#!/usr/bin/env python # -*- coding: utf-8 -*- # SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ 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: luisa: grid_codes: distance: natura: max_depth: max_shore_distance: min_shore_distance: capacity_per_sqkm: correction_factor: min_p_max_pu: clip_p_max_pu: resource: .. seealso:: Documentation of the configuration file ``config/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) at 100m resolution. .. image:: img/corine.png :scale: 33 % - ``data/LUISA_basemap_020321_50m.tif``: `LUISA Base Map `_ land coverage dataset at 50m resolution similar to CORINE. For codes in relation to CORINE land cover, see `Annex 1 of the technical documentation `_. - ``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/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/" + params["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 `atlite `_ library. This uses the CORINE land use data, LUISA land use data, Natura2000 nature reserves, GEBCO bathymetry data, and shipping lanes. .. 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``. The maximal installable potential for the node (`p_nom_max`) is computed by adding 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. """ import functools import logging import time import atlite import geopandas as gpd import numpy as np import pandas as pd import xarray as xr from _helpers import configure_logging, set_scenario_config from dask.distributed import Client from pypsa.geo import haversine from shapely.geometry import LineString logger = logging.getLogger(__name__) if __name__ == "__main__": if "snakemake" not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake("build_renewable_profiles", technology="offwind-dc") configure_logging(snakemake) set_scenario_config(snakemake) nprocesses = int(snakemake.threads) noprogress = snakemake.config["run"].get("disable_progressbar", True) noprogress = noprogress or not snakemake.config["atlite"]["show_progress"] params = snakemake.params.renewable[snakemake.wildcards.technology] resource = params["resource"] # pv panel params / wind turbine params tech = next(t for t in ["panel", "turbine"] if t in resource) models = resource[tech] if not isinstance(models, dict): models = {0: models} resource[tech] = models[next(iter(models))] correction_factor = params.get("correction_factor", 1.0) capacity_per_sqkm = params["capacity_per_sqkm"] snapshots = snakemake.params.snapshots if correction_factor != 1.0: logger.info(f"correction_factor is set as {correction_factor}") if nprocesses > 1: client = Client(n_workers=nprocesses, threads_per_worker=1) else: client = None sns = pd.date_range(freq="h", **snapshots) cutout = atlite.Cutout(snakemake.input.cutout).sel(time=sns) regions = gpd.read_file(snakemake.input.regions) assert not regions.empty, ( f"List of regions in {snakemake.input.regions} is empty, please " "disable the corresponding renewable technology" ) # do not pull up, set_index does not work if geo dataframe is empty regions = regions.set_index("name").rename_axis("bus") buses = regions.index res = params.get("excluder_resolution", 100) excluder = atlite.ExclusionContainer(crs=3035, res=res) if params["natura"]: excluder.add_raster(snakemake.input.natura, nodata=0, allow_no_overlap=True) for dataset in ["corine", "luisa"]: kwargs = {"nodata": 0} if dataset == "luisa" else {} settings = params.get(dataset, {}) if not settings: continue if dataset == "luisa" and res > 50: logger.info( "LUISA data is available at 50m resolution, " f"but coarser {res}m resolution is used." ) if isinstance(settings, list): settings = {"grid_codes": settings} if "grid_codes" in settings: codes = settings["grid_codes"] excluder.add_raster( snakemake.input[dataset], codes=codes, invert=True, crs=3035, **kwargs ) if settings.get("distance", 0.0) > 0.0: codes = settings["distance_grid_codes"] buffer = settings["distance"] excluder.add_raster( snakemake.input[dataset], codes=codes, buffer=buffer, crs=3035, **kwargs ) if params.get("ship_threshold"): shipping_threshold = ( params["ship_threshold"] * 8760 * 6 ) # approximation because 6 years of data which is hourly collected func = functools.partial(np.less, shipping_threshold) excluder.add_raster( snakemake.input.ship_density, codes=func, crs=4326, allow_no_overlap=True ) if params.get("max_depth"): # lambda not supported for atlite + multiprocessing # use named function np.greater with partially