Merge branch 'dev' into features/#716-electrolysis-for-redispatch

etrago/analyze/plot.py

@@ -841,16 +841,18 @@ def calc_dispatch_per_carrier(network, timesteps, dispatch_type="total"):
     ]
 
     dist = pd.Series(
-        index=pd.MultiIndex.from_tuples(index, names=["bus", "carrier"]),
+        index=pd.MultiIndex.from_tuples(
+            index, names=["bus", "carrier"]
+        ).unique(),
         dtype=float,
-    )
+    ).sort_index()
 
     for i in dist.index:
         gens = network.generators[
             (network.generators.bus == i[0])
             & (network.generators.carrier == i[1])
         ].index
-        dist[i] = (
+        dist.loc[i] = (
             (
                 network.generators_t.p[gens].transpose()[
                     network.snapshots[timesteps]

etrago/cluster/gas.py

@@ -417,20 +417,20 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None):
         },
         one_port_strategies={
             "Generator": {
-                "marginal_cost": np.mean,
-                "capital_cost": np.mean,
-                "p_nom_max": np.sum,
-                "p_nom_min": np.sum,
-                "e_nom_max": np.sum,
+                "marginal_cost": "mean",
+                "capital_cost": "mean",
+                "p_nom_max": "sum",
+                "p_nom_min": "sum",
+                "e_nom_max": "sum",
             },
             "Store": {
-                "marginal_cost": np.mean,
-                "capital_cost": np.mean,
-                "e_nom": np.sum,
+                "marginal_cost": "mean",
+                "capital_cost": "mean",
+                "e_nom": "sum",
                 "e_nom_max": sum_with_inf,
             },
             "Load": {
-                "p_set": np.sum,
+                "p_set": "sum",
             },
         },
     )

etrago/cluster/spatial.py

@@ -121,38 +121,38 @@ def strategies_lines():
 def strategies_one_ports():
     return {
         "StorageUnit": {
-            "marginal_cost": np.mean,
-            "capital_cost": np.mean,
-            "efficiency_dispatch": np.mean,
-            "standing_loss": np.mean,
-            "efficiency_store": np.mean,
-            "p_min_pu": np.min,
+            "marginal_cost": "mean",
+            "capital_cost": "mean",
+            "efficiency_dispatch": "mean",
+            "standing_loss": "mean",
+            "efficiency_store": "mean",
+            "p_min_pu": "min",
             "p_nom_extendable": ext_storage,
             "p_nom_max": sum_with_inf,
         },
         "Store": {
-            "marginal_cost": np.mean,
-            "capital_cost": np.mean,
-            "standing_loss": np.mean,
-            "e_nom": np.sum,
-            "e_nom_min": np.sum,
+            "marginal_cost": "mean",
+            "capital_cost": "mean",
+            "standing_loss": "mean",
+            "e_nom": "sum",
+            "e_nom_min": "sum",
             "e_nom_max": sum_with_inf,
-            "e_initial": np.sum,
-            "e_min_pu": np.mean,
-            "e_max_pu": np.mean,
+            "e_initial": "sum",
+            "e_min_pu": "mean",
+            "e_max_pu": "mean",
         },
     }
 
 
 def strategies_generators():
     return {
-        "p_nom_min": np.min,
+        "p_nom_min": "min",
         "p_nom_max": sum_with_inf,
-        "weight": np.sum,
-        "p_nom": np.sum,
-        "p_nom_opt": np.sum,
-        "marginal_cost": np.mean,
-        "capital_cost": np.mean,
+        "weight": "sum",
+        "p_nom": "sum",
+        "p_nom_opt": "sum",
+        "marginal_cost": "mean",
+        "capital_cost": "mean",
         "e_nom_max": sum_with_inf,
     }
 
@@ -163,30 +163,30 @@ def strategies_links():
         "bus0": _make_consense_links,
         "bus1": _make_consense_links,
         "carrier": _make_consense_links,
-        "p_nom": np.sum,
+        "p_nom": "sum",
         "p_nom_extendable": _make_consense_links,
         "p_nom_max": sum_with_inf,
-        "capital_cost": np.mean,
-        "length": np.mean,
+        "capital_cost": "mean",
+        "length": "mean",
         "geom": nan_links,
         "topo": nan_links,
         "type": nan_links,
-        "efficiency": np.mean,
-        "p_nom_min": np.sum,
-        "p_set": np.mean,
-        "p_min_pu": np.min,
-        "p_max_pu": np.max,
-        "marginal_cost": np.mean,
+        "efficiency": "mean",
+        "p_nom_min": "sum",
+        "p_set": "mean",
+        "p_min_pu": "min",
+        "p_max_pu": "max",
+        "marginal_cost": "mean",
         "terrain_factor": _make_consense_links,
-        "p_nom_opt": np.mean,
+        "p_nom_opt": "mean