067d259ff6
* and reduce corresponding sections in readme * and modify environment docs * and add bibtex support (tentative)
277 lines
12 KiB
Python
277 lines
12 KiB
Python
#!/usr/bin/env python
|
|
"""
|
|
Summary
|
|
-------
|
|
The script ``build_renewable_profiles.py`` calculates for each node several geographical properties:
|
|
|
|
1. the installable capacity (based on land-use)
|
|
2. the available generation time series (based on weather data) and
|
|
3. the average distance from the node for onshore wind, AC-connected offshore wind, DC-connected offshore wind and solar PV generators.
|
|
4. 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
|
|
-----------------
|
|
|
|
config.renewable (describes the parameters for onwind, offwind-ac, offwind-dc
|
|
and solar)
|
|
config.snapshots (describes the time dimensions of the selection of snapshots)
|
|
|
|
Inputs
|
|
------
|
|
|
|
base_network
|
|
land-use shapes
|
|
region shapes for onshore, offshore and countries
|
|
cutout
|
|
|
|
Outputs
|
|
-------
|
|
|
|
profile_{tech}.nc for tech in [onwind,offwind-ac,offwind-dc,solar]
|
|
|
|
profile_{tech}.nc contains five common fields:
|
|
|
|
profile (bus x time) - the per unit hourly availability factors for each node
|
|
weight (bus) - the sum of the layout weighting for each node
|
|
p_nom_max (bus) - the maximal installable capacity at the node (in MW)
|
|
potential (y,x) - the layout of generator units at cutout grid cells inside the
|
|
voronoi cell (maximal installable capacity at each grid cell multiplied by the
|
|
capacity factor)
|
|
average_distance (bus) - the average distance of units in the voronoi cell to
|
|
the grid node (in km)
|
|
|
|
for offshore we also have:
|
|
|
|
underwater_fraction (bus) - the fraction of the average connection distance
|
|
which is under water
|
|
|
|
Long description:
|
|
-----------------
|
|
|
|
First the script computes how much of the technology can be installed at each
|
|
cutout grid cell and each node using the library `GLAES
|
|
<https://github.com/FZJ-IEK3-VSA/glaes>`_. This uses the CORINE land use data,
|
|
Natura2000 nature reserves and GEBCO for bathymetry.
|
|
|
|
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 (since we assume more generators are installed at cells with a higher
|
|
capacity factor).
|
|
|
|
This layout is then used to compute the generation availability time series from
|
|
the atlite cutout.
|
|
|
|
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 we 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 matplotlib.pyplot as plt
|
|
|
|
import os
|
|
import atlite
|
|
import numpy as np
|
|
import xarray as xr
|
|
import pandas as pd
|
|
import multiprocessing as mp
|
|
|
|
import glaes as gl
|
|
import geokit as gk
|
|
from osgeo import gdal
|
|
from scipy.sparse import csr_matrix, vstack
|
|
|
|
from pypsa.geo import haversine
|
|
from vresutils import landuse as vlanduse
|
|
from vresutils.array import spdiag
|
|
|
|
import progressbar as pgb
|
|
import logging
|
|
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):
|
|
# 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
|
|
|
|
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__':
|
|
pgb.streams.wrap_stderr()
|
|
logging.basicConfig(level=snakemake.config['logging_level'])
|
|
|
|
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:
|
|
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())
|
|
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)
|