Merge branch 'master' into snakemake_dependencies_in_functions

README.md

@@ -58,7 +58,7 @@ The dataset consists of:
 - Electrical demand time series from the
   [OPSD project](https://open-power-system-data.org/).
 - Renewable time series based on ERA5 and SARAH, assembled using the [atlite tool](https://github.com/FRESNA/atlite).
-- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [vresutils library](https://github.com/FRESNA/vresutils) and the [glaes library](https://github.com/FZJ-IEK3-VSA/glaes).
+- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [atlite library](https://github.com/PyPSA/atlite).
 
 Already-built versions of the model can be found in the accompanying [Zenodo
 repository](https://doi.org/10.5281/zenodo.3601881).

envs/environment.yaml

@@ -10,9 +10,9 @@ dependencies:
   - python>=3.8
   - pip
 
-  - pypsa>=0.18
+  - pypsa>=0.18.1
   - atlite>=0.2.5
-  - dask<=2021.3.1 # until https://github.com/dask/dask/issues/7583 is solved
+  - dask
 
   # Dependencies of the workflow itself
   - xlrd
@@ -36,7 +36,7 @@ dependencies:
   - progressbar2
   - pyomo
   - matplotlib
-  - proj<8
+  - proj
 
   # Keep in conda environment when calling ipython
   - ipython
@@ -54,5 +54,5 @@ dependencies:
   - tabula-py
 
   - pip:
-    - vresutils==0.3.1
+    - vresutils>=0.3.1
     - tsam>=1.1.0

scripts/add_electricity.py

@@ -95,7 +95,6 @@ import powerplantmatching as pm
 from powerplantmatching.export import map_country_bus
 
 from vresutils.costdata import annuity
-from vresutils.load import timeseries_opsd
 from vresutils import transfer as vtransfer
 
 idx = pd.IndexSlice
@@ -218,7 +217,6 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
 
             # relative factors 0.6 and 0.4 have been determined from a linear
             # regression on the country to continent load data
-            # (refer to vresutils.load._upsampling_weights)
             factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
             return pd.DataFrame(factors.values * l.values[:,np.newaxis],
                                 index=l.index, columns=factors.index)

scripts/base_network.py

@@ -97,7 +97,7 @@ def _get_country(df):
 
 
 def _find_closest_links(links, new_links, distance_upper_bound=1.5):
-    treecoords = np.asarray([np.asarray(shapely.wkt.loads(s))[[0, -1]].flatten()
+    treecoords = np.asarray([np.asarray(shapely.wkt.loads(s).coords)[[0, -1]].flatten()
                               for s in links.geometry])
     querycoords = np.vstack([new_links[['x1', 'y1', 'x2', 'y2']],
                             new_links[['x2', 'y2', 'x1', 'y1']]])

scripts/build_shapes.py

@@ -79,7 +79,7 @@ from itertools import takewhile
 import pandas as pd
 import geopandas as gpd
 from shapely.geometry import MultiPolygon, Polygon
-from shapely.ops import cascaded_union
+from shapely.ops import unary_union
 import pycountry as pyc
 
 logger = logging.getLogger(__name__)
@@ -95,7 +95,7 @@ def _get_country(target, **keys):
 
 def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
     if isinstance(polys, MultiPolygon):
-        polys = sorted(polys, key=attrgetter('area'), reverse=True)
+        polys = sorted(polys.geoms, key=attrgetter('area'), reverse=True)
         mainpoly = polys[0]
         mainlength = np.sqrt(mainpoly.area/(2.*np.pi))
         if mainpoly.area > minarea:
@@ -138,7 +138,7 @@ def country_cover(country_shapes, eez_shapes=None):
     if eez_shapes is not None:
         shapes += list(eez_shapes)
 
-    europe_shape = cascaded_union(shapes)
+    europe_shape = unary_union(shapes)
     if isinstance(europe_shape, MultiPolygon):
         europe_shape = max(europe_shape, key=attrgetter('area'))
     return Polygon(shell=europe_shape.exterior)

scripts/cluster_network.py

@@ -140,6 +140,9 @@ from functools import reduce
 from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_spectral_clustering,
                                      _make_consense, get_clustering_from_busmap)
 
+import warnings
+warnings.filterwarnings(action='ignore', category=UserWarning)
+
 from add_electricity import load_costs
 
 idx = pd.IndexSlice
@@ -215,7 +218,7 @@ def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
     results = opt.solve(m)
     assert results['Solver'][0]['Status'] == 'ok', f"Solver returned non-optimally: {results}"
 
-    return pd.Series(m.n.get_values(), index=L.index).astype(int)
+    return pd.Series(m.n.get_values(), index=L.index).round().astype(int)
 
 
 def busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights=None, algorithm="kmeans", **algorithm_kwds):