunct.rh1_to_esscharg = Arc(
+        source=m.fs.reheater[1].outlet,
+        destination=m.fs.charge_mode_disjunct.ess_charge_split.inlet,
+        doc="Connection from reheater 1 to HP splitter"
+    )
+    m.fs.charge_mode_disjunct.esscharg_to_turb3 = Arc(
+        source=m.fs.charge_mode_disjunct.ess_charge_split.to_turbine,
+        destination=m.fs.turbine[3].inlet,
+        doc="Connection from HP splitter to turbine 3"
+    )
+    m.fs.charge_mode_disjunct.esscharg_to_hxc = Arc(
+        source=m.fs.charge_mode_disjunct.ess_charge_split.to_hxc,
+        destination=m.fs.charge_mode_disjunct.hxc.shell_inlet,
+        doc="Connection from HP splitter to HXC inlet 1"
+    )
+    m.fs.charge_mode_disjunct.hxc_to_hxpump = Arc(
+        source=m.fs.charge_mode_disjunct.hxc.shell_outlet,
+        destination=m.fs.charge_mode_disjunct.hx_pump.inlet,
+        doc="Connection from HXC to HX pump"
+    )
+
+    # Declare arcs to connect the mixer 2 to the plant
+    m.fs.charge_mode_disjunct.fwh9_to_mix2 = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.charge_mode_disjunct.mixer2.from_fwh9,
+        doc="Connection from FWH9 outlet to mixer2"
+    )
+    m.fs.charge_mode_disjunct.hxpump_to_mix2 = Arc(
+        source=m.fs.charge_mode_disjunct.hx_pump.outlet,
+        destination=m.fs.charge_mode_disjunct.mixer2.from_hx_pump,
+        doc="Connection from HX pump to mixer 2"
+    )
+    m.fs.charge_mode_disjunct.mix2_to_boiler = Arc(
+        source=m.fs.charge_mode_disjunct.mixer2.outlet,
+        destination=m.fs.boiler.inlet,
+        doc="Connection from fwh9 outlet to boiler"
+    )
+
+    # Declare constraints to save global variables
+    m.fs.charge_mode_disjunct.eq_salt_amount_in_charge_storage = pyo.Constraint(
+        expr=m.fs.salt_storage == m.fs.charge_mode_disjunct.hxc.tube_inlet.flow_mass[0]
+    )
+    m.fs.charge_mode_disjunct.eq_hx_pump_work = pyo.Constraint(
+        expr=m.fs.hx_pump_work == (
+            (1e-6) * (pyunits.MW / pyunits.W) *
+            m.fs.charge_mode_disjunct.hx_pump.control_volume.work[0]
+        )
+    )
+    m.fs.charge_mode_disjunct.eq_discharge_turbine_work = pyo.Constraint(
+        expr=m.fs.discharge_turbine_work == 0
+    )
+
+    # m.fs.charge_mode_disjunct.eq_charge_heat_duty = pyo.Constraint(
+    #     expr=(
+    #         (1e-6) * (pyunits.MW / pyunits.W) *
+    #         m.fs.charge_mode_disjunct.hxc.heat_duty[0]
+    #     ) <= m.max_storage_heat_duty
+    # )
+
+    # Save area and hot salt temperature in global variable
+    m.fs.charge_mode_disjunct.eq_charge_area = pyo.Constraint(
+        expr=m.fs.charge_area == m.fs.charge_mode_disjunct.hxc.area
+    )
+    m.fs.charge_mode_disjunct.eq_hot_salt_temperature = pyo.Constraint(
+        expr=m.fs.hot_salt_temp == m.fs.charge_mode_disjunct.hxc.tube_outlet.temperature[0]
+    )
+
+
+def discharge_mode_disjunct_equations(disj):
+    m = disj.model()
+
+    # Declare units for the discharge storage system: A splitter to
+    # divert some condensate from the feed water heater train to be
+    # heated up in the discharge heat exchanger, a discharge heat
+    # exchanger, and a turbine to produce extra energy.
+    m.fs.discharge_mode_disjunct.ess_discharge_split = HelmSplitter(
+        property_package=m.fs.prop_water,
+        outlet_list=["to_hxd", "to_fwh1"]
+    )
+
+    m.fs.discharge_mode_disjunct.hxd = HeatExchanger(
+        delta_temperature_callback=delta_temperature_underwood_callback,
+        hot_side_name="shell",
+        cold_side_name="tube",
+        shell={"property_package": m.fs.solar_salt_properties},
+        tube={"property_package": m.fs.prop_water}
+    )
+
+    m.fs.discharge_mode_disjunct.es_turbine = HelmTurbineStage(
+        property_package=m.fs.prop_water
+    )
+    m.fs.discharge_mode_disjunct.eq_turbine_temperature_out = pyo.Constraint(
+        expr=(
+            m.fs.discharge_mode_disjunct.es_turbine.control_volume.properties_out[0].temperature ==
+            m.fs.discharge_mode_disjunct.es_turbine.control_volume.properties_out[0].temperature_sat + 1
+        )
+    )
+
+    # Calculate the overall heat transfer coefficient for the Solar
+    # salt charge heat exchanger. For that, first calculate Reynolds
+    # number, Prandtl number, and Nusselt number.
+    solar_hxd = m.fs.discharge_mode_disjunct.hxd
+    solar_hxd.salt_reynolds_number = pyo.Expression(
+        expr=(
+            solar_hxd.shell_inlet.flow_mass[0]
+            * m.fs.tube_outer_dia
+            / (m.fs.shell_eff_area
+               * solar_hxd.hot_side.properties_in[0].visc_d_phase["Liq"])
+        ),
+        doc="Salt Reynolds Number"
+    )
+    solar_hxd.salt_prandtl_number = pyo.Expression(
+        expr=(
+            solar_hxd.hot_side.properties_in[0].cp_mass["Liq"]
+            * solar_hxd.hot_side.properties_in[0].visc_d_phase["Liq"]
+            / solar_hxd.hot_side.properties_in[0].therm_cond_phase["Liq"]
+        ),
+        doc="Salt Prandtl Number"
+    )
+    # Assuming that the wall conditions are same as those at the outlet
+    solar_hxd.salt_prandtl_wall = pyo.Expression(
+        expr=(
+            solar_hxd.hot_side.properties_out[0].cp_mass["Liq"]
+            * solar_hxd.hot_side.properties_out[0].visc_d_phase["Liq"]
+            / solar_hxd.hot_side.properties_out[0].therm_cond_phase["Liq"]
+        ),
+        doc="Wall Salt Prandtl Number"
+    )
+    solar_hxd.salt_nusselt_number = pyo.Expression(
+        expr=(
+            0.35 * (solar_hxd.salt_reynolds_number**0.6)
+            * (solar_hxd.salt_prandtl_number**0.4)
+            * ((solar_hxd.salt_prandtl_number
+                / solar_hxd.salt_prandtl_wall)**0.25)
+            * (2**0.2)
+        ),
+        doc="Solar Salt Nusslet Number from 2019, App Ener (233-234), 126"
+    )
+    solar_hxd.steam_reynolds_number = pyo.Expression(
+        expr=(
+            solar_hxd.tube_inlet.flow_mol[0]
+            * solar_hxd.cold_side.properties_in[0].mw
+            * m.fs.tube_inner_dia
+            / (m.fs.tube_cs_area
+               * m.fs.n_tubes
+               * solar_hxd.cold_side.properties_in[0].visc_d_phase["Liq"])
+        ),
+        doc="Steam Reynolds Number"
+    )
+    solar_hxd.steam_prandtl_number = pyo.Expression(
+        expr=(
+            (solar_hxd.cold_side.properties_in[0].cp_mol
+             / solar_hxd.cold_side.properties_in[0].mw)
+            * solar_hxd.cold_side.properties_in[0].visc_d_phase["Liq"]
+            / solar_hxd.cold_side.properties_in[0].therm_cond_phase["Liq"]
+        ),
+        doc="Steam Prandtl Number"
+    )
+    solar_hxd.steam_nusselt_number = pyo.Expression(
+        expr=(
+            0.023 * (solar_hxd.steam_reynolds_number ** 0.8)
+            * (solar_hxd.steam_prandtl_number ** (0.33))
+            * ((solar_hxd.cold_side.properties_in[0].visc_d_phase["Liq"]
+                / solar_hxd.cold_side.properties_out[0].visc_d_phase["Vap"]
+                ) ** 0.14)
+        ),
+        doc="Steam Nusslet Number from 2001 Zavoico, Sandia"
+    )
+
+    # Calculate discharge heat exchanger salt and steam side heat
+    # transfer coefficients
+    solar_hxd.h_salt = pyo.Expression(
+        expr=(
+            solar_hxd.hot_side.properties_in[0].therm_cond_phase["Liq"]
+            * solar_hxd.salt_nusselt_number / m.fs.tube_outer_dia
+        ),
+        doc="Salt side convective heat transfer coefficient [W/mK]"
+    )
+    solar_hxd.h_steam = pyo.Expression(
+        expr=(
+            solar_hxd.cold_side.properties_in[0].therm_cond_phase["Liq"]
+            * solar_hxd.steam_nusselt_number / m.fs.tube_inner_dia
+        ),
+        doc="Steam side convective heat transfer coefficient [W/mK]"
+    )
+
+    @m.fs.discharge_mode_disjunct.hxd.Constraint(
+        doc="Solar salt discharge heat exchanger overall heat transfer coefficient")
+    def constraint_hxd_ohtc(b):
+        return (
+            b.overall_heat_transfer_coefficient[0] *
+            (2 * m.fs.k_steel *
+             b.h_steam +
+             m.fs.tube_outer_dia *
+             m.fs.log_tube_dia_ratio *
+             b.h_salt *
+             b.h_steam +
+             m.fs.tube_dia_ratio *
+             b.h_salt *
+             2 * m.fs.k_steel)
+        ) == (2 * m.fs.k_steel *
+              b.h_salt *
+              b.h_steam)
+
+    # Reconnect arcs that were disconnected in the global model
+    m.fs.discharge_mode_disjunct.rh1_to_turb3 = Arc(
+        source=m.fs.reheater[1].outlet,
+        destination=m.fs.turbine[3].inlet
+    )
+    m.fs.discharge_mode_disjunct.fwh9_to_boiler = Arc(
+        source=m.fs.fwh[9].tube_outlet,
+        destination=m.fs.boiler.inlet
+    )
+
+    # Declare arcs to connect discharge heat exchanger to plant
+    m.fs.discharge_mode_disjunct.condpump_to_essdisch = Arc(
