e14bae345b
For each country we gradually switch industry processes from today's specific energy carrier usage per ton material output to the best-in-class energy consumption of tomorrow in the industry_sector_ratios.csv. This is done on a per-country basis. The ratio of today to tomorrow's energy consumption is set with the config["industry"]["sector_ratios_fraction_future"] parameter.
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build specific energy consumption by carrier and industries and by country,
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that interpolates between the current average energy consumption (from 2015-2020)
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and the ideal future best-in-class consumption.
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"""
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import pandas as pd
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from prepare_sector_network import get
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def build_industry_sector_ratios_intermediate():
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# in TWh/a
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demand = pd.read_csv(snakemake.input.industrial_energy_demand_per_country_today,
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header=[0,1],
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index_col=0)
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# in Mt/a
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production = pd.read_csv(snakemake.input.industrial_production_per_country,
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index_col=0) / 1e3
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production = production.unstack().swaplevel()
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# in MWh/t
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future_sector_ratios = pd.read_csv(snakemake.input.industry_sector_ratios,
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index_col=0)
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production.index.names = [None,None]
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today_sector_ratios = demand.div(production, axis=1)
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today_sector_ratios.drop(columns=today_sector_ratios.columns[today_sector_ratios.isna().all()],
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inplace=True)
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rename = pd.Series(today_sector_ratios.index,
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today_sector_ratios.index)
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rename["waste"] = "biomass"
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rename["electricity"] = "elec"
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rename["solid"] = "coke"
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rename["gas"] = "methane"
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rename["other"] = "biomass"
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rename["liquid"] = "naphtha"
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today_sector_ratios.rename(rename,
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inplace=True)
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fraction_future = get(params["sector_ratios_fraction_future"], year)
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intermediate_sector_ratios = {}
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for ct in today_sector_ratios.columns.unique(level=0):
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intermediate_sector_ratio = future_sector_ratios.copy()
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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]
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+ (1 - fraction_future)*today_sector_ratios[ct])
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intermediate_sector_ratios[ct] = intermediate_sector_ratio
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intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)
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intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("build_industry_sector_ratios_intermediate")
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year = int(snakemake.wildcards.planning_horizons[-4:])
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params = snakemake.params.industry
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build_industry_sector_ratios_intermediate()
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