Merge branch 'master' into eu-energy-security

doc/release_notes.rst

@@ -99,6 +99,10 @@ Upcoming Release
 
 * Use updated SARAH-2 and ERA5 cutouts with slightly wider scope to east and additional variables.
 
+* Fix crs bug. Change crs 4236 to 4326.
+
+* Update rasterio version to correctly calculate exclusion raster
+
 
 PyPSA-Eur 0.4.0 (22th September 2021)
 =====================================

doc/tutorial.rst

@@ -43,7 +43,7 @@ For more information on the data dependencies of PyPSA-Eur, continue reading :re
 How to customise PyPSA-Eur?
 ===========================
 
-The model can be adapted to only include selected countries (e.g. Germany) instead of all European countries to limit the spatial scope.
+The model can be adapted to only include selected countries (e.g. Belgium) instead of all European countries to limit the spatial scope.
 
 .. literalinclude:: ../config.tutorial.yaml
    :language: yaml

envs/environment.yaml

@@ -24,6 +24,7 @@ dependencies:
   - yaml
   - pytables
   - lxml
+  - powerplantmatching>=0.5.3
   - numpy
   - pandas
   - geopandas
@@ -45,7 +46,7 @@ dependencies:
   # GIS dependencies:
   - cartopy
   - descartes
-  - rasterio
+  - rasterio<=1.2.9  # 1.2.10 creates error https://github.com/PyPSA/atlite/issues/238
 
   # PyPSA-Eur-Sec Dependencies
   - geopy

scripts/add_electricity.py

@@ -94,7 +94,6 @@ import geopandas as gpd
 import powerplantmatching as pm
 from powerplantmatching.export import map_country_bus
 
-from vresutils.costdata import annuity
 from vresutils import transfer as vtransfer
 
 idx = pd.IndexSlice
@@ -105,6 +104,18 @@ logger = logging.getLogger(__name__)
 def normed(s): return s/s.sum()
 
 
+def calculate_annuity(n, r):
+    """Calculate the annuity factor for an asset with lifetime n years and
+    discount rate of r, e.g. annuity(20, 0.05) * 20 = 1.6"""
+
+    if isinstance(r, pd.Series):
+        return pd.Series(1/n, index=r.index).where(r == 0, r/(1. - 1./(1.+r)**n))
+    elif r > 0:
+        return r / (1. - 1./(1.+r)**n)
+    else:
+        return 1 / n
+
+
 def _add_missing_carriers_from_costs(n, costs, carriers):
     missing_carriers = pd.Index(carriers).difference(n.carriers.index)
     if missing_carriers.empty: return
@@ -138,7 +149,7 @@ def load_costs(tech_costs, config, elec_config, Nyears=1.):
                           "investment" : 0,
                           "lifetime" : 25})
 
-    costs["capital_cost"] = ((annuity(costs["lifetime"], costs["discount rate"]) +
+    costs["capital_cost"] = ((calculate_annuity(costs["lifetime"], costs["discount rate"]) +
                              costs["FOM"]/100.) *
                              costs["investment"] * Nyears)
 

scripts/build_renewable_profiles.py

@@ -240,7 +240,7 @@ if __name__ == '__main__':
         # use named function np.greater with partially frozen argument instead
         # and exclude areas where: -max_depth > grid cell depth
         func = functools.partial(np.greater,-config['max_depth'])
-        excluder.add_raster(snakemake.input.gebco, codes=func, crs=4236, nodata=-1000)
+        excluder.add_raster(snakemake.input.gebco, codes=func, crs=4326, nodata=-1000)
 
     if 'min_shore_distance' in config:
         buffer = config['min_shore_distance']

scripts/make_summary.py

@@ -171,6 +171,9 @@ def calculate_capacity(n,label,capacity):
         if 'p_nom_opt' in c.df.columns:
             c_capacities = abs(c.df.p_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
             capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
+        elif 'e_nom_opt' in c.df.columns:
+            c_capacities = abs(c.df.e_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
+            capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
 
     for c in n.iterate_components(n.passive_branch_components):
         c_capacities = c.df['s_nom_opt'].groupby(c.df.carrier).sum()
@@ -185,11 +188,11 @@ def calculate_capacity(n,label,capacity):
 def calculate_supply(n, label, supply):
     """calculate the max dispatch of each component at the buses where the loads are attached"""
 
-    load_types = n.loads.carrier.value_counts().index
+    load_types = n.buses.carrier.unique()
 
     for i in load_types:
 
-        buses = n.loads.bus[n.loads.carrier == i].values
+        buses = n.buses.query("carrier == @i").index
 
         bus_map = pd.Series(False,index=n.buses.index)
 
@@ -232,11 +235,11 @@ def calculate_supply(n, label, supply):
 def calculate_supply_energy(n, label, supply_energy):
     """calculate the total dispatch of each component at the buses where the loads are attached"""
 
-    load_types = n.loads.carrier.value_counts().index
+    load_types = n.buses.carrier.unique()
 
     for i in load_types:
 
-        buses = n.loads.bus[n.loads.carrier == i].values
+        buses = n.buses.query("carrier == @i").index
 
         bus_map = pd.Series(False,index=n.buses.index)