+        source=m.fs.cond_pump.outlet,
+        destination=m.fs.discharge_mode_disjunct.ess_discharge_split.inlet,
+        doc="Connection from condenser pump outlet to BFP splitter"
+    )
+    m.fs.discharge_mode_disjunct.essdisch_to_fwh1 = Arc(
+        source=m.fs.discharge_mode_disjunct.ess_discharge_split.to_fwh1,
+        destination=m.fs.fwh[1].tube_inlet,
+        doc="Connection from condenser pump splitter to FWH1"
+    )
+    m.fs.discharge_mode_disjunct.essdisch_to_hxd = Arc(
+        source=m.fs.discharge_mode_disjunct.ess_discharge_split.to_hxd,
+        destination=m.fs.discharge_mode_disjunct.hxd.tube_inlet,
+        doc="Connection from condenser pump splitter to discharge heat exchanger"
+    )
+    m.fs.discharge_mode_disjunct.hxd_to_esturbine = Arc(
+        source=m.fs.discharge_mode_disjunct.hxd.tube_outlet,
+        destination=m.fs.discharge_mode_disjunct.es_turbine.inlet,
+        doc="Connection from discharge heat exchanger to ES turbine"
+    )
+
+
+    # Save the amount of salt used in the discharge heat exchanger
+    m.fs.discharge_mode_disjunct.eq_salt_amount_in_discharge_storage = pyo.Constraint(
+        expr=m.fs.salt_storage == -m.fs.discharge_mode_disjunct.hxd.shell_inlet.flow_mass[0]
+    )
+
+    # Fix HX pump work to zero since it does not exist during
+    # discharge mode and the value is saved in a global variable
+    m.fs.discharge_mode_disjunct.eq_hx_pump_work = pyo.Constraint(
+        expr=m.fs.hx_pump_work == 0
+    )
+
+    m.fs.discharge_mode_disjunct.eq_discharge_turbine_work = pyo.Constraint(
+        expr=m.fs.discharge_turbine_work == (
+            (-1e-6) * (pyunits.MW / pyunits.W) *
+            m.fs.discharge_mode_disjunct.es_turbine.work[0]
+        )
+    )
+
+    # m.fs.discharge_mode_disjunct.eq_discharge_heat_duty = pyo.Constraint(
+    #     expr=(
+    #         (1e-6) * (pyunits.MW / pyunits.W) *
+    #         m.fs.discharge_mode_disjunct.hxd.heat_duty[0] +
+    #         m.fs.energy_loss
+    #     ) <= m.max_storage_heat_duty
+    # )
+
+    m.fs.discharge_mode_disjunct.eq_charge_area = pyo.Constraint(
+        expr=m.fs.discharge_area == m.fs.discharge_mode_disjunct.hxd.area
+    )
+
+
+def _deactivate_arcs(m):
+    """Deactivate arcs"""
+
+    # Disconnect arcs from ultra supercritical plant base model to
+    # connect the charge heat exchanger
+    for arc_s in [m.fs.rh1_to_turb3,
+                  m.fs.fwh9_to_boiler,
+                  m.fs.condpump_to_fwh1]:
+        arc_s.expanded_block.enth_mol_equality.deactivate()
+        arc_s.expanded_block.flow_mol_equality.deactivate()
+        arc_s.expanded_block.pressure_equality.deactivate()
+
+
+def set_model_input(m):
+    """Define model inputs and fixed variables or parameter values
+
+    The parameter values in this block, unless otherwise stated
+    explicitly, are either assumed or estimated for a total power out
+    of 437 MW.
+
+    Unless stated otherwise, the units are: temperature in K, pressure
+    in Pa, flow in mol/s, massic flow in kg/s, and heat and heat duty
+    in W
+
+    """
+
+    ###########################################################################
+    #  Storage heat exchanger section
+    ###########################################################################
+    # Add heat exchanger area from supercritical plant model_input. For
+    # conceptual design optimization, area is unfixed and optimized
+    m.fs.charge_mode_disjunct.hxc.area.fix(2500)
+    m.fs.discharge_mode_disjunct.hxd.area.fix(2500)
+
+    # Define storage fluid conditions. The fluid inlet flow is fixed
+    # during initialization, but is unfixed and determined during
+    # optimization
+    m.fs.charge_mode_disjunct.hxc.tube_inlet.flow_mass.fix(140)
+    m.fs.charge_mode_disjunct.hxc.tube_inlet.temperature.fix(513.15)
+    m.fs.charge_mode_disjunct.hxc.tube_inlet.pressure.fix(101325)
+
+    m.fs.discharge_mode_disjunct.hxd.shell_inlet.flow_mass.fix(200)
+    m.fs.discharge_mode_disjunct.hxd.shell_inlet.temperature.fix(853.15)
+    m.fs.discharge_mode_disjunct.hxd.shell_inlet.pressure.fix(101325)
+
+    # HX pump efficiecncy assumption
+    m.fs.charge_mode_disjunct.hx_pump.efficiency_pump.fix(0.80)
+    # m.fs.charge.hx_pump.outlet.pressure[0].fix(m.main_steam_pressure * 1.1231)
+
+    # m.fs.discharge_mode_disjunct.es_turbine.ratioP.fix(0.0286)
+    m.fs.discharge_mode_disjunct.es_turbine.efficiency_isentropic.fix(0.8)
+
+    ###########################################################################
+    #  ESS VHP and HP splitters                                               #
+    ###########################################################################
+    # The model is built for a fixed flow of steam through the
+    # charger.  This flow of steam to the charger is unfixed and
+    # determine during design optimization
+    m.fs.charge_mode_disjunct.ess_charge_split.split_fraction[0, "to_hxc"].fix(0.1)
+    m.fs.discharge_mode_disjunct.ess_discharge_split.split_fraction[0, "to_hxd"].fix(0.1)
+
+    # Fix global variables
+    # m.fs.hx_pump_work.fix(0)
+    # m.fs.discharge_turbine_work.fix(0)
+
+
+def set_scaling_factors(m):
+    """Scaling factors in the flowsheet
+
+    """
+
+    # Include scaling factors for Solar salt charge and discharge heat
+    # exchanger.
+    for fluid in [m.fs.charge_mode_disjunct.hxc,
+                  m.fs.discharge_mode_disjunct.hxd]:
+        iscale.set_scaling_factor(fluid.area, 1e-2)
+        iscale.set_scaling_factor(
+            fluid.overall_heat_transfer_coefficient, 1e-3)
+        iscale.set_scaling_factor(fluid.shell.heat, 1e-6)
+        iscale.set_scaling_factor(fluid.tube.heat, 1e-6)
+
+    iscale.set_scaling_factor(m.fs.charge_mode_disjunct.hx_pump.control_volume.work, 1e-6)
+    iscale.set_scaling_factor(m.fs.discharge_mode_disjunct.es_turbine.control_volume.work, 1e-6)
+
+    # Calculate scaling factors
+    iscale.calculate_scaling_factors(m)
+
+
+def set_var_scaling(m):
+    iscale.set_scaling_factor(m.fs.fuel_cost, 1e-3)
+    iscale.set_scaling_factor(m.fs.plant_fixed_operating_cost, 1e-3)
+    iscale.set_scaling_factor(m.fs.plant_variable_operating_cost, 1e-3)
+    # iscale.set_scaling_factor(m.fs.plant_capital_cost, 1e-3)
+
+    iscale.set_scaling_factor(m.fs.salt_amount, 1e-3)
+    iscale.set_scaling_factor(m.fs.salt_inventory_hot, 1e-3)
+    iscale.set_scaling_factor(m.fs.salt_inventory_cold, 1e-3)
+    iscale.set_scaling_factor(m.fs.previous_salt_inventory_hot, 1e-3)
+    iscale.set_scaling_factor(m.fs.previous_salt_inventory_cold, 1e-3)
+
+    iscale.set_scaling_factor(m.fs.constraint_salt_inventory_hot, 1e-3)
+
+    iscale.set_scaling_factor(m.fs.charge_mode_disjunct.capital_cost, 1e-3)
+    iscale.set_scaling_factor(m.fs.discharge_mode_disjunct.capital_cost, 1e-3)
+    # iscale.set_scaling_factor(m.fs.storage_capital_cost, 1e-3)
+
+    # Calculate scaling factors
+    iscale.calculate_scaling_factors(m)
+
+
+def initialize(m,
+               solver=None,
+               deact_arcs_after_init=None,
+               outlvl=idaeslog.WARNING,
+               optarg={"tol": 1e-8, "max_iter": 300}):
+    """Initialize the units included in the charge model
+    """
+    print()
+    print('>> Start initialization of charge units in ultra-supercritical plant')
+    # print('   {} DOFs before initialization'.format(degrees_of_freedom(m)))
+
+    optarg = {
+        "max_iter": 300,
+        "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver(solver, optarg)
+
+    # Include scaling factors
+    set_scaling_factors(m)
+    # iscale.calculate_scaling_factors(m)
+
+    # Initialize all units in charge mode operation
+    propagate_state(m.fs.charge_mode_disjunct.rh1_to_esscharg)
+    m.fs.charge_mode_disjunct.ess_charge_split.initialize(outlvl=outlvl,
+                                                          optarg=solver.options)
+    propagate_state(m.fs.charge_mode_disjunct.esscharg_to_hxc)
+    m.fs.charge_mode_disjunct.hxc.initialize(outlvl=outlvl,
+                                             optarg=solver.options)
+
+    if not deact_arcs_after_init:
+        # Reinitialize and fix turbine 3 inlet since the arc is
+        # disconnected
+        propagate_state(m.fs.charge_mode_disjunct.esscharg_to_turb3)
+        m.fs.turbine[3].inlet.fix()
+        m.fs.turbine[3].initialize(outlvl=outlvl,
+                                   optarg=solver.options)
+
+    propagate_state(m.fs.charge_mode_disjunct.hxc_to_hxpump)
+    m.fs.charge_mode_disjunct.hx_pump.initialize(outlvl=outlvl,
+                                                 optarg=solver.options)
+
+    # Fix value of global variable
+    m.fs.hx_pump_work.fix((1e-6) * (pyunits.MW / pyunits.W) *
+                          m.fs.charge_mode_disjunct.hx_pump.control_volume.work[0].value)
+
