Implements Backstop Technologies

workflow/scripts/osemosys_global/check_backstop.py

@@ -0,0 +1,27 @@
+"""Prints warning if backstop techs are used"""
+
+import pandas as pd
+import sys
+from pathlib import Path
+
+if __name__ == "__main__":
+
+    if len(sys.argv) != 2:
+        raise ValueError("Usage: python create_checkbackstop.py <sceanrio_name>")
+    else:
+        scenario = sys.argv[1]
+
+    new_capacity_csv = Path("results", scenario, "results", "NewCapacity.csv")
+    try:
+        new_capacity = pd.read_csv(new_capacity_csv)
+    except FileNotFoundError:
+        sys.exit() 
+
+    backstop = new_capacity[new_capacity.TECHNOLOGY.str.startswith("PWRBCK")]
+
+    if not backstop.empty:
+        techs = backstop.TECHNOLOGY.unique().tolist()
+        print("\n*****\n")
+        print("The following Backstop Technologies are being used:\n")
+        print(techs)
+        print("\n*****\n")

workflow/scripts/osemosys_global/powerplant/backstop.py

@@ -0,0 +1,66 @@
+"""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
+    - CapacityToActivity
+    """
+
+    df = tech_set.copy()
+
+    # technologies
+    techs = df[df.VALUE.str.startswith("PWR")].copy()  # pwrtrn not added yet
+    techs["VALUE"] = "PWRBCK" + 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("PWRBCK", "ELC")
+    oar["FUEL"] = oar.FUEL + "01"
+    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"]]
+
+    # capacity to activity
+    capact = pd.DataFrame(index=pd.Index(bck_techs, name="TECHNOLOGY")).reset_index()
+    capact["REGION"] = region
+    capact["VALUE"] = 31.536
+    capact = capact[["REGION", "TECHNOLOGY", "VALUE"]]
+
+    return techs, oar, capex, opex, capact

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,
@@ -139,22 +141,15 @@ def main(
                                                                       region_name)
     # Calculate residual capacity.
     df_res_cap = res_capacity(gen_table, DUPLICATE_TECHS, start_year, end_year, region_name)
-
-   # if custom_nodes:
-
+
     # Adds residual capacity for custom entries.
     df_res_cap, custom_techs = add_custom_res_cap(df_res_cap, custom_res_cap, 
                                                   tech_list, start_year, 
                                                   end_year, region_name)      
 
     # Creates sets for TECHNOLOGIES including custom entries.
     tech_set = create_sets('TECHNOLOGY', df_oar_final, powerplant_data_dir, custom_techs)
-  #  else:
-  #      custom_techs = []
-
-        # Creates sets for TECHNOLOGIES absent custom entries.
-  #      tech_set = create_sets('TECHNOLOGY', df_oar_final, powerplant_data_dir, [])
-
+
     # Creates sets for FUEL.
     fuel_set = create_sets('FUEL', df_oar_final, powerplant_data_dir, [])       
 
@@ -237,6 +232,14 @@ def main(
                                                                        df_res_cap, 
                                                                        region_name)
 
+    # add backstop technologies 
+    bck_techs, bck_oar, bck_capex, bck_opex, bck_capact = 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])
+    df_capact_final = pd.concat([df_capact_final, bck_capact])
+
     # OUTPUT CSV's USED AS INPUT FOR TRANSMISSION RULE
 
     df_res_cap.to_csv(os.path.join(powerplant_data_dir, "ResidualCapacity.csv"), index=None)
@@ -312,8 +315,6 @@ def main(
         input_data_dir = snakemake.params.input_data_dir
         powerplant_data_dir = snakemake.params.powerplant_data_dir  
         file_specified_annual_demand = f'{output_data_dir}/SpecifiedAnnualDemand.csv'  
-
-       # if custom_nodes:
         file_custom_res_cap = snakemake.input.custom_res_cap
         file_custom_res_potentials = snakemake.input.custom_res_potentials
 
