backstop templated

workflow/scripts/osemosys_global/powerplant/backstop.py

@@ -0,0 +1,59 @@
+"""Sets backstop parameters"""
+
+import pandas as pd
+
+
+def get_backstop_data(
+    tech_set: pd.DataFrame, year_set: pd.DataFrame, region: str
+) -> tuple[pd.DataFrame]:
+    """Gets the following backstop data
+
+    - Technologies
+    - OAR
+    - Capital Cost
+    - Fixed Cost
+    """
+
+    df = tech_set.copy()
+
+    # technologies
+    techs = df[df.VALUE.str.startswith("PWR")].copy()  # pwrtrn not added yet
+    techs["VALUE"] = "BCK" + df.VALUE.str[-7:-2]
+    techs = techs.drop_duplicates()
+    bck_techs = techs.VALUE.to_list()
+
+    # activity ratios
+    years = year_set.VALUE.to_list()
+    oar = pd.DataFrame(
+        index=pd.MultiIndex.from_product(
+            [bck_techs, years], names=["TECHNOLOGY", "YEAR"]
+        )
+    ).reset_index()
+    oar["REGION"] = region
+    oar["FUEL"] = oar.TECHNOLOGY.str.replace("BCK", "ELEC")
+    oar["FUEL"] = oar.FUEL + "02"
+    oar["MODE_OF_OPERATION"] = 1
+    oar["VALUE"] = 1
+    oar = oar[["REGION", "TECHNOLOGY", "FUEL", "MODE_OF_OPERATION", "YEAR", "VALUE"]]
+
+    # capital cost
+    capex = pd.DataFrame(
+        index=pd.MultiIndex.from_product(
+            [bck_techs, years], names=["TECHNOLOGY", "YEAR"]
+        )
+    ).reset_index()
+    capex["REGION"] = region
+    capex["VALUE"] = 999999
+    capex = capex[["REGION", "TECHNOLOGY", "YEAR", "VALUE"]]
+
+    # fixed costs
+    opex = pd.DataFrame(
+        index=pd.MultiIndex.from_product(
+            [bck_techs, years], names=["TECHNOLOGY", "YEAR"]
+        )
+    ).reset_index()
+    opex["REGION"] = region
+    opex["VALUE"] = 999999
+    opex = opex[["REGION", "TECHNOLOGY", "YEAR", "VALUE"]]
+
+    return techs, oar, capex, opex

workflow/scripts/osemosys_global/powerplant/main.py

@@ -74,6 +74,8 @@
 
 from calibration import apply_calibration
 
+from backstop import get_backstop_data
+
 def main(
     plexos_prop: pd.DataFrame,
     plexos_memb: pd.DataFrame,
@@ -238,6 +240,13 @@ def main(
                                                                        df_res_cap, 
                                                                        region_name)
 
+    # add backstop technologies 
+    bck_techs, bck_oar, bck_capex, bck_opex = get_backstop_data(tech_set, years_set, region_name)
+    tech_set = pd.concat([tech_set, bck_techs])
+    df_oar_final = pd.concat([df_oar_final, bck_oar])
+    df_cap_cost_final = pd.concat([df_cap_cost_final, bck_capex])
+    df_fix_cost_final = pd.concat([df_fix_cost_final, bck_opex])
+
     # OUTPUT CSV's USED AS INPUT FOR TRANSMISSION RULE
 
     df_res_cap.to_csv(os.path.join(powerplant_data_dir, "ResidualCapacity.csv"), index=None)
@@ -352,7 +361,7 @@ def main(
         fossil_capacity_targets = [["BTNXX", 'COA', 2030, 2050, 'ABS', 1],
                                    ["INDNE", 'CCG', 2040, 2050, 'MIN', 10],
                                    ["INDSO", 'OCG', 2025, 2050, 'MAX', 25]]
-        min_generation_factors = {'OCG1': [50, "IND", 2021]}
+        calibration = {'OCG1': [50, "IND", 2021]}
         output_data_dir = 'results/data'
         input_data_dir = 'resources/data'
         powerplant_data_dir = 'results/data/powerplant'

workflow/scripts/osemosys_global/powerplant/variable_costs.py

@@ -341,6 +341,26 @@ def _get_country_nodes_mapper(nodes: list[str]) -> dict[str, list[str]]:
     return df.set_index(["REGION", "TECHNOLOGY", "MODE_OF_OPERATION", "YEAR"])
 
 
+def get_backstop_var_costs(
+    techs: pd.Series, years: pd.Series, regions: pd.Series
+) -> pd.Series:
+    """Gets backstop variable costs"""
+
+    t = techs[techs.str.startswith("BCK")].unique().tolist()
+    y = years.unique().tolist()
+    r = regions.unique().tolist()[0]  # only one region
+
+    df = pd.DataFrame(
+        index=pd.MultiIndex.from_product(
+            [[r], t, y], names=["REGION", "TECHNOLOGY", "YEAR"]
+        )
+    ).reset_index()
+    df["MODE_OF_OPERATION"] = 1
+    df["VALUE"] = 999999
+
+    return df.set_index(["REGION", "TECHNOLOGY", "MODE_OF_OPERATION", "YEAR"])
+
+
 def main(
     cmo_forecasts: pd.DataFrame,
     cmo_data_year: int,
@@ -403,6 +423,11 @@ def main(
     var_costs = pd.concat([mining_data, renewable_data])
     var_costs = var_costs[var_costs.index.get_level_values("YEAR").isin(y)]
     var_costs = filter_var_cost_technologies(var_costs, technologies)
+
+    bck_var_costs = get_backstop_var_costs(technologies, years, regions)
+
+    var_costs = pd.concat([var_costs, bck_var_costs])
+
     return var_costs.round(3)
 
 
@@ -418,10 +443,10 @@ def main(
     else:
         file_cmo_forecasts = "resources/data/CMO-October-2024-Forecasts.xlsx"
         file_fuel_prices = "resources/data/fuel_prices.csv"
-        file_regions = "results/India/data/REGION.csv"
-        file_years = "results/India/data/YEAR.csv"
-        file_technologies = "results/India/data/TECHNOLOGY.csv"
-        file_var_costs = "results/India/data/VariableCosts.csv"
+        file_regions = "results/data/REGION.csv"
+        file_years = "results/data/YEAR.csv"
+        file_technologies = "results/data/powerplant/TECHNOLOGY.csv"
+        file_var_costs = "results/data/powerplant/VariableCosts.csv"
 
     cmo_forecasts = import_cmo_forecasts(file_cmo_forecasts)
     user_fuel_prices = import_fuel_prices(file_fuel_prices)