#!/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:
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/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) <https://land.copernicus.eu/pan-european/corine-land-cover>`_ inventory on `44 classes <https://wiki.openstreetmap.org/wiki/Corine_Land_Cover#Tagging>`_ 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 <https://en.wikipedia.org/wiki/Bathymetry>`_ 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) <https://www.gebco.net/data_and_products/gridded_bathymetry_data/>`_.
.. image:: img/gebco_2019_grid_image.jpg
:scale: 50 %
**Source:** `GEBCO <https://www.gebco.net/data_and_products/images/gebco_2019_grid_image.jpg>`_
- ``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
<https://en.wikipedia.org/wiki/Voronoi_diagram>`_, 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
<https://github.com/FZJ-IEK3-VSA/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 functools
import logging
import time
import atlite
import geopandas as gpd
import numpy as np
import xarray as xr
from _helpers import configure_logging
from dask.distributed import Client, LocalCluster
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="solar")
configure_logging(snakemake)
nprocesses = int(snakemake.threads)
noprogress = snakemake.config["run"].get("disable_progressbar", True)
params = snakemake.params["renewable"][snakemake.wildcards.technology]
resource = params["resource"] # pv panel params / wind turbine params
correction_factor = params.get("correction_factor", 1.0)
capacity_per_sqkm = params["capacity_per_sqkm"]
p_nom_max_meth = params.get("potential", "conservative")
if isinstance(params.get("corine", {}), list):
params["corine"] = {"grid_codes": params["corine"]}
if correction_factor != 1.0:
logger.info(f"correction_factor is set as {correction_factor}")
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
cutout = atlite.Cutout(snakemake.input.cutout)
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)
corine = params.get("corine", {})
if "grid_codes" in corine:
codes = corine["grid_codes"]
excluder.add_raster(snakemake.input.corine, codes=codes, invert=True, crs=3035)
if corine.get("distance", 0.0) > 0.0:
codes = corine["distance_grid_codes"]
buffer = corine["distance"]
excluder.add_raster(
snakemake.input.corine, codes=codes, buffer=buffer, crs=3035
)
if "ship_threshold" in params:
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
)
kwargs = dict(nprocesses=nprocesses, disable_progressbar=noprogress)
if noprogress:
logger.info("Calculate landuse availabilities...")
start = time.time()
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
duration = time.time() - start
logger.info(f"Completed availability calculation ({duration:2.2f}s)")
else:
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
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"))
resource["dask_kwargs"] = {"scheduler": client}
capacity_factor = correction_factor * func(capacity_factor=True, **resource)
layout = capacity_factor * area * capacity_per_sqkm
profile, capacities = func(
matrix=availability.stack(spatial=["y", "x"]),
layout=layout,
index=buses,
per_unit=True,
return_capacity=True,
**resource,
)
logger.info(f"Calculating maximal capacity per bus (method '{p_nom_max_meth}')")
if p_nom_max_meth == "simple":
p_nom_max = capacity_per_sqkm * availability @ area
elif p_nom_max_meth == "conservative":
max_cap_factor = capacity_factor.where(availability != 0).max(["x", "y"])
p_nom_max = capacities / max_cap_factor
else:
raise AssertionError(
'params key `potential` should be one of "simple" '
f'(default) or "conservative", not "{p_nom_max_meth}"'
)
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 * profile).rename("profile"),
capacities.rename("weight"),
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
ds = ds.sel(
bus=(
(ds["profile"].mean("time") > 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)
client.shutdown()