frozen argument instead # and exclude areas where: -max_depth > grid cell depth func = functools.partial(np.greater, -params["max_depth"]) excluder.add_raster(snakemake.input.gebco, codes=func, crs=4326, nodata=-1000) if "min_shore_distance" in params: buffer = params["min_shore_distance"] excluder.add_geometry(snakemake.input.country_shapes, buffer=buffer) if "max_shore_distance" in params: buffer = params["max_shore_distance"] excluder.add_geometry( snakemake.input.country_shapes, buffer=buffer, invert=True ) logger.info("Calculate landuse availability...") start = time.time() kwargs = dict(nprocesses=nprocesses, disable_progressbar=noprogress) availability = cutout.availabilitymatrix(regions, excluder, **kwargs) duration = time.time() - start logger.info(f"Completed landuse availability calculation ({duration:2.2f}s)") # For Moldova and Ukraine: Overwrite parts not covered by Corine with # externally determined available areas if "availability_matrix_MD_UA" in snakemake.input.keys(): availability_MDUA = xr.open_dataarray( snakemake.input["availability_matrix_MD_UA"] ) availability.loc[availability_MDUA.coords] = availability_MDUA area = cutout.grid.to_crs(3035).area / 1e6 area = xr.DataArray( area.values.reshape(cutout.shape), [cutout.coords["y"], cutout.coords["x"]] ) potential = capacity_per_sqkm * availability.sum("bus") * area func = getattr(cutout, resource.pop("method")) if client is not None: resource["dask_kwargs"] = {"scheduler": client} logger.info("Calculate average capacity factor...") start = time.time() capacity_factor = correction_factor * func(capacity_factor=True, **resource) layout = capacity_factor * area * capacity_per_sqkm duration = time.time() - start logger.info(f"Completed average capacity factor calculation ({duration:2.2f}s)") profiles = [] capacities = [] for year, model in models.items(): logger.info( f"Calculate weighted capacity factor time series for model {model}..." ) start = time.time() resource[tech] = model profile, capacity = func( matrix=availability.stack(spatial=["y", "x"]), layout=layout, index=buses, per_unit=True, return_capacity=True, **resource, ) dim = {"year": [year]} profile = profile.expand_dims(dim) capacity = capacity.expand_dims(dim) profiles.append(profile.rename("profile")) capacities.append(capacity.rename("weight")) duration = time.time() - start logger.info( f"Completed weighted capacity factor time series calculation for model {model} ({duration:2.2f}s)" ) profiles = xr.merge(profiles) capacities = xr.merge(capacities) logger.info("Calculating maximal capacity per bus") p_nom_max = capacity_per_sqkm * availability @ area logger.info("Calculate average distances.") layoutmatrix = (layout * availability).stack(spatial=["y", "x"]) coords = cutout.grid[["x", "y"]] bus_coords = regions[["x", "y"]] average_distance = [] centre_of_mass = [] for bus in buses: row = layoutmatrix.sel(bus=bus).data nz_b = row != 0 row = row[nz_b] co = coords[nz_b] distances = haversine(bus_coords.loc[bus], co) average_distance.append((distances * (row / row.sum())).sum()) centre_of_mass.append(co.values.T @ (row / row.sum())) average_distance = xr.DataArray(average_distance, [buses]) centre_of_mass = xr.DataArray(centre_of_mass, [buses, ("spatial", ["x", "y"])]) ds = xr.merge( [ correction_factor * profiles, capacities, p_nom_max.rename("p_nom_max"), potential.rename("potential"), average_distance.rename("average_distance"), ] ) if snakemake.wildcards.technology.startswith("offwind"): logger.info("Calculate underwater fraction of connections.") offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).unary_union underwater_fraction = [] for bus in buses: p = centre_of_mass.sel(bus=bus).data line = LineString([p, regions.loc[bus, ["x", "y"]]]) frac = line.intersection(offshore_shape).length / line.length underwater_fraction.append(frac) ds["underwater_fraction"] = xr.DataArray(underwater_fraction, [buses]) # select only buses with some capacity and minimal capacity factor mean_profile = ds["profile"].mean("time") if "year" in ds.indexes: mean_profile = mean_profile.max("year") ds = ds.sel( bus=( (mean_profile > params.get("min_p_max_pu", 0.0)) & (ds["p_nom_max"] > params.get("min_p_nom_max", 0.0)) ) ) if "clip_p_max_pu" in params: min_p_max_pu = params["clip_p_max_pu"] ds["profile"] = ds["profile"].where(ds["profile"] >= min_p_max_pu, 0) ds.to_netcdf(snakemake.output.profile) if client is not None: client.shutdown()