+    propagate_state(m.fs.charge_mode_disjunct.fwh9_to_mix2)
+    propagate_state(m.fs.charge_mode_disjunct.hxpump_to_mix2)
+    m.fs.charge_mode_disjunct.mixer2.initialize(outlvl=outlvl)
+
+    # Initialize all units in discharge mode operation
+    propagate_state(m.fs.discharge_mode_disjunct.condpump_to_essdisch)
+    m.fs.discharge_mode_disjunct.ess_discharge_split.initialize(outlvl=outlvl,
+                                                                optarg=solver.options)
+    propagate_state(m.fs.discharge_mode_disjunct.essdisch_to_hxd)
+    m.fs.discharge_mode_disjunct.hxd.initialize(outlvl=outlvl,
+                                                optarg=solver.options)
+    propagate_state(m.fs.discharge_mode_disjunct.hxd_to_esturbine)
+    m.fs.discharge_mode_disjunct.es_turbine.initialize(outlvl=outlvl,
+                                                       optarg=solver.options)
+    # Fix value of global variable
+    m.fs.discharge_turbine_work.fix((-1e-6) * (pyunits.MW / pyunits.W) *
+                                    m.fs.discharge_mode_disjunct.es_turbine.work[0].value)
+
+    if not deact_arcs_after_init:
+        # Reinitialize FWH8 using bfp outlet
+        m.fs.fwh[8].fwh_vfrac_constraint.deactivate()
+        m.fs.fwh[8].tube_inlet.flow_mol.fix(m.fs.bfp.outlet.flow_mol[0])
+        m.fs.fwh[8].tube_inlet.enth_mol.fix(m.fs.bfp.outlet.enth_mol[0])
+        m.fs.fwh[8].tube_inlet.pressure.fix(m.fs.bfp.outlet.pressure[0])
+        m.fs.fwh[8].initialize(outlvl=outlvl,
+                               optarg=solver.options)
+        m.fs.fwh[8].fwh_vfrac_constraint.activate()
+
+    # Check and raise an error if the degrees of freedom are not 0
+    # print('   {} DOFs before initialization solution'.format(degrees_of_freedom(m)))
+    if not degrees_of_freedom(m) == 0:
+        raise ConfigurationError(
+            "The degrees of freedom after building the model are not 0. "
+            "You have {} degrees of freedom. "
+            "Please check your inputs to ensure a square problem "
+            "before initializing the model.".format(degrees_of_freedom(m))
+            )
+
+    res = solver.solve(m,
+                       tee=False,
+                       symbolic_solver_labels=True,
+                       options=optarg)
+
+    print("   **Solver termination for Charge Model Initialization:",
+          res.solver.termination_condition)
+    # print('   {} DOFs after initialization solution'.format(degrees_of_freedom(m)))
+    print('>> End initialization of charge units in ultra-supercritical plant')
+    print()
+
+
+def build_costing(m):
+    """Add cost correlations for the storage design analysis.
+
+    This function is used to estimate the capital and operatig cost of
+    integrating an energy storage system. It contains cost
+    correlations to estimate: (i) the capital cost of charge heat
+    exchanger and salt inventory and (ii) the operating costs for 1
+    year.
+
+    """
+
+    ##############################################################
+    # Add capital cost
+    # 1. Calculate change and discharge heat exchangers costs
+    # 2. Calculate total capital cost for charge and discharge heat
+    # exchangers
+    ##############################################################
+
+    # Add IDAES costing method
+    m.fs.costing = SSLWCosting()
+
+    ###### 1. Calculate charge and discharge heat exchangers costs
+    # Calculate and initialize the Solar salt charge and discharge
+    # heat exchangers coss, which are estimated using the IDAES
+    # costing method with default options, i.e., a U-tube heat
+    # exchanger, stainless steel material, and a tube length of
+    # 12ft. Refer to costing documentation to change any of the
+    # default options. The purchase cost of heat exchanger has to be
+    # annualized when used
+    for storage_hx in [m.fs.charge_mode_disjunct.hxc,
+                       m.fs.discharge_mode_disjunct.hxd]:
+        storage_hx.costing = UnitModelCostingBlock(
+            flowsheet_costing_block=m.fs.costing,
+            costing_method=SSLWCostingData.cost_heat_exchanger
+        )
+
+    ###### 2. Calculate total capital cost for charge and discharge
+    ###### heat exchangers
+    m.fs.charge_mode_disjunct.capital_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e7),
+        doc="Capital cost of charge heat exchanger in $/h")
+    def charge_solar_cap_cost_rule(b):
+        return b.capital_cost == (
+            m.fs.charge_mode_disjunct.hxc.costing.capital_cost /
+            (m.fs.num_of_years * 365 * 24)
+        )
+    m.fs.charge_mode_disjunct.cap_cost_eq = pyo.Constraint(
+        rule=charge_solar_cap_cost_rule)
+
+    m.fs.discharge_mode_disjunct.capital_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e7),
+        doc="Capital cost of discharge heat exhcnager in $/h")
+    def discharge_solar_cap_cost_rule(b):
+        return b.capital_cost == (
+            m.fs.discharge_mode_disjunct.hxd.costing.capital_cost /
+            (m.fs.num_of_years * 365 * 24)
+        )
+    m.fs.discharge_mode_disjunct.cap_cost_eq = pyo.Constraint(
+        rule=discharge_solar_cap_cost_rule)
+
+    # Save total storage annual capital cost at global level
+    m.fs.storage_capital_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e7),
+        doc="Annualized capital cost for solar salt in $/h")
+
+    m.fs.no_storage_mode_disjunct.capital_cost_eq_constraint = pyo.Constraint(
+        expr=m.fs.storage_capital_cost == 0
+    )
+    m.fs.charge_mode_disjunct.capital_cost_eq_constraint = pyo.Constraint(
+        expr=m.fs.storage_capital_cost == m.fs.charge_mode_disjunct.capital_cost
+    )
+    m.fs.discharge_mode_disjunct.capital_cost_eq_constraint = pyo.Constraint(
+        expr=m.fs.storage_capital_cost == m.fs.discharge_mode_disjunct.capital_cost
+    )
+
+    ###########################################################################
+    #  Annual operating cost
+    ###########################################################################
+    m.fs.operating_hours = pyo.Expression(
+        expr=365 * 3600 * m.fs.hours_per_day,
+        doc="Number of operating hours per year")
+    m.fs.fuel_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e12),
+        doc="Operating cost in $/h")
+
+    def fuel_cost_rule(b):
+        return b.fuel_cost == (
+            b.operating_hours *
+            b.coal_price *
+            (b.coal_heat_duty * 1e6)
+        ) / (365 * 24)
+    m.fs.fuel_cost_eq = pyo.Constraint(rule=fuel_cost_rule)
+
+    ###########################################################################
+    #  Add capital and operating cost for full plant
+    ###########################################################################
+
+    # Calculate capital cost for power plant
+    # m.fs.plant_capital_cost = pyo.Var(
+    #     initialize=1000000,
+    #     bounds=(0, 1e12),
+    #     doc="Plant capital cost in $/hour")
+    m.fs.plant_fixed_operating_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e12),
+        doc="Plant fixed operating cost in $/hour")
+    m.fs.plant_variable_operating_cost = pyo.Var(
+        initialize=1000000,
+        bounds=(0, 1e12),
+        doc="Plant variable operating cost in $/hour")
+
+    # def plant_cap_cost_rule(b):
+    #     return m.fs.plant_capital_cost == (
+    #         ((2688973 * m.fs.plant_power_out[0]
+    #           + 618968072) /
+    #          (m.fs.num_of_years * 365 * 24)
+    #         ) * (m.CE_index / 575.4)
+    #     )
+    # m.fs.plant_cap_cost_eq = pyo.Constraint(rule=plant_cap_cost_rule)
+
+    def op_fixed_plant_cost_rule(b):
+        return b.plant_fixed_operating_cost == (
+            ((16657.5 * b.plant_power_out[0]
+              + 6109833.3) /
+             (b.num_of_years * 365 * 24)
+            ) * (m.CE_index / 575.4)
+        )
+    m.fs.op_fixed_plant_cost_eq = pyo.Constraint(rule=op_fixed_plant_cost_rule)
+
+    def op_variable_plant_cost_rule(b):
+        return b.plant_variable_operating_cost == (
+            (31754.7 * b.plant_power_out[0]
+            ) * (m.CE_index / 575.4)
+        ) / (365 * 24)
+    m.fs.op_variable_plant_cost_eq = pyo.Constraint(
+        rule=op_variable_plant_cost_rule)
+
+    return m
+
+
+def initialize_with_costing(m):
+
+    optarg = {
+        "tol": 1e-8,
+        "max_iter": 300,
+        "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver('ipopt', optarg)
+
+    # Fix operating cost variable to initialize cost in a square
+    # problem
+    # m.fs.fuel_cost.fix(1e6)
+
+    # Initialize capital costs for charge and discharge heat
+    # exchangers
+    calculate_variable_from_constraint(
+        m.fs.charge_mode_disjunct.capital_cost,
+        m.fs.charge_mode_disjunct.cap_cost_eq)
+    calculate_variable_from_constraint(