@@ -351,13 +352,11 @@ 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'
         file_specified_annual_demand = f'{output_data_dir}/SpecifiedAnnualDemand.csv'
-
-        #if custom_nodes:
         file_custom_res_cap = 'resources/data/custom_nodes/residual_capacity.csv'
         file_custom_res_potentials = 'resources/data/custom_nodes/RE_potentials.csv' 
 
@@ -385,13 +384,9 @@ def main(
 
     availability = import_afs(file_default_af_factors)
     specified_annual_demand = import_specified_annual_demand(file_specified_annual_demand)
-
-    #if custom_nodes:
+
     custom_res_cap = import_custom_res_cap(file_custom_res_cap)
     custom_res_potentials = import_custom_res_potentials(file_custom_res_potentials)
-    #else:
-    #    custom_res_cap = []
-    #    custom_res_potentials = []
 
     input_data = {
         "plexos_prop": plexos_prop,

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("PWRBCK")].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)

workflow/scripts/osemosys_global/reserves/reserve_margin_tag_technology.py

@@ -7,10 +7,10 @@
 def set_reserve_margin_technologies(margins_technologies, 
                                     tech_set, start_year, 
                                     end_year, region_name):
-    
+
     years = get_years(start_year, end_year)
     data = pd.DataFrame()
-    
+
     '''Check if transmission is added to the config to contribute to
     reserves and develop outputs.'''
     for tech, val in margins_technologies.items():
@@ -20,21 +20,45 @@ def set_reserve_margin_technologies(margins_technologies,
                 for x in tech_set["VALUE"].unique()
                 if x.startswith("TRN")
                 ]
-    
+
         else:
             rm_techs = [
                 x
                 for x in tech_set["VALUE"].unique()
                 if x.startswith("PWR")
                 if x[3:6] == tech
             ]
-        
+
         tech_data = pd.DataFrame(
             list(itertools.product([region_name], rm_techs, years)),
             columns=["REGION", "TECHNOLOGY", "YEAR"],
         )
-        
+
         tech_data['VALUE'] = val / 100
         data = pd.concat([data, tech_data])
 
-    return data
+    backstop = get_backstop_rm(tech_set, region_name, start_year, end_year)
+
+    return pd.concat([data, backstop])
+
+
+def get_backstop_rm(
+    tech_set: pd.DataFrame, region: str, start_year: int, end_year: int
+) -> pd.DataFrame:
+    """Backstop does not contribute to reserve margin"""
+
+    bck_techs = tech_set[tech_set.VALUE.str.startswith("PWRBCK")]
+
+    if bck_techs.empty:
+        return pd.DataFrame(columns=["REGION", "TECHNOLOGY", "YEAR", "VALUE"])
+
+    t = bck_techs.VALUE.unique().tolist()
+    y = get_years(start_year, end_year)
+
+    df = pd.DataFrame(
+        index=pd.MultiIndex.from_product([t, y], names=["TECHNOLOGY", "YEAR"])
+    ).reset_index()
+    df["REGION"] = region
+    df["VALUE"] = 0
+
+    return df[["REGION", "TECHNOLOGY", "YEAR", "VALUE"]]

workflow/scripts/osemosys_global/visualise.py

@@ -199,7 +199,7 @@ def plot_generation_hourly(
     else:
         fig_file = os.path.join(save_dir, 'GenerationHourly.html')
     return fig.write_html(fig_file)
-    
+
 def midpoint(x1, y1, x2, y2):
     return ((x1 + x2)/2, (y1 + y2)/2)
 
@@ -445,4 +445,4 @@ def plot_transmission_flow(
 
         except FileNotFoundError:
             print(f"Usage: python {sys.argv[0]} <input_data.csv> <result_data.csv> <scenario_figs_dir> <countries> <results_by_country> <years> <custom_nodes>")
-            sys.exit(1)
+            sys.exit(1)

workflow/snakefile

@@ -71,6 +71,7 @@ include: "rules/validate.smk"
 # handlers 
 
 onsuccess:
+    shell(f"python workflow/scripts/osemosys_global/check_backstop.py {config['scenario']}")
     print('Workflow finished successfully!')
 
     # Will fix this in next update so that preprocessing steps dont always need to be rerun