#!/usr/bin/env python # SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors # # SPDX-License-Identifier: GPL-3.0-or-later """Calculates for each network node the (i) installable capacity (based on land-use), (ii) the available generation time series (based on weather data), and (iii) the average distance from the node for onshore wind, AC-connected offshore wind, DC-connected offshore wind and solar PV generators. In addition for offshore wind it calculates the fraction of the grid connection which is under water. .. note:: Hydroelectric profiles are built in script :mod:`build_hydro_profiles`. Relevant settings ----------------- .. code:: yaml snapshots: atlite: nprocesses: renewable: {technology}: cutout: corine: grid_codes: distance: natura: max_depth: max_shore_distance: min_shore_distance: capacity_per_sqkm: correction_factor: potential: min_p_max_pu: clip_p_max_pu: resource: .. seealso:: Documentation of the configuration file ``config.yaml`` at :ref:`snapshots_cf`, :ref:`atlite_cf`, :ref:`renewable_cf` Inputs ------ - ``data/bundle/corine/g250_clc06_V18_5.tif``: `CORINE Land Cover (CLC) `_ inventory on `44 classes `_ of land use (e.g. forests, arable land, industrial, urban areas). .. image:: ../img/corine.png :scale: 33 % - ``data/bundle/GEBCO_2014_2D.nc``: A `bathymetric `_ data set with a global terrain model for ocean and land at 15 arc-second intervals by the `General Bathymetric Chart of the Oceans (GEBCO) `_. .. image:: ../img/gebco_2019_grid_image.jpg :scale: 50 % **Source:** `GEBCO `_ - ``resources/natura.tiff``: confer :ref:`natura` - ``resources/country_shapes.geojson``: confer :ref:`shapes` - ``resources/offshore_shapes.geojson``: confer :ref:`shapes` - ``resources/regions_onshore.geojson``: (if not offshore wind), confer :ref:`busregions` - ``resources/regions_offshore.geojson``: (if offshore wind), :ref:`busregions` - ``"cutouts/" + config["renewable"][{technology}]['cutout']``: :ref:`cutout` - ``networks/base.nc``: :ref:`base` Outputs ------- - ``resources/profile_{technology}.nc`` with the following structure =================== ========== ========================================================= Field Dimensions Description =================== ========== ========================================================= profile bus, time the per unit hourly availability factors for each node ------------------- ---------- --------------------------------------------------------- weight bus sum of the layout weighting for each node ------------------- ---------- --------------------------------------------------------- p_nom_max bus maximal installable capacity at the node (in MW) ------------------- ---------- --------------------------------------------------------- potential y, x layout of generator units at cutout grid cells inside the Voronoi cell (maximal installable capacity at each grid cell multiplied by capacity factor) ------------------- ---------- --------------------------------------------------------- average_distance bus average distance of units in the Voronoi cell to the grid node (in km) ------------------- ---------- --------------------------------------------------------- underwater_fraction bus fraction of the average connection distance which is under water (only for offshore) =================== ========== ========================================================= - **profile** .. image:: ../img/profile_ts.png :scale: 33 % :align: center - **p_nom_max** .. image:: ../img/p_nom_max_hist.png :scale: 33 % :align: center - **potential** .. image:: ../img/potential_heatmap.png :scale: 33 % :align: center - **average_distance** .. image:: ../img/distance_hist.png :scale: 33 % :align: center - **underwater_fraction** .. image:: ../img/underwater_hist.png :scale: 33 % :align: center Description ----------- This script functions at two main spatial resolutions: the resolution of the network nodes and their `Voronoi cells `_, and the resolution of the cutout grid cells for the weather data. Typically the weather data grid is finer than the network nodes, so we have to work out the distribution of generators across the grid cells within each Voronoi cell. This is done by taking account of a combination of the available land at each grid cell and the capacity factor there. First the script computes how much of the technology can be installed at each cutout grid cell and each node using the `GLAES `_ library. This uses the CORINE land use data, Natura2000 nature reserves and GEBCO bathymetry data. .. image:: ../img/eligibility.png :scale: 50 % :align: center To compute the layout of generators in each node's Voronoi cell, the installable potential in each grid cell is multiplied with the capacity factor at each grid cell. This is done since we assume more generators are installed at cells with a higher capacity factor. .. image:: ../img/offwinddc-gridcell.png :scale: 50 % :align: center .. image:: ../img/offwindac-gridcell.png :scale: 50 % :align: center .. image:: ../img/onwind-gridcell.png :scale: 50 % :align: center .. image:: ../img/solar-gridcell.png :scale: 50 % :align: center This layout is then used to compute the generation availability time series from the weather data cutout from ``atlite``. Two methods are available to compute the maximal installable potential for the node (`p_nom_max`): ``simple`` and ``conservative``: - ``simple`` adds up the installable potentials of the individual grid cells. If the model comes close to this limit, then the time series may slightly overestimate production since it is assumed the geographical distribution is proportional to capacity factor. - ``conservative`` assertains the nodal limit by increasing capacities proportional to the layout until the limit of an individual grid cell is reached. """ import logging from _helpers import configure_logging import os import atlite import numpy as np import xarray as xr import pandas as pd import multiprocessing as mp import matplotlib.pyplot as plt import progressbar as pgb from scipy.sparse import csr_matrix, vstack from pypsa.geo import haversine from vresutils import landuse as vlanduse from vresutils.array import spdiag logger = logging.getLogger(__name__) bounds = dx = dy = config = paths = gebco = clc = natura = None def init_globals(bounds_xXyY, n_dx, n_dy, n_config, n_paths): # Late import so that the GDAL Context is only created in the new processes global gl, gk, gdal import glaes as gl import geokit as gk from osgeo import gdal as gdal # global in each process of the multiprocessing.Pool global bounds, dx, dy, config, paths, gebco, clc, natura bounds = gk.Extent.from_xXyY(bounds_xXyY) dx = n_dx dy = n_dy config = n_config paths = n_paths if "max_depth" in config: gebco = gk.raster.loadRaster(paths["gebco"]) gebco.SetProjection(gk.srs.loadSRS(4326).ExportToWkt()) clc = gk.raster.loadRaster(paths["corine"]) clc.SetProjection(gk.srs.loadSRS(3035).ExportToWkt()) natura = gk.raster.loadRaster(paths["natura"]) def downsample_to_coarse_grid(bounds, dx, dy, mask, data): # The GDAL warp function with the 'average' resample algorithm needs a band of zero values of at least # the size of one coarse cell around the original raster or it produces erroneous results orig = mask.createRaster(data=data) padded_extent = mask.extent.castTo(bounds.srs).pad(max(dx, dy)).castTo(mask.srs) padded = padded_extent.fit((mask.pixelWidth, mask.pixelHeight)).warp(orig, mask.pixelWidth, mask.pixelHeight) orig = None # free original raster average = bounds.createRaster(dx, dy, dtype=gdal.GDT_Float32) assert gdal.Warp(average, padded, resampleAlg='average') == 1, "gdal warp failed: %s" % gdal.GetLastErrorMsg() return average def calculate_potential(gid, save_map=None): feature = gk.vector.extractFeature(paths["regions"], where=gid) ec = gl.ExclusionCalculator(feature.geom) corine = config.get("corine", {}) if isinstance(corine, list): corine = {'grid_codes': corine} if "grid_codes" in corine: ec.excludeRasterType(clc, value=corine["grid_codes"], invert=True) if corine.get("distance", 0.) > 0.: ec.excludeRasterType(clc, value=corine["distance_grid_codes"], buffer=corine["distance"]) if config.get("natura", False): ec.excludeRasterType(natura, value=1) if "max_depth" in config: ec.excludeRasterType(gebco, (None, -config["max_depth"])) # TODO compute a distance field as a raster beforehand if 'max_shore_distance' in config: ec.excludeVectorType(paths["country_shapes"], buffer=config['max_shore_distance'], invert=True) if 'min_shore_distance' in config: ec.excludeVectorType(paths["country_shapes"], buffer=config['min_shore_distance']) if save_map is not None: ec.draw() plt.savefig(save_map, transparent=True) plt.close() availability = downsample_to_coarse_grid(bounds, dx, dy, ec.region, np.where(ec.region.mask, ec._availability, 0)) return csr_matrix(gk.raster.extractMatrix(availability).flatten() / 100.) if __name__ == '__main__': if 'snakemake' not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake('build_renewable_profiles', technology='solar') configure_logging(snakemake) pgb.streams.wrap_stderr() config = snakemake.config['renewable'][snakemake.wildcards.technology] time = pd.date_range(freq='m', **snakemake.config['snapshots']) params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]])) cutout = atlite.Cutout(config['cutout'], cutout_dir=os.path.dirname(snakemake.input.cutout), **params) minx, maxx, miny, maxy = cutout.extent dx = (maxx - minx) / (cutout.shape[1] - 1) dy = (maxy - miny) / (cutout.shape[0] - 1) bounds_xXyY = (minx - dx/2., maxx + dx/2., miny - dy/2., maxy + dy/2.) # Use GLAES to compute available potentials and the transition matrix paths = dict(snakemake.input) # Use the following for testing the default windows method on linux # mp.set_start_method('spawn') with mp.Pool(initializer=init_globals, initargs=(bounds_xXyY, dx, dy, config, paths), maxtasksperchild=20, processes=snakemake.config['atlite'].get('nprocesses', 2)) as pool: # The GDAL library creates a GDAL context on module import, which may not be shared over multiple # processes or the PROJ4 library has a hickup, so we import only after forking. import geokit as gk regions = gk.vector.extractFeatures(paths["regions"], onlyAttr=True) buses = pd.Index(regions['name'], name="bus") widgets = [ pgb.widgets.Percentage(), ' ', pgb.widgets.SimpleProgress(format='(%s)' % pgb.widgets.SimpleProgress.DEFAULT_FORMAT), ' ', pgb.widgets.Bar(), ' ', pgb.widgets.Timer(), ' ', pgb.widgets.ETA() ] progressbar = pgb.ProgressBar(prefix='Compute GIS potentials: ', widgets=widgets, max_value=len(regions)) matrix = vstack(list(progressbar(pool.imap(calculate_potential, regions.index)))) potentials = config['capacity_per_sqkm'] * vlanduse._cutout_cell_areas(cutout) potmatrix = matrix * spdiag(potentials.ravel()) if not config.get('keep_all_available_areas', False): potmatrix.data[potmatrix.data < 1.] = 0 # ignore weather cells where only less than 1 MW can be installed potmatrix.eliminate_zeros() resource = config['resource'] func = getattr(cutout, resource.pop('method')) correction_factor = config.get('correction_factor', 1.) if correction_factor != 1.: logger.warning('correction_factor is set as {}'.format(correction_factor)) capacity_factor = correction_factor * func(capacity_factor=True, show_progress='Compute capacity factors: ', **resource).stack(spatial=('y', 'x')).values layoutmatrix = potmatrix * spdiag(capacity_factor) profile, capacities = func(matrix=layoutmatrix, index=buses, per_unit=True, return_capacity=True, show_progress='Compute profiles: ', **resource) p_nom_max_meth = config.get('potential', 'conservative') if p_nom_max_meth == 'simple': p_nom_max = xr.DataArray(np.asarray(potmatrix.sum(axis=1)).squeeze(), [buses]) elif p_nom_max_meth == 'conservative': # p_nom_max has to be calculated for each bus and is the minimal ratio # (min over all weather grid cells of the bus region) between the available # potential (potmatrix) and the used normalised layout (layoutmatrix / # capacities), so we would like to calculate i.e. potmatrix / (layoutmatrix / # capacities). Since layoutmatrix = potmatrix * capacity_factor, this # corresponds to capacities/max(capacity factor in the voronoi cell) p_nom_max = xr.DataArray([1./np.max(capacity_factor[inds]) if len(inds) else 0. for inds in np.split(potmatrix.indices, potmatrix.indptr[1:-1])], [buses]) * capacities else: raise AssertionError('Config key `potential` should be one of "simple" (default) or "conservative",' ' not "{}"'.format(p_nom_max_meth)) layout = xr.DataArray(np.asarray(potmatrix.sum(axis=0)).reshape(cutout.shape), [cutout.meta.indexes[ax] for ax in ['y', 'x']]) # Determine weighted average distance from substation cell_coords = cutout.grid_coordinates() average_distance = [] for i in regions.index: row = layoutmatrix[i] distances = haversine(regions.loc[i, ['x', 'y']], cell_coords[row.indices])[0] average_distance.append((distances * (row.data / row.data.sum())).sum()) average_distance = xr.DataArray(average_distance, [buses]) ds = xr.merge([(correction_factor * profile).rename('profile'), capacities.rename('weight'), p_nom_max.rename('p_nom_max'), layout.rename('potential'), average_distance.rename('average_distance')]) if snakemake.wildcards.technology.startswith("offwind"): import geopandas as gpd from shapely.geometry import LineString offshore_shape = gpd.read_file(snakemake.input.offshore_shapes).unary_union underwater_fraction = [] for i in regions.index: row = layoutmatrix[i] centre_of_mass = (cell_coords[row.indices] * (row.data / row.data.sum())[:,np.newaxis]).sum(axis=0) line = LineString([centre_of_mass, regions.loc[i, ['x', 'y']]]) underwater_fraction.append(line.intersection(offshore_shape).length / line.length) ds['underwater_fraction'] = xr.DataArray(underwater_fraction, [buses]) # select only buses with some capacity and minimal capacity factor ds = ds.sel(bus=((ds['profile'].mean('time') > config.get('min_p_max_pu', 0.)) & (ds['p_nom_max'] > config.get('min_p_nom_max', 0.)))) if 'clip_p_max_pu' in config: ds['profile'].values[ds['profile'].values < config['clip_p_max_pu']] = 0. ds.to_netcdf(snakemake.output.profile)