+        m.fs.discharge_mode_disjunct.capital_cost,
+        m.fs.discharge_mode_disjunct.cap_cost_eq)
+
+    # Initialize operating cost
+    calculate_variable_from_constraint(
+        m.fs.fuel_cost,
+        m.fs.fuel_cost_eq)
+
+    # # Initialize capital cost of power plant
+    # calculate_variable_from_constraint(
+    #     m.fs.plant_capital_cost,
+    #     m.fs.plant_cap_cost_eq)
+
+    # Initialize plant fixed and variable operating costs
+    calculate_variable_from_constraint(
+        m.fs.plant_fixed_operating_cost,
+        m.fs.op_fixed_plant_cost_eq)
+    calculate_variable_from_constraint(
+        m.fs.plant_variable_operating_cost,
+        m.fs.op_variable_plant_cost_eq)
+
+    print()
+    print('>> Start initialization of costing correlations')
+
+    # Check and raise an error if the degrees of freedom are not 0
+    # print('   {} DOFs before cost initialization'.format(degrees_of_freedom(m)))
+    if not degrees_of_freedom(m) == 0:
+        raise ConfigurationError(
+            "The degrees of freedom after building the model are not 0. "
+            "You have {} degrees of freedom. "
+            "Please check your inputs to ensure a square problem "
+            "before initializing the model.".format(degrees_of_freedom(m))
+            )
+
+    res = solver.solve(m,
+                       tee=False,
+                       symbolic_solver_labels=True,
+                       options=optarg)
+    print("   **Solver termination in cost initialization: ",
+          res.solver.termination_condition)
+    print('>> End initialization of costing correlations')
+    print()
+
+
+def calculate_bounds(m):
+    m.fs.temperature_degrees = 5
+
+    # Calculate bounds for solar salt from properties expressions
+    m.fs.solar_salt_temperature_max = 853.15 + m.fs.temperature_degrees # in K
+    m.fs.solar_salt_temperature_min = 513.15 - m.fs.temperature_degrees # in K
+    # Note: min/max interchanged because at max temperature we obtain the min value
+    m.fs.solar_salt_enth_mass_max = (
+        (m.fs.solar_salt_properties.cp_param_1.value *
+         (m.fs.solar_salt_temperature_max - 273.15))
+        + (m.fs.solar_salt_properties.cp_param_2.value * 0.5 * \
+           (m.fs.solar_salt_temperature_max - 273.15)**2)
+    )
+    m.fs.solar_salt_enth_mass_min = (
+        (m.fs.solar_salt_properties.cp_param_1.value *
+         (m.fs.solar_salt_temperature_min - 273.15))
+        + (m.fs.solar_salt_properties.cp_param_2.value * 0.5 * \
+           (m.fs.solar_salt_temperature_min - 273.15)**2)
+    )
+
+    m.fs.salt_enth_mass_max = m.fs.solar_salt_enth_mass_max
+    m.fs.salt_enth_mass_min = m.fs.solar_salt_enth_mass_min
+
+    # print('   **Calculate bounds for solar salt')
+    # print('     Mass enthalpy max: {: >4.4f}, min: {: >4.4f}'.format(
+    #     m.fs.solar_salt_enth_mass_max, m.fs.solar_salt_enth_mass_min))
+
+
+def add_bounds(m):
+    """Add bounds to units in charge model
+
+    """
+
+    calculate_bounds(m)
+
+    # Unless stated otherwise, the temperature is in K, pressure in
+    # Pa, flow in mol/s, massic flow in kg/s, and heat and heat duty
+    # in W
+    m.flow_max = m.main_flow * 3       # Units in mol/s
+    m.flow_min = 11804                   # Units in mol/s
+    m.heat_duty_max = (m.max_storage_heat_duty * 1e6)  # Units in MW
+    m.factor = 2.5
+    m.flow_max_storage = 0.2 * m.flow_max
+    m.flow_min_storage = 1e-3
+
+    # Turbines
+    for k in m.set_turbine:
+        m.fs.turbine[k].work.setlb(-1e10)
+        m.fs.turbine[k].work.setub(0)
+
+    # Booster
+    for unit_k in [m.fs.booster]:
+        unit_k.inlet.flow_mol[:].setlb(m.flow_min_storage)
+        unit_k.inlet.flow_mol[:].setub(m.flow_max)
+        unit_k.outlet.flow_mol[:].setlb(m.flow_min_storage)
+        unit_k.outlet.flow_mol[:].setub(m.flow_max)
+
+    # Turbine splitters flow
+    for k in m.set_turbine_splitter:
+        m.fs.turbine_splitter[k].inlet.flow_mol[:].setlb(m.flow_min_storage)
+        m.fs.turbine_splitter[k].inlet.flow_mol[:].setub(m.flow_max)
+        m.fs.turbine_splitter[k].outlet_1.flow_mol[:].setlb(m.flow_min_storage)
+        m.fs.turbine_splitter[k].outlet_1.flow_mol[:].setub(m.flow_max)
+        m.fs.turbine_splitter[k].outlet_2.flow_mol[:].setlb(m.flow_min_storage)
+        m.fs.turbine_splitter[k].outlet_2.flow_mol[:].setub(m.flow_max)
+
+    # Add bounds to all units in charge mode
+    for unit_in_charge in [m.fs.charge_mode_disjunct]:
+        # Charge heat exchanger (HXC)
+        unit_in_charge.hxc.shell_inlet.flow_mol.setlb(m.flow_min_storage)
+        unit_in_charge.hxc.shell_inlet.flow_mol.setub(m.flow_max_storage)
+        unit_in_charge.hxc.tube_inlet.flow_mass.setlb(m.flow_min_storage)
+        unit_in_charge.hxc.tube_inlet.flow_mass.setub(m.max_salt_flow)
+        unit_in_charge.hxc.shell_outlet.flow_mol.setlb(m.flow_min_storage)
+        unit_in_charge.hxc.shell_outlet.flow_mol.setub(m.flow_max_storage)
+        unit_in_charge.hxc.tube_outlet.flow_mass.setlb(m.flow_min_storage)
+        unit_in_charge.hxc.tube_outlet.flow_mass.setub(m.max_salt_flow)
+        unit_in_charge.hxc.tube_inlet.pressure.setlb(101320)
+        unit_in_charge.hxc.tube_inlet.pressure.setub(101330)
+        unit_in_charge.hxc.tube_outlet.pressure.setlb(101320)
+        unit_in_charge.hxc.tube_outlet.pressure.setub(101330)
+        unit_in_charge.hxc.heat_duty.setlb(0)
+        unit_in_charge.hxc.heat_duty.setub(m.heat_duty_max)
+        unit_in_charge.hxc.shell.heat.setlb(-m.heat_duty_max)
+        unit_in_charge.hxc.shell.heat.setub(0)
+        unit_in_charge.hxc.tube.heat.setlb(0)
+        unit_in_charge.hxc.tube.heat.setub(m.heat_duty_max)
+        unit_in_charge.hxc.tube.properties_in[:].enth_mass.setlb(
+            m.fs.salt_enth_mass_min / m.factor)
+        unit_in_charge.hxc.tube.properties_in[:].enth_mass.setub(
+            m.fs.salt_enth_mass_max * m.factor)
+        unit_in_charge.hxc.tube.properties_out[:].enth_mass.setlb(
+            m.fs.salt_enth_mass_min / m.factor)
+        unit_in_charge.hxc.tube.properties_out[:].enth_mass.setub(
+            m.fs.salt_enth_mass_max * m.factor)
+        # unit_in_charge.hxc.tube.properties_in[:].enth_mass.setlb(1)
+        # unit_in_charge.hxc.tube.properties_in[:].enth_mass.setub(1.5e6)
+        # unit_in_charge.hxc.tube.properties_out[:].enth_mass.setlb(1)
+        # unit_in_charge.hxc.tube.properties_out[:].enth_mass.setub(1.5e6)
+        unit_in_charge.hxc.overall_heat_transfer_coefficient.setlb(1)
+        unit_in_charge.hxc.overall_heat_transfer_coefficient.setub(10000)
+        unit_in_charge.hxc.area.setlb(m.min_area)
+        unit_in_charge.hxc.area.setub(m.max_area)
+        unit_in_charge.hxc.delta_temperature_in.setlb(9)
+        unit_in_charge.hxc.delta_temperature_out.setlb(5)
+        unit_in_charge.hxc.delta_temperature_in.setub(120)
+        unit_in_charge.hxc.delta_temperature_out.setub(100)
+        unit_in_charge.hxc.costing.pressure_factor.setlb(0)
+        unit_in_charge.hxc.costing.pressure_factor.setub(1e6)
+        unit_in_charge.hxc.costing.capital_cost.setlb(0)
+        unit_in_charge.hxc.costing.capital_cost.setub(1e8)
+        unit_in_charge.hxc.costing.base_cost_per_unit.setlb(0)
+        unit_in_charge.hxc.costing.base_cost_per_unit.setub(1e8)
+        unit_in_charge.hxc.costing.material_factor.setlb(0)
+        unit_in_charge.hxc.costing.material_factor.setub(100)
+
+        # HX pump
+        for unit_k in [unit_in_charge.hx_pump]:
+            unit_k.inlet.flow_mol.setlb(0)
+            unit_k.inlet.flow_mol.setub(m.flow_max_storage)
+            unit_k.outlet.flow_mol.setlb(0)
+            unit_k.outlet.flow_mol.setub(m.flow_max_storage)
+            unit_k.deltaP.setlb(0)
+            unit_k.deltaP.setub(1e10)
+        unit_in_charge.hx_pump.work_mechanical[0].setlb(0)
+        unit_in_charge.hx_pump.work_mechanical[0].setub(1e10)
+        unit_in_charge.hx_pump.ratioP.setlb(0)
+        unit_in_charge.hx_pump.ratioP.setub(100)
+        unit_in_charge.hx_pump.work_fluid[0].setlb(0)
+        unit_in_charge.hx_pump.work_fluid[0].setub(1e8)
+        unit_in_charge.hx_pump.efficiency_pump[0].setlb(0)
+        unit_in_charge.hx_pump.efficiency_pump[0].setub(1)
+
+        # HP splitter
+        unit_in_charge.ess_charge_split.inlet.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_charge.ess_charge_split.inlet.flow_mol[:].setub(m.flow_max)
+        unit_in_charge.ess_charge_split.to_hxc.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_charge.ess_charge_split.to_hxc.flow_mol[:].setub(m.flow_max_storage)
+        unit_in_charge.ess_charge_split.to_turbine.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_charge.ess_charge_split.to_turbine.flow_mol[:].setub(m.flow_max)
+        unit_in_charge.ess_charge_split.split_fraction[0.0, "to_hxc"].setlb(0)
