100 lines
2.7 KiB
Python
100 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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This rule builds heat demand time series using heating degree day (HDD)
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approximation.
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Snapshots are resampled to daily time resolution and ``Atlite.convert.heat_demand`` is used to convert ambient temperature from the default weather cutout to heat demand time series for the respective cutout.
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Heat demand is distributed by population to clustered onshore regions.
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The rule is executed in ``build_sector.smk``.
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.. seealso::
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`Atlite.Cutout.heat_demand <https://atlite.readthedocs.io/en/master/ref_api.html#module-atlite.convert>`_
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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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- ``resources/<run_name>/pop_layout_<scope>.nc``: Population layout (spatial population distribution).
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- ``resources/<run_name>/regions_onshore_elec_s<simpl>_<clusters>.geojson``: Onshore region shapes.
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- ``cutout``: Weather data cutout, as specified in config
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Outputs
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-------
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- ``resources/daily_heat_demand_<scope>_elec_s<simpl>_<clusters>.nc``:
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Relevant settings
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-----------------
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.. code:: yaml
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atlite:
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default_cutout``:
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"""
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import atlite
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import geopandas as gpd
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import numpy as np
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import xarray as xr
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from _helpers import get_snapshots, set_scenario_config
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from dask.distributed import Client, LocalCluster
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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_daily_heat_demands",
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scope="total",
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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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nprocesses = int(snakemake.threads)
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cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
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client = Client(cluster, asynchronous=True)
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cutout_name = snakemake.input.cutout
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time = get_snapshots(snakemake.params.snapshots, snakemake.params.drop_leap_day)
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daily = get_snapshots(
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snakemake.params.snapshots,
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snakemake.params.drop_leap_day,
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freq="D",
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)
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cutout = atlite.Cutout(cutout_name).sel(time=time)
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clustered_regions = (
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gpd.read_file(snakemake.input.regions_onshore).set_index("name").buffer(0)
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)
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I = cutout.indicatormatrix(clustered_regions) # noqa: E741
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pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
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stacked_pop = pop_layout.stack(spatial=("y", "x"))
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M = I.T.dot(np.diag(I.dot(stacked_pop)))
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heat_demand = cutout.heat_demand(
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matrix=M.T,
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index=clustered_regions.index,
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dask_kwargs=dict(scheduler=client),
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show_progress=False,
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).sel(time=daily)
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heat_demand.to_netcdf(snakemake.output.heat_demand)
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