pypsa-eur/scripts/build_solar_thermal_profiles.py

"""Build solar thermal collector time series."""

import geopandas as gpd
import atlite
import pandas as pd
import xarray as xr
import numpy as np
from dask.distributed import Client, LocalCluster

if __name__ == '__main__':
    if 'snakemake' not in globals():
        from helper import mock_snakemake
        snakemake = mock_snakemake(
            'build_solar_thermal_profiles',
            simpl='',
            clusters=48,
        )

    nprocesses = int(snakemake.threads)
    cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
    client = Client(cluster, asynchronous=True)

    config = snakemake.config['solar_thermal']

    time = pd.date_range(freq='h', **snakemake.config['snapshots'])
    cutout_config = snakemake.config['atlite']['cutout']
    cutout = atlite.Cutout(cutout_config).sel(time=time)

    clustered_regions = gpd.read_file(
        snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()

    I = cutout.indicatormatrix(clustered_regions)

    pop_layout = xr.open_dataarray(snakemake.input.pop_layout)

    stacked_pop = pop_layout.stack(spatial=('y', 'x'))
    M = I.T.dot(np.diag(I.dot(stacked_pop)))

    nonzero_sum = M.sum(axis=0, keepdims=True)
    nonzero_sum[nonzero_sum == 0.] = 1.
    M_tilde = M / nonzero_sum

    solar_thermal = cutout.solar_thermal(**config, matrix=M_tilde.T,
                                            index=clustered_regions.index,
                                            dask_kwargs=dict(scheduler=client),
                                            show_progress=False)

    solar_thermal.to_netcdf(snakemake.output.solar_thermal)