+        unit_in_charge.ess_charge_split.split_fraction[0.0, "to_hxc"].setub(1)
+        unit_in_charge.ess_charge_split.split_fraction[0.0, "to_turbine"].setlb(0)
+        unit_in_charge.ess_charge_split.split_fraction[0.0, "to_turbine"].setub(1)
+
+        # Mixer 2
+        unit_in_charge.mixer2.from_fwh9.flow_mol.setlb(m.flow_min_storage)
+        unit_in_charge.mixer2.from_fwh9.flow_mol.setub(m.flow_max)
+        unit_in_charge.mixer2.from_hx_pump.flow_mol.setlb(0)
+        unit_in_charge.mixer2.from_hx_pump.flow_mol.setub(m.flow_max_storage)
+        unit_in_charge.mixer2.outlet.flow_mol.setlb(0)
+        unit_in_charge.mixer2.outlet.flow_mol.setub(m.flow_max)
+
+
+    # Add bounds to all units in discharge mode
+    for unit_in_discharge in [m.fs.discharge_mode_disjunct]:
+        # Discharge heat exchanger (HXD)
+        unit_in_discharge.hxd.shell_inlet.flow_mass.setlb(m.flow_min_storage)
+        unit_in_discharge.hxd.shell_inlet.flow_mass.setub(m.max_salt_flow)
+        unit_in_discharge.hxd.shell_outlet.flow_mass.setlb(m.flow_min_storage)
+        unit_in_discharge.hxd.shell_outlet.flow_mass.setub(m.max_salt_flow)
+        unit_in_discharge.hxd.tube_inlet.flow_mol.setlb(m.flow_min_storage)
+        unit_in_discharge.hxd.tube_inlet.flow_mol.setub(m.flow_max_storage)
+        unit_in_discharge.hxd.tube_outlet.flow_mol.setlb(m.flow_min_storage)
+        unit_in_discharge.hxd.tube_outlet.flow_mol.setub(m.flow_max_storage)
+        unit_in_discharge.hxd.shell_inlet.pressure.setlb(101320)
+        unit_in_discharge.hxd.shell_inlet.pressure.setub(101330)
+        unit_in_discharge.hxd.shell_outlet.pressure.setlb(101320)
+        unit_in_discharge.hxd.shell_outlet.pressure.setub(101330)
+        unit_in_discharge.hxd.heat_duty.setlb(0)
+        unit_in_discharge.hxd.heat_duty.setub(m.heat_duty_max)
+        unit_in_discharge.hxd.tube.heat.setlb(0)
+        unit_in_discharge.hxd.tube.heat.setub(m.heat_duty_max)
+        unit_in_discharge.hxd.shell.heat.setlb(-m.heat_duty_max)
+        unit_in_discharge.hxd.shell.heat.setub(0)
+        unit_in_discharge.hxd.shell.properties_in[:].enth_mass.setlb(
+            m.fs.salt_enth_mass_min / m.factor)
+        unit_in_discharge.hxd.shell.properties_in[:].enth_mass.setub(
+            m.fs.salt_enth_mass_max * m.factor)
+        unit_in_discharge.hxd.shell.properties_out[:].enth_mass.setlb(
+            m.fs.salt_enth_mass_min / m.factor)
+        unit_in_discharge.hxd.shell.properties_out[:].enth_mass.setub(
+            m.fs.salt_enth_mass_max * m.factor)
+        # unit_in_discharge.hxd.shell.properties_in[:].enth_mass.setlb(1)
+        # unit_in_discharge.hxd.shell.properties_in[:].enth_mass.setub(1.5e6)
+        # unit_in_discharge.hxd.shell.properties_out[:].enth_mass.setlb(1)
+        # unit_in_discharge.hxd.shell.properties_out[:].enth_mass.setub(1.5e6)
+        unit_in_discharge.hxd.overall_heat_transfer_coefficient.setlb(1)
+        unit_in_discharge.hxd.overall_heat_transfer_coefficient.setub(10000)
+        unit_in_discharge.hxd.area.setlb(m.min_area)
+        unit_in_discharge.hxd.area.setub(m.max_area)
+        unit_in_discharge.hxd.delta_temperature_in.setlb(5)
+        unit_in_discharge.hxd.delta_temperature_out.setlb(10)
+        unit_in_discharge.hxd.delta_temperature_in.setub(350)
+        unit_in_discharge.hxd.delta_temperature_out.setub(500)
+        unit_in_discharge.hxd.costing.pressure_factor.setlb(0)
+        unit_in_discharge.hxd.costing.pressure_factor.setub(1e5)
+        unit_in_discharge.hxd.costing.capital_cost.setlb(0)
+        unit_in_discharge.hxd.costing.capital_cost.setub(1e8)
+        unit_in_discharge.hxd.costing.base_cost_per_unit.setlb(0)
+        unit_in_discharge.hxd.costing.base_cost_per_unit.setub(1e8)
+        unit_in_discharge.hxd.costing.material_factor.setlb(0)
+        unit_in_discharge.hxd.costing.material_factor.setub(100)
+
+
+        # BFP splitter
+        unit_in_discharge.ess_discharge_split.inlet.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_discharge.ess_discharge_split.inlet.flow_mol[:].setub(m.flow_max)
+        unit_in_discharge.ess_discharge_split.to_hxd.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_discharge.ess_discharge_split.to_hxd.flow_mol[:].setub(m.flow_max_storage)
+        unit_in_discharge.ess_discharge_split.to_fwh1.flow_mol[:].setlb(0)
+        unit_in_discharge.ess_discharge_split.to_fwh1.flow_mol[:].setub(m.flow_max)
+        unit_in_discharge.ess_discharge_split.split_fraction[0.0, "to_hxd"].setlb(0)
+        unit_in_discharge.ess_discharge_split.split_fraction[0.0, "to_hxd"].setub(1)
+        unit_in_discharge.ess_discharge_split.split_fraction[0.0, "to_fwh1"].setlb(0)
+        unit_in_discharge.ess_discharge_split.split_fraction[0.0, "to_fwh1"].setub(1)
+
+        # ES Turbine
+        unit_in_discharge.es_turbine.inlet.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_discharge.es_turbine.inlet.flow_mol[:].setub(m.flow_max_storage)
+        unit_in_discharge.es_turbine.outlet.flow_mol[:].setlb(m.flow_min_storage)
+        unit_in_discharge.es_turbine.outlet.flow_mol[:].setub(m.flow_max_storage)
+        unit_in_discharge.es_turbine.deltaP.setlb(-1e10)
+        unit_in_discharge.es_turbine.deltaP.setub(1e10)
+        unit_in_discharge.es_turbine.work.setlb(-1e12)
+        unit_in_discharge.es_turbine.work.setub(0)
+        unit_in_discharge.es_turbine.efficiency_isentropic.setlb(0)
+        unit_in_discharge.es_turbine.efficiency_isentropic.setub(1)
+        unit_in_discharge.es_turbine.ratioP.setlb(0)
+        unit_in_discharge.es_turbine.ratioP.setub(100)
+        unit_in_discharge.es_turbine.efficiency_mech.setlb(0)
+        unit_in_discharge.es_turbine.efficiency_mech.setub(1)
+        unit_in_discharge.es_turbine.shaft_speed.setlb(0)
+        unit_in_discharge.es_turbine.shaft_speed.setub(1000)
+
+
+def main(method=None,
+         max_power=None,
+         load_init_file=None,
+         path_init_file=None,
+         deact_arcs_after_init=None,
+         energy_loss=None,
+         solver=None):
+
+    if load_init_file:
+        # Build ultra-supercritical plant model and initialize it
+        m = usc.build_plant_model()
+
+        # Create a flowsheet, add properties, unit models, and arcs
+        m = create_gdp_model(m,
+                             method=method,
+                             max_power=max_power,
+                             deact_arcs_after_init=deact_arcs_after_init,
+                             energy_loss=energy_loss)
+
+        # Set required inputs to the model to have a square problem for
+        # initialization
+        set_model_input(m)
+
+        # Add scaling factors
+        set_scaling_factors(m)
+
+        # Add cost correlations
+        m = build_costing(m)
+
+        # Initialize using .json file (with bounds)
+        ms.from_json(m, fname=path_init_file)
+        print()
+        print('>>>>> Initializing model using .json file: {}'.format(path_init_file))
+
+        # Add bounds
+        add_bounds(m)
+
+    else:
+        # Build ultra-supercritical plant model and initialize it
+        m = usc.build_plant_model()
+        print()
+        print('>> Start initialization of ultra-supercritical plant base model')
+        usc.initialize(m)
+        print('>> End initialization of ultra-supercritical plant base model')
+
+        # Create a flowsheet, add properties, unit models, and arcs
+        m = create_gdp_model(m,
+                             method=method,
+                             max_power=max_power,
+                             deact_arcs_after_init=deact_arcs_after_init,
+                             energy_loss=energy_loss)
+
+        # Set required inputs to the model to have a square problem for
+        # initialization
+        set_model_input(m)
+
+        # Add scaling factors
+        set_scaling_factors(m)
+
+        # Initialize the model with a sequential initialization and custom
+        # routines
+        initialize(m, deact_arcs_after_init=deact_arcs_after_init)
+
+        # Add cost correlations
+        m = build_costing(m)
+        # print('DOF after costing: ', degrees_of_freedom(m))
+
+        # Initialize costing
+        initialize_with_costing(m)
+
+        # Add bounds
+        add_bounds(m)
+
+        # Calculate and store initialization file
+        ms.to_json(m, fname=path_init_file)
+        print()
+        print('>>>>> Saving initialization .json file in {}'.format(path_init_file))
+
+
+    # Add disjunctions
+    add_disjunction(m)
+
+    if deact_arcs_after_init:
+        # Deactivate arcs
+        _deactivate_arcs(m)
+
+    return m
+
+
+def print_results(m, results):
+
+    m.fs.condenser_mix.makeup.display()
+
+    print('================================')
+    print()
