3a1b57515a
* industry distribution: split EAF + integrated using GEM GSPT, add ammonia plants * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add release note [no ci] * add cement plant data for countries not in Hotmaps * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add refineries data for countries not in Hotmaps --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
98 lines
3.2 KiB
Python
98 lines
3.2 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 industrial production per model region.
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Inputs
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-------
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- ``resources/industrial_distribution_key_elec_s{simpl}_{clusters}.csv``
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- ``resources/industrial_production_per_country_tomorrow_{planning_horizons}.csv``
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Outputs
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-------
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- ``resources/industrial_production_per_node_elec_s{simpl}_{clusters}_{planning_horizons}.csv``
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Description
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-------
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This rule maps the industrial production per country from a certain time horizon to each bus region.
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The mapping file provides a value between 0 and 1 for each bus and industry subcategory, indicating the share of the country's production of that sector in that bus.
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The industrial production per country is multiplied by the mapping value to get the industrial production per bus.
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The unit of the production is kt/a.
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"""
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from itertools import product
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import pandas as pd
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from _helpers import set_scenario_config
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# map JRC/our sectors to hotmaps sector, where mapping exist
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sector_mapping = {
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"Electric arc": "EAF",
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"Integrated steelworks": "Integrated steelworks",
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"DRI + Electric arc": "DRI + EAF",
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"Ammonia": "Ammonia",
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"HVC": "Chemical industry",
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"HVC (mechanical recycling)": "Chemical industry",
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"HVC (chemical recycling)": "Chemical industry",
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"Methanol": "Chemical industry",
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"Chlorine": "Chemical industry",
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"Other chemicals": "Chemical industry",
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"Pharmaceutical products etc.": "Chemical industry",
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"Cement": "Cement",
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"Ceramics & other NMM": "Non-metallic mineral products",
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"Glass production": "Glass",
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"Pulp production": "Paper and printing",
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"Paper production": "Paper and printing",
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"Printing and media reproduction": "Paper and printing",
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"Alumina production": "Non-ferrous metals",
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"Aluminium - primary production": "Non-ferrous metals",
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"Aluminium - secondary production": "Non-ferrous metals",
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"Other non-ferrous metals": "Non-ferrous metals",
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}
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def build_nodal_industrial_production():
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fn = snakemake.input.industrial_production_per_country_tomorrow
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industrial_production = pd.read_csv(fn, index_col=0)
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fn = snakemake.input.industrial_distribution_key
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keys = pd.read_csv(fn, index_col=0)
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keys["country"] = keys.index.str[:2]
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nodal_production = pd.DataFrame(
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index=keys.index, columns=industrial_production.columns, dtype=float
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)
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countries = keys.country.unique()
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sectors = industrial_production.columns
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for country, sector in product(countries, sectors):
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buses = keys.index[keys.country == country]
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mapping = sector_mapping.get(sector, "population")
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key = keys.loc[buses, mapping]
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nodal_production.loc[buses, sector] = (
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industrial_production.at[country, sector] * key
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)
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nodal_production.to_csv(snakemake.output.industrial_production_per_node)
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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_production_per_node",
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simpl="",
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clusters=48,
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)
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set_scenario_config(snakemake)
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build_nodal_industrial_production()
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