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.
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 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 industrial energy demand per model region.
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"""
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import pandas as pd
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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(
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"build_industrial_energy_demand_per_node",
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simpl="",
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clusters=48,
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planning_horizons=2030,
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)
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# import ratios
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fn = snakemake.input.industry_sector_ratios
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sector_ratios = pd.read_csv(fn,
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header=[0,1],
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index_col=0)
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# material demand per node and industry (Mton/a)
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fn = snakemake.input.industrial_production_per_node
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nodal_production = pd.read_csv(fn, index_col=0) / 1e3
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# energy demand today to get current electricity
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fn = snakemake.input.industrial_energy_demand_per_node_today
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nodal_today = pd.read_csv(fn, index_col=0)
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nodal_sector_ratios = pd.concat({node: sector_ratios[node[:2]] for node in nodal_production.index},
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axis=1)
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nodal_production_stacked = nodal_production.stack()
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nodal_production_stacked.index.names = [None,None]
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# final energy consumption per node and industry (TWh/a)
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nodal_df = (nodal_sector_ratios.multiply(nodal_production_stacked)).T.groupby(level=0).sum()
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rename_sectors = {
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"elec": "electricity",
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"biomass": "solid biomass",
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"heat": "low-temperature heat",
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}
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nodal_df.rename(columns=rename_sectors, inplace=True)
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nodal_df["current electricity"] = nodal_today["electricity"]
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nodal_df.index.name = "TWh/a (MtCO2/a)"
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fn = snakemake.output.industrial_energy_demand_per_node
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nodal_df.to_csv(fn, float_format="%.2f")
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