+    print("***************** Optimization Results ******************")
+    print('Obj ($/h): {:.4f}'.format(value(m.obj) / m.scaling_obj))
+    print('Revenue ($/h): {:.4f}'.format(
+        value(m.fs.revenue)))
+    # print('Plant capital cost ($/h): {:.4f}'.format(
+    #     value(m.fs.plant_capital_cost)))
+    print('Plant fixed operating costs ($/h): {:.4f}'.format(
+        value(m.fs.plant_fixed_operating_cost)))
+    print('Plant variable operating costs ($/h): {:.4f}'.format(
+        value(m.fs.plant_variable_operating_cost)))
+    print('Coal Cost (fuel) ($/h): {:.4f}'.format(
+        value(m.fs.fuel_cost)))
+    print('Storage Capital Cost ($/h): {:.4f}'.format(
+        value(m.fs.storage_capital_cost)))
+    print()
+    print("***************** Tank Results ******************")
+    print('Hot Salt Inventory (mton): {:.4f}, prev: {:.4f}'.format(
+        value(m.fs.salt_inventory_hot),
+        value(m.fs.previous_salt_inventory_hot)))
+    print('Cold Salt Inventory (mton): {:.4f}, prev: {:.4f}'.format(
+        value(m.fs.salt_inventory_cold),
+        value(m.fs.previous_salt_inventory_cold)))
+    print('Salt to storage (mton): {:.4f}'.format(
+        value(m.fs.salt_storage)))
+    print('Salt Amount (mton): {:.4f}'.format(
+        value(m.fs.salt_amount)))
+    print('')
+    print("***************** Power Plant Operation ******************")
+    print('')
+    print('Net Power (MW): {:.4f}'.format(
+        value(m.fs.net_power)))
+    print('Plant Power (MW): {:.4f}'.format(
+        value(m.fs.plant_power_out[0])))
+    print('Discharge turbine power (MW) [ES turbine Power]: {:.4f} [{:.4f}]'.format(
+        value(m.fs.discharge_turbine_work),
+        value(m.fs.discharge_mode_disjunct.es_turbine.work_mechanical[0]) * (-1e-6)))
+    print('HX pump work (MW): {:.4f}'.format(
+        value(m.fs.hx_pump_work)))
+    print('Boiler feed water flow (mol/s): {:.4f}'.format(
+        value(m.fs.boiler.inlet.flow_mol[0])))
+    print('Boiler (plant) heat duty (MW_th): {:.4f}'.format(
+        value(m.fs.plant_heat_duty[0])))
+    print('Makeup water flow: {:.4f}'.format(
+        value(m.fs.condenser_mix.makeup.flow_mol[0])))
+    print()
+    # print('Boiler efficiency (%): boiler: {:.4f}'.format(
+    #     value(m.fs.boiler_efficiency) * 100))
+    print('Boiler/Cycle efficiencies (%): boiler: {:.4f}, cycle: {:.4f}'.format(
+        value(m.fs.boiler_efficiency) * 100,
+        value(m.fs.cycle_efficiency) * 100))
+    print()
+    if m.fs.charge_mode_disjunct.binary_indicator_var.value == 1:
+        print("***************** Charge Heat Exchanger (HXC) ******************")
+        print('HXC area (m2): {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.area)))
+        print('HXC heat duty (MW): {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.heat_duty[0]) * 1e-6))
+        print('HXC salt flow (kg/s): {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.tube_inlet.flow_mass[0])))
+        print('HXC steam flow to storage (mol/s): {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.shell_inlet.flow_mol[0])))
+        print('HXC salt temperature (K): in: {:.4f}, out: {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.tube_inlet.temperature[0]),
+            value(m.fs.charge_mode_disjunct.hxc.tube_outlet.temperature[0])))
+        print('HXC water temperature (K): in: {:.4f}, out: {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.hot_side.properties_in[0].temperature),
+            value(m.fs.charge_mode_disjunct.hxc.hot_side.properties_out[0].temperature)))
+        print('HXC delta temperature (K): in: {:.4f}, out: {:.4f}'.format(
+            value(m.fs.charge_mode_disjunct.hxc.delta_temperature_in[0]),
+            value(m.fs.charge_mode_disjunct.hxc.delta_temperature_out[0])))
+        print('')
+    elif m.fs.discharge_mode_disjunct.binary_indicator_var.value == 1:
+        print("*************** Discharge Heat Exchanger (HXD) ****************")
+        print('')
+        print('HXD area (m2): {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.area)))
+        print('HXD heat duty (MW): {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.heat_duty[0]) * 1e-6))
+        print('HXD salt flow (kg/s): {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.shell_inlet.flow_mass[0])))
+        print('HXD Steam flow to storage (mol/s): {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.tube_inlet.flow_mol[0])))
+        print('HXD salt temperature (K): in: {:.4f}, out: {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.shell_inlet.temperature[0]),
+            value(m.fs.discharge_mode_disjunct.hxd.shell_outlet.temperature[0])))
+        print('HXD water temperature (K): in: {:.4f}, out: {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.cold_side.properties_in[0].temperature),
+            value(m.fs.discharge_mode_disjunct.hxd.cold_side.properties_out[0].temperature)))
+        print('HXD delta temperature (K): in: {:.4f}, out: {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.hxd.delta_temperature_in[0]),
+            value(m.fs.discharge_mode_disjunct.hxd.delta_temperature_out[0])))
+        print('ES Turbine work (MW): {:.4f}'.format(
+            value(m.fs.discharge_mode_disjunct.es_turbine.work[0]) * -1e-6))
+        print('')
+
+    print('')
+    print('Solver details')
+    print(results)
+    print(' ')
+    print('==============================================================')
+
+
+def print_reports(m):
+
+    print('')
+    for unit_k in [m.fs.boiler, m.fs.reheater[1],
+                   m.fs.reheater[2],
+                   m.fs.bfp, m.fs.bfpt,
+                   m.fs.booster,
+                   m.fs.condenser_mix,
+                   m.fs.charge.hxc]:
+        unit_k.display()
+
+    for k in RangeSet(11):
+        m.fs.turbine[k].report()
+    for k in RangeSet(11):
+        m.fs.turbine[k].display()
+    for j in RangeSet(9):
+        m.fs.fwh[j].report()
+    for j in m.set_fwh_mixer:
+        m.fs.fwh_mixer[j].display()
+
+def print_model(_, nlp_model, nlp_data):
+
+    print('       ___________________________________________')
+    if nlp_model.fs.charge_mode_disjunct.indicator_var.value == 1:
+        print('        Disjunction 1: Charge mode is selected')
+        print('         HXC area (m2): {:.4f}'.format(
+            value(nlp_model.fs.charge_mode_disjunct.hxc.area)))
+        print('         HXC heat duty (MW): {:.4f}'.format(
+            value(nlp_model.fs.charge_mode_disjunct.hxc.heat_duty[0]) * 1e-6))
+        print('         HXC salt flow (kg/s): {:.4f}'.format(
+            value(nlp_model.fs.charge_mode_disjunct.hxc.tube_inlet.flow_mass[0])))
+        print('         HXC steam flow (mol/s): {:.4f}'.format(
+            value(nlp_model.fs.charge_mode_disjunct.hxc.shell_inlet.flow_mol[0])))
+        print('         HXC Salt temperature in/out (K): {:.4f}/{:.4f}'.format(
+            value(nlp_model.fs.charge_mode_disjunct.hxc.tube_inlet.temperature[0]),
+            value(nlp_model.fs.charge_mode_disjunct.hxc.tube_outlet.temperature[0])))
+        print('         HXC Delta temperature in/out (K): {:.4f}/{:.4f}'.format(
+            value(nlp_model.fs.charge_mode_disjunct.hxc.delta_temperature_in[0]),
+            value(nlp_model.fs.charge_mode_disjunct.hxc.delta_temperature_out[0])))
+    elif nlp_model.fs.discharge_mode_disjunct.indicator_var.value == 1:
+        print('        Disjunction 1: Discharge mode is selected')
+        print('         HXD area (m2): {:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.area)))
+        print('         HXD heat duty (MW): {:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.heat_duty[0]) * 1e-6))
+        print('         HXD salt flow (kg/s): {:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.shell_inlet.flow_mass[0])))
+        print('         HXD steam flow (mol/s): {:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.tube_inlet.flow_mol[0])))
+        print('         HXD Salt temperature in/out (K): {:.4f}/{:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.shell_inlet.temperature[0]),
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.shell_outlet.temperature[0])))
+        print('         HXD Delta temperature in/out (K): {:.4f}/{:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.delta_temperature_in[0]),
+            value(nlp_model.fs.discharge_mode_disjunct.hxd.delta_temperature_out[0])))
+        print('         ES turbine work (MW): {:.4f}'.format(
+            value(nlp_model.fs.discharge_mode_disjunct.es_turbine.work_mechanical[0]) * (-1e-6)))
+    elif nlp_model.fs.no_storage_mode_disjunct.indicator_var.value == 1:
+        print('        Disjunction 1: No storage mode is selected')
+    else:
+        print('        No other operation alternative!')
