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>
92 lines
2.5 KiB
Python
92 lines
2.5 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_energy_demand_today_base_s_{clusters}.csv``
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- ``resources/industry_sector_ratios_{planning_horizons}.csv``
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- ``resources/industrial_production_base_s_{clusters}_{planning_horizons}.csv``
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Outputs
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-------
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- ``resources/industrial_energy_demand_base_s_{clusters}_{planning_horizons}.csv``
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Description
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-------
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This rule aggregates the energy demand of the industrial sectors per model region.
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For each bus, the following carriers are considered:
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- electricity
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- coal
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- coke
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- solid biomass
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- methane
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- hydrogen
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- low-temperature heat
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- naphtha
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- ammonia
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- process emission
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- process emission from feedstock
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which can later be used as values for the industry load.
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"""
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import pandas as pd
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from _helpers import set_scenario_config
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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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clusters=48,
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planning_horizons=2030,
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)
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set_scenario_config(snakemake)
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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, header=[0, 1], 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(
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{node: sector_ratios[node[:2]] for node in nodal_production.index}, axis=1
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)
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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 = (
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(nodal_sector_ratios.multiply(nodal_production_stacked))
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.T.groupby(level=0)
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.sum()
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)
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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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