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>
90 lines
2.7 KiB
Python
90 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 hourly heat demand time series from daily heat demand.
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Water and space heating demand profiles are generated using intraday profiles from BDEW. Different profiles are used for the residential and services sectors as well as weekdays and weekend.
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The daily heat demand is multiplied by the intraday profile to obtain the hourly heat demand time series. The rule is executed in ``build_sector.smk``.
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Relevant Settings
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-----------------
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.. code:: yaml
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snapshots:
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drop_leap_day:
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Inputs
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------
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- ``data/heat_load_profile_BDEW.csv``: Intraday heat profile for water and space heating demand for the residential and services sectors for weekends and weekdays.
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- ``resources/daily_heat_demand_total_base_s<simpl>_<clusters>.nc``: Daily heat demand per cluster.
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Outputs
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-------
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- ``resources/hourly_heat_demand_total_base_s<simpl>_<clusters>.nc``:
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"""
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from itertools import product
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import pandas as pd
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import xarray as xr
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from _helpers import generate_periodic_profiles, get_snapshots, 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_hourly_heat_demand",
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scope="total",
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clusters=5,
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)
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set_scenario_config(snakemake)
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snapshots = get_snapshots(
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snakemake.params.snapshots, snakemake.params.drop_leap_day
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)
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daily_space_heat_demand = (
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xr.open_dataarray(snakemake.input.heat_demand)
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.to_pandas()
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.reindex(index=snapshots, method="ffill")
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)
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intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
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sectors = ["residential", "services"]
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uses = ["water", "space"]
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heat_demand = {}
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for sector, use in product(sectors, uses):
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weekday = list(intraday_profiles[f"{sector} {use} weekday"])
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weekend = list(intraday_profiles[f"{sector} {use} weekend"])
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weekly_profile = weekday * 5 + weekend * 2
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intraday_year_profile = generate_periodic_profiles(
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daily_space_heat_demand.index.tz_localize("UTC"),
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nodes=daily_space_heat_demand.columns,
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weekly_profile=weekly_profile,
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)
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if use == "space":
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heat_demand[f"{sector} {use}"] = (
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daily_space_heat_demand * intraday_year_profile
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)
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else:
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heat_demand[f"{sector} {use}"] = intraday_year_profile
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heat_demand = pd.concat(heat_demand, axis=1, names=["sector use", "node"])
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heat_demand.index.name = "snapshots"
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ds = heat_demand.stack(future_stack=True).to_xarray()
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ds.to_netcdf(snakemake.output.heat_demand)
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