+
+    print()
+    print('        Obj (M$/year): {:.4f}'.format(
+        value(nlp_model.obj) / nlp_model.scaling_obj))
+    print('        Revenue ($/h): {:.4f}'.format(
+        value(nlp_model.fs.revenue)))
+    # print('        Plant capital cost ($/h): {:.4f}'.format(
+    #     value(nlp_model.fs.plant_capital_cost)))
+    print('        Plant fixed operating costs ($/h): {:.4f}'.format(
+        value(nlp_model.fs.plant_fixed_operating_cost)))
+    print('        Plant variable operating costs ($/h): {:.4f}'.format(
+        value(nlp_model.fs.plant_variable_operating_cost)))
+    print('        Coal Cost (fuel) ($/h): {:.4f}'.format(
+        value(nlp_model.fs.fuel_cost)))
+    print('        Storage Capital Cost ($/h): {:.4f}'.format(
+        value(nlp_model.fs.storage_capital_cost)))
+    print('        Net Power (MW): {:.4f}'.format(
+        value(nlp_model.fs.net_power)))
+    print('        Plant Power (MW): {:.4f}'.format(
+        value(nlp_model.fs.plant_power_out[0])))
+    print('        Discharge turbine work (MW): {:.4f}'.format(
+        value(nlp_model.fs.discharge_turbine_work)))
+    print('        HX pump work (MW): {:.4f}'.format(
+        value(nlp_model.fs.hx_pump_work)))
+    # print('        Boiler efficiency (%): {:.4f}'.format(
+    #     value(nlp_model.fs.boiler_efficiency) * 100))
+    print('        Boiler/cycle efficiency (%): {:.4f}/{:.4f}'.format(
+        value(nlp_model.fs.boiler_efficiency) * 100,
+        value(nlp_model.fs.cycle_efficiency) * 100))
+    print('        Hot Previous Salt Inventory (mton): {:.4f}'.format(
+        value(nlp_model.fs.previous_salt_inventory_hot)))
+    print('        Cold Previous Salt Inventory (mton): {:.4f}'.format(
+        value(nlp_model.fs.previous_salt_inventory_cold)))
+    print('        Salt to storage (kg/s) [mton]: {:.4f} [{:.4f}]'.format(
+        value(nlp_model.fs.salt_storage),
+        value(nlp_model.fs.salt_storage) * 3600 * nlp_model.factor_mton))
+    print('        Hot Salt Inventory (mton): {:.4f}'.format(
+        value(nlp_model.fs.salt_inventory_hot)))
+    print('        Cold Salt Inventory (mton): {:.4f}'.format(
+        value(nlp_model.fs.salt_inventory_cold)))
+
+
+    print('       ___________________________________________')
+
+    log_close_to_bounds(nlp_model)
+    # log_infeasible_constraints(nlp_model)
+
+
+def run_nlps(m,
+             solver=None,
+             operation_mode=None):
+    """This function fixes the indicator variables of the disjuncts so to
+    solve NLP problems
+
+    """
+
+    print()
+    print('>>> You are solving an NLP problem by fixing the operation disjuncts!')
+    if operation_mode == "charge":
+        print('           ** Solving for charge mode')
+        m.fs.charge_mode_disjunct.indicator_var.fix(True)
+        m.fs.discharge_mode_disjunct.indicator_var.fix(False)
+        m.fs.no_storage_mode_disjunct.indicator_var.fix(False)
+    elif operation_mode == "discharge":
+        print('           ** Solving for discharge mode')
+        m.fs.charge_mode_disjunct.indicator_var.fix(False)
+        m.fs.discharge_mode_disjunct.indicator_var.fix(True)
+        m.fs.no_storage_mode_disjunct.indicator_var.fix(False)
+    elif operation_mode == "no_storage":
+        print('           ** Solving for no storage mode')
+        m.fs.charge_mode_disjunct.indicator_var.fix(False)
+        m.fs.discharge_mode_disjunct.indicator_var.fix(False)
+        m.fs.no_storage_mode_disjunct.indicator_var.fix(True)
+    else:
+        print('<(x.x)> Unrecognized operation mode! Try charge, discharge, or no_storage')
+    print()
+    print()
+
+    TransformationFactory('gdp.fix_disjuncts').apply_to(m)
+    print("The degrees of freedom after gdp transformation ",
+          degrees_of_freedom(m))
+
+    results = solver.solve(
+        m,
+        tee=True,
+        symbolic_solver_labels=True,
+        options={
+            "linear_solver": "ma27",
+            "max_iter": 150
+        }
+    )
+
+    log_close_to_bounds(m)
+    log_infeasible_constraints(m)
+
+    print_results(m, results)
+
+    return m, results
+
+
+def run_gdp(m):
+
+    print('>>> You are solving GDP model using GDPopt')
+    print('    {} DOFs before solving GDP model '.format(degrees_of_freedom(m)))
+
+    opt = SolverFactory('gdpopt')
+    _prop_bnds_root_to_leaf_map[ExternalFunctionExpression] = lambda x, y, z: None
+
+    results = opt.solve(
+        m,
+        tee=True,
+        algorithm='RIC',
+        # OA_penalty_factor=1e4,
+        # max_slack=1e4,
+        call_after_subproblem_solve=print_model,
+        mip_solver='gurobi_direct',
+        nlp_solver='ipopt',
+        init_algorithm="no_init",
+        time_limit="2400",
+        subproblem_presolve=False,
+        nlp_solver_args=dict(
+            tee=True,
+            symbolic_solver_labels=True,
+            options={
+                "linear_solver": "ma27",
+                "max_iter": 150
+            }
+        )
+    )
+
+    print_results(m, results)
+    # print_reports(m)
+
+    return results
+
+
+def model_analysis(m,
+                   solver=None,
+                   power=None,
+                   max_power=None,
+                   tank_scenario=None,
+                   fix_power=None,
+                   operation_mode=None,
+                   method=None,
+                   deact_arcs_after_init=None):
+    """Unfix variables for analysis. This section is deactived for the
+    simulation of square model
+    """
+
+    if fix_power:
+        m.fs.power_demand_eq = pyo.Constraint(
+            expr=m.fs.net_power == power
+        )
+    else:
+        m.fs.plant_power_min = pyo.Constraint(
+            expr=m.fs.plant_power_out[0] >= m.min_power
+        )
+        m.fs.plant_power_max = pyo.Constraint(
+            expr=m.fs.plant_power_out[0] <= max_power
+        )
+        hxc_heat_duty = (1e-6) * (pyunits.MW / pyunits.W) * m.fs.charge_mode_disjunct.hxc.heat_duty[0]
+        hxd_heat_duty = (1e-6) * (pyunits.MW / pyunits.W) * m.fs.discharge_mode_disjunct.hxd.heat_duty[0]
+        m.fs.charge_mode_disjunct.storage_min_heat_duty = pyo.Constraint(
+            expr=hxc_heat_duty >= m.min_storage_heat_duty
+        )
+        m.fs.discharge_mode_disjunct.storage_min_heat_duty = pyo.Constraint(
+            expr=hxd_heat_duty >= m.min_storage_heat_duty
+        )
+        # m.fs.charge_mode_disjunct.storage_max_heat_duty = pyo.Constraint(
+        #     expr=hxc_heat_duty <= m.max_storage_heat_duty
+        # )
+        # m.fs.discharge_mode_disjunct.storage_max_heat_duty = pyo.Constraint(
+        #     expr=hxd_heat_duty <= m.max_storage_heat_duty * (1 - 0.01)
+        # )
+
+    # Fix and unfix boiler data
+    m.fs.boiler.outlet.pressure.fix(m.main_steam_pressure)
+    m.fs.boiler.inlet.flow_mol.unfix()
+
+    # Unfix data fixed during initialization
+    # m.fs.fuel_cost.unfix()
+    m.fs.charge_mode_disjunct.ess_charge_split.split_fraction[0, "to_hxc"].unfix()
+    m.fs.discharge_mode_disjunct.ess_discharge_split.split_fraction[0, "to_hxd"].unfix()
+
+    if not deact_arcs_after_init:
+        m.fs.turbine[3].inlet.unfix()
+        m.fs.fwh[8].tube_inlet.unfix()
+
+    for salt_hxc in [m.fs.charge_mode_disjunct.hxc]:
+        salt_hxc.shell_inlet.unfix()
+        salt_hxc.tube_inlet.flow_mass.unfix()
+        salt_hxc.area.unfix()
+
+    for salt_hxd in [m.fs.discharge_mode_disjunct.hxd]:
+        salt_hxd.tube_inlet.unfix()
+        salt_hxd.shell_inlet.flow_mass.unfix()
+        salt_hxd.area.unfix()
+
+    # Unfix global variables
+    m.fs.hx_pump_work.unfix()
+    m.fs.discharge_turbine_work.unfix()
+
+    # Fix storage heat exchangers design
+    m.fs.charge_mode_disjunct.hxc.tube_outlet.temperature[0].fix(m.hot_salt_temp)
+    m.fs.discharge_mode_disjunct.hxd.shell_inlet.temperature[0].fix(m.hot_salt_temp)
+    m.fs.discharge_mode_disjunct.hxd.shell_outlet.temperature[0].fix(m.cold_salt_temp)
+
+    # Add salt inventory variables
+    min_tank = 1 * m.factor_mton # in mton
+    max_tank = m.max_salt_amount - min_tank # in mton
+    max_inventory = 1e7 * m.factor_mton # in mton
+    min_inventory = 75000 * m.factor_mton # in mton
+
+    # Add variables and mass balances for the hot storage tank
+    m.fs.previous_salt_inventory_hot = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=min_inventory,
+        bounds=(0, max_inventory),
+        doc="Hot salt inventory at the beginning of time period in mton"
+        )
+    m.fs.salt_inventory_hot = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=min_inventory,
+        bounds=(0, max_inventory),
+        doc="Hot salt inventory at the end of time period in mton"
+        )
