pypsa-eur/scripts/build_industry_sector_ratios_intermediate.py

# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Build specific energy consumption by carrier and industries and by country,
that interpolates between the current average energy consumption (from
2015-2020) and the ideal future best-in-class consumption.
"""

import pandas as pd
from prepare_sector_network import get


def build_industry_sector_ratios_intermediate():

    # in TWh/a
    demand = pd.read_csv(
        snakemake.input.industrial_energy_demand_per_country_today,
        header=[0, 1],
        index_col=0,
    )

    # in Mt/a
    production = (
        pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
        / 1e3
    )
    production = production.unstack().swaplevel()

    # in MWh/t
    future_sector_ratios = pd.read_csv(
        snakemake.input.industry_sector_ratios, index_col=0
    )

    production.index.names = [None, None]

    today_sector_ratios = demand.div(production, axis=1)

    today_sector_ratios.drop(
        columns=today_sector_ratios.columns[today_sector_ratios.isna().all()],
        inplace=True,
    )

    rename = pd.Series(today_sector_ratios.index, today_sector_ratios.index)
    rename["waste"] = "biomass"
    rename["electricity"] = "elec"
    rename["solid"] = "coke"
    rename["gas"] = "methane"
    rename["other"] = "biomass"
    rename["liquid"] = "naphtha"

    today_sector_ratios.rename(rename, inplace=True)

    fraction_future = get(params["sector_ratios_fraction_future"], year)

    intermediate_sector_ratios = {}

    for ct in today_sector_ratios.columns.unique(level=0):

        intermediate_sector_ratio = future_sector_ratios.copy()

        intermediate_sector_ratio.loc[
            today_sector_ratios[ct].index, today_sector_ratios[ct].columns
        ] = (
            fraction_future
            * intermediate_sector_ratio.loc[
                today_sector_ratios[ct].index, today_sector_ratios[ct].columns
            ]
            + (1 - fraction_future) * today_sector_ratios[ct]
        )
        intermediate_sector_ratios[ct] = intermediate_sector_ratio

    intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)

    intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake("build_industry_sector_ratios_intermediate")

    year = int(snakemake.wildcards.planning_horizons[-4:])

    params = snakemake.params.industry

    build_industry_sector_ratios_intermediate()