#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2024 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: min_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)
<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) at 100m
resolution.
.. image:: img/corine.png
:scale: 33 %
- ``data/LUISA_basemap_020321_50m.tif``: `LUISA Base Map
<https://publications.jrc.ec.europa.eu/repository/handle/JRC124621>`_ land
coverage dataset at 50m resolution similar to CORINE. For codes in relation to
CORINE land cover, see `Annex 1 of the technical documentation
<https://publications.jrc.ec.europa.eu/repository/bitstream/JRC124621/technical_report_luisa_basemap_2018_v7_final.pdf>`_.
- ``data/bundle/gebco/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 `atlite
<https://github.com/pypsa/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 xarray as xr
from _helpers import configure_logging, get_snapshots, 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"]
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 = get_snapshots(snakemake.params.snapshots, snakemake.params.drop_leap_day)
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 params.get("min_depth"):
func = functools.partial(np.greater, -params["min_depth"])
excluder.add_raster(
snakemake.input.gebco, codes=func, crs=4326, nodata=-1000, invert=True
)
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()