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lisazeyen 2024-08-09 14:24:06 +02:00
parent c5256c1f37
commit 9b8139abbe
1 changed files with 30 additions and 2 deletions
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32
scripts/build_industrial_energy_demand_per_country_today.py
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@ -227,14 +227,42 @@ def industrial_energy_demand(countries, year):
return pd.concat(demand_l, keys=countries)
def add_coke_ovens(demand, fn, year):
def add_coke_ovens(demand, fn, year, factor=0.75):
"""
Adds the energy consumption of coke ovens to the energy demand for
integrated steelworks.
This function reads the energy consumption data for coke ovens from a
CSV file, processes it to match the structure of the `demand` DataFrame,
and then adds a specified share of this energy consumption to the energy
demand for integrated steelworks.
The `factor` parameter controls what proportion of the coke ovens' energy
consumption should be attributed to the iron and steel production.
The default value of 75% is based on https://doi.org/10.1016/j.erss.2022.102565
Parameters:
demand (pd.DataFrame): A pandas DataFrame containing energy demand data
with a multi-level column index where one of the
levels corresponds to "Integrated steelworks".
fn (str): The file path to the CSV file containing the coke ovens energy
consumption data.
year (int): The year for which the coke ovens data should be selected.
factor (float, optional): The proportion of coke ovens energy consumption to add to the
integrated steelworks demand. Defaults to 0.75.
Returns:
pd.DataFrame: The updated `demand` DataFrame with the coke ovens energy
consumption added to the integrated steelworks energy demand.
"""
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
demand.loc[:,sel] += factor * df.values
return demand