+    m.fs.previous_salt_inventory_cold = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=max_tank - min_inventory,
+        bounds=(0, max_inventory),
+        doc="Cold salt inventory at the beginning of time period in mton"
+        )
+    m.fs.salt_inventory_cold = pyo.Var(
+        domain=NonNegativeReals,
+        initialize=max_tank - min_inventory,
+        bounds=(0, max_inventory),
+        doc="Cold salt inventory at the end of time period in mton"
+        )
+
+    @m.fs.Constraint(doc="Inventory balance at the end of the time period")
+    def constraint_salt_inventory_hot(b):
+        return b.salt_inventory_hot == (
+            b.previous_salt_inventory_hot
+            + (3600 * m.fs.salt_storage) * m.factor_mton
+        )
+
+    @m.fs.Constraint(doc="Maximum previous salt inventory at any time")
+    def constraint_salt_inventory(b):
+        return b.salt_amount == (
+            b.salt_inventory_hot
+            + b.salt_inventory_cold
+        )
+
+    # Fix the previous salt inventory based on the tank scenario
+    if tank_scenario == "hot_empty":
+        m.fs.previous_salt_inventory_hot.fix(min_tank)
+        m.fs.previous_salt_inventory_cold.fix(max_tank)
+    elif tank_scenario == "hot_half_full":
+        m.fs.previous_salt_inventory_hot.fix(max_tank / 2)
+        m.fs.previous_salt_inventory_cold.fix(max_tank / 2)
+    elif tank_scenario == "hot_full":
+        m.fs.previous_salt_inventory_hot.fix(max_tank)
+        m.fs.previous_salt_inventory_cold.fix(min_tank)
+    else:
+        print('Unrecognized scenario! Try hot_empty, hot_full, or hot_half_full')
+
+    # Add LMP data
+    m.fs.lmp = pyo.Var(
+        m.fs.time,
+        domain=Reals,
+        initialize=80,
+        doc="Hourly LMP in $/MWh"
+        )
+
+    # Fix LMP data according to the case we want to solve. When
+    # solving GDP model, a random value is selected
+    if operation_mode == "charge":
+        m_chg.fs.lmp[0].fix(22.9684)
+    elif operation_mode == "discharge":
+        m_chg.fs.lmp[0].fix(200)
+    elif operation_mode == "no_storage":
+        m_chg.fs.lmp[0].fix(50)
+    else:
+        m_chg.fs.lmp[0].fix(22.9684)
+        print('   **Use fixed LMP signal value of {} $/MWh'.format(
+            value(m.fs.lmp[0])))
+        print()
+
+
+    m.fs.revenue = pyo.Expression(
+        expr=(m.fs.lmp[0] * m.fs.net_power),
+        doc="Revenue function in $/h assuming 1 hr operation"
+    )
+
+    # Set scaling factors to variables including during model analysis
+    set_var_scaling(m)
+
+    # Add a total cost function as the objective function. Also,
+    # include a scaling factor to the objective.
+    m.scaling_obj = 1e-3
+    m.obj = Objective(
+        expr=(
+            m.fs.revenue -
+            (m.fs.fuel_cost +
+             m.fs.plant_fixed_operating_cost +
+             m.fs.plant_variable_operating_cost) -
+            (m.fs.storage_capital_cost
+             # + m.fs.plant_capital_cost
+            )
+        ) * m.scaling_obj,
+        sense=maximize
+    )
+
+    if operation_mode is not None:
+        # Solve NLP problem with fix operation mode disjunct
+        run_nlps(m,
+                 solver=solver,
+                 operation_mode=operation_mode)
+    else:
+        # Solve using GDPopt
+        run_gdp(m)
+
+
+if __name__ == "__main__":
+
+    optarg = {
+        "max_iter": 300,
+        # "halt_on_ampl_error": "yes",
+    }
+    solver = get_solver('ipopt', optarg)
+
+    # How to run this model:
+    #   load_init_file: Set to True if you wish to initialize using a .json file and
+    #                   indicate the path of the .json file in path_init_file
+    #   fix_power:      Select True if you want to fix the power output of the plant.
+    #                   If True, then provide the power value in power_demand
+    #   method:         Select between "with_efficiency" or "without_efficiency"
+    #   tank_scenario:  Select the initial value for the salt tank levels:
+    #                   "hot_empty", "hot_full", "hot_half_full" (hot refers to hot salt)
+    #   operation_mode: Select None if you want to solve the GDP formulation (GDPopt solver)
+    #                   If you wish to solve for one mode, select an operation mode and the
+    #                   respective NLP problem is solved. The modes are: "charge", "discharge",
+    #                   or "no_storage"
+    #  deact_arcs_after_init: Set to True if you wish to deactivate the arcs that are
+    #                         connecting reheater 1 to turbine 3 and bfp to FWH8 after
+    #                         initialization. If False, the arcs are deactivated in
+    #                         create_gdp_model and turbine 3 and FWH8 inlets are fixed during
+    #                         initialization.
+
+    max_power = design_data_dict["plant_max_power"] # in MW
+    power_demand = 400 # in MW
+    load_init_file = False
+    if load_init_file:
+        path_init_file = design_data_dict["gdp_init_file_path"]
+    else:
+        path_init_file = None
+
+    fix_power = False
+    method = "with_efficiency"
+    tank_scenario = "hot_empty"
+    operation_mode = None
+    deact_arcs_after_init = True # when False, cost initialization takes about 20 sec more
+    energy_loss = True
+
+    m_chg = main(method=method,
+                 max_power=max_power,
+                 load_init_file=load_init_file,
+                 path_init_file=path_init_file,
+                 deact_arcs_after_init=deact_arcs_after_init,
+                 energy_loss=energy_loss)
+
+    m = model_analysis(m_chg,
+                       solver,
+                       power=power_demand,
+                       max_power=max_power,
+                       tank_scenario=tank_scenario,
+                       fix_power=fix_power,
+                       operation_mode=operation_mode,
+                       method=method,
+                       deact_arcs_after_init=deact_arcs_after_init)
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/uscp_design_data_new_storage_design.json b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/uscp_design_data_new_storage_design.json
new file mode 100644
index 000000000..464febed2
--- /dev/null
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/gdp_mp/uscp_design_data_new_storage_design.json
@@ -0,0 +1,25 @@
+{
+    "gdp_init_file_path": "initialized_usc_storage_gdp_mp_unfixed_area.json",
+    "plant_max_power": 436,
+    "plant_min_power": 283,
+    "ramp_rate": 60,
+    "hxc_area": 1896,
+    "hxd_area": 1978,
+    "min_discharge_turbine_power": 8,
+    "max_discharge_turbine_power": 34,
+    "min_storage_heat_duty": 10,
+    "max_storage_heat_duty": 250,
+    "max_salt_amount": 6739292,
+    "max_salt_flow": 500,
+    "hot_salt_temperature": 831,
+    "cold_salt_temperature": 513.15,
+    "min_storage_area": 0,
+    "max_storage_area": 6000,
+    "min_storage_area_design": 250,
+    "max_storage_area_design": 3000,
+    "min_solar_salt_temperature": 513.15,
+    "max_solar_salt_temperature": 853.15,
+    "factor_mton": 1e-3,
+    "operating_hours_per_day": 24,
+    "number_of_years": 30
+}
diff --git a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/integrated_storage_with_ultrasupercritical_power_plant.py b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/integrated_storage_with_ultrasupercritical_power_plant.py
index 6a50938b7..363d1f754 100644
--- a/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/integrated_storage_with_ultrasupercritical_power_plant.py
+++ b/dispatches/case_studies/fossil_case/ultra_supercritical_plant/storage/integrated_storage_with_ultrasupercritical_power_plant.py
@@ -47,7 +47,7 @@
 from idaes.core import MaterialBalanceType
 from idaes.core.util.initialization import propagate_state
 from idaes.core.solvers import get_solver
-from idaes.core.util.model_statistics import degrees_of_freedom
+from idaes.core.util.model_statistics import degrees_of_freedom, variables_near_bounds_generator
 import idaes.core.util.scaling as iscale
 import idaes.logger as idaeslog
 
@@ -580,7 +580,7 @@ def set_model_input(m):
     # Add heat exchanger area from supercritical plant
     # model_input. For conceptual design optimization, area is unfixed
     # and optimized
-    m.fs.hxc.area.fix(2500)
+    m.fs.hxc.area.fix(2000)
     m.fs.hxd.area.fix(2000)
 
     # Define storage fluid conditions. The fluid inlet flow is fixed
@@ -601,7 +601,7 @@ def set_model_input(m):
     m.fs.cooler.outlet.enth_mol[0].fix(10000)
     m.fs.cooler.deltaP[0].fix(0)
 
-    # HX pump efficiecncy assumption
+    # HX pump efficiency assumption
     m.fs.hx_pump.efficiency_pump.fix(0.80)
 
     m.fs.es_turbine.ratioP.fix(0.0286)