013b705ee4
* Cluster first: build renewable profiles and add all assets after clustering * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * correction: pass landfall_lengths through functions * assign landfall_lenghts correctly * remove parameter add_land_use_constraint * fix network_dict * calculate distance to shoreline, remove underwater_fraction * adjust simplification parameter to exclude Crete from offshore wind connections * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove unused geth2015 hydro capacities * removing remaining traces of {simpl} wildcard * add release notes and update workflow graphics * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: lisazeyen <lisa.zeyen@web.de>
100 lines
3.1 KiB
Python
100 lines
3.1 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 energy demand per model region.
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Inputs
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-------
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- ``resources/industrial_distribution_key_base_s_{clusters}.csv``
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- ``resources/industrial_energy_demand_per_country_today.csv``
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Outputs
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-------
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- ``resources/industrial_energy_demand_per_node_today_base_s_{clusters}.csv``
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Description
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-------
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This rule maps the industrial energy demand per country `industrial_energy_demand_per_country_today.csv` to each bus region.
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The energy demand per country is multiplied by the mapping value from the file ``industrial_distribution_key_base_s_{clusters}.csv`` between 0 and 1 to get the industrial energy demand per bus.
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The unit of the energy demand is TWh/a.
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"""
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from itertools import product
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import numpy as np
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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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"Basic chemicals (without ammonia)": "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_energy_demand():
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fn = snakemake.input.industrial_energy_demand_per_country_today
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industrial_demand = pd.read_csv(fn, header=[0, 1], 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_demand = pd.DataFrame(
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0.0, dtype=float, index=keys.index, columns=industrial_demand.index
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)
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countries = keys.country.unique()
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sectors = industrial_demand.columns.unique(1)
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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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demand = industrial_demand[country, sector]
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outer = pd.DataFrame(
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np.outer(key, demand), index=key.index, columns=demand.index
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)
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nodal_demand.loc[buses] += outer
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nodal_demand.index.name = "TWh/a"
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nodal_demand.to_csv(snakemake.output.industrial_energy_demand_per_node_today)
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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_today",
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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_energy_demand()
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