add coke oven transformation output

rules/build_sector.smk

@@ -301,6 +301,7 @@ rule build_energy_totals:
         eurostat="data/eurostat/Balances-April2023",
         eurostat_households="data/eurostat/eurostat-household_energy_balances-february_2024.csv",
     output:
+        transformation_output_coke=resources("transformation_output_coke.csv"),
         energy_name=resources("energy_totals.csv"),
         co2_name=resources("co2_totals.csv"),
         transport_name=resources("transport_data.csv"),
@@ -666,6 +667,7 @@ rule build_industrial_energy_demand_per_country_today:
         countries=config_provider("countries"),
         industry=config_provider("industry"),
     input:
+        transformation_output_coke=resources("transformation_output_coke.csv"),
         jrc="data/jrc-idees-2021",
         industrial_production_per_country=resources(
             "industrial_production_per_country.csv"

scripts/build_energy_totals.py

@@ -1473,6 +1473,31 @@ def update_residential_from_eurostat(energy: pd.DataFrame) -> pd.DataFrame:
     )
 
 
+def build_transformation_output_coke(eurostat, fn):
+    """
+    Extracts and builds the transformation output data for coke ovens from the
+    Eurostat dataset.
+
+    This function specifically filters the Eurostat data to extract
+    transformation output related to coke ovens.
+    Since the transformation output for coke ovens 
+    is not included in the final energy consumption of the iron and steel sector,
+    it needs to be processed and added separately. The filtered data is saved
+    as a CSV file.
+
+    Parameters:
+    eurostat (pd.DataFrame): A pandas DataFrame containing Eurostat data with
+                             a multi-level index  
+    fn (str): The file path where the resulting CSV file should be saved.
+    
+    Output:
+    The resulting transformation output data for coke ovens is saved as a CSV
+    file at the path specified in fn.
+    """
+    slicer = pd.IndexSlice[:,:,:, "Coke ovens", "Other sources", :]
+    df = eurostat.loc[slicer, :].droplevel(level=[2,3,4,5])
+    df.to_csv(fn)
+    
 # %%
 if __name__ == "__main__":
     if "snakemake" not in globals():
@@ -1498,6 +1523,10 @@ if __name__ == "__main__":
         nprocesses=snakemake.threads,
         disable_progressbar=snakemake.config["run"].get("disable_progressbar", False),
     )
+    
+    build_transformation_output_coke(eurostat,
+                                     snakemake.output.transformation_output_coke)
+    
     swiss = build_swiss()
     idees = build_idees(idees_countries)
 

scripts/build_industrial_energy_demand_per_country_today.py

@@ -227,6 +227,18 @@ def industrial_energy_demand(countries, year):
     return pd.concat(demand_l, keys=countries)
 
 
+def add_coke_ovens(demand, fn, year):
+    df = pd.read_csv(fn, index_col=[0,1]).xs(year, level=1)
+    df = df.rename(columns={'Total all products':'Total'})[fuels.keys()]
+    df = df.rename(columns=fuels).T.groupby(level=0).sum().T
+    df["other"] = df["all"] - df.loc[:,df.columns != "all"].sum(axis=1)
+    df = df.T.reindex_like(demand.xs("Integrated steelworks", axis=1, level=1)).fillna(0)
+    sel = demand.columns.get_level_values(1)=="Integrated steelworks"
+    demand.loc[:,sel] += 0.75 * df.values
+    
+    return demand
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -253,6 +265,10 @@ if __name__ == "__main__":
     # for format compatibility
     demand = demand.stack(future_stack=True).unstack(level=[0, 2])
     
+    # add energy consumption of coke ovens
+    demand = add_coke_ovens(demand, snakemake.input.transformation_output_coke,
+                            year)
+    
     # style and annotation
     demand.index.name = "TWh/a"
     demand.sort_index(axis=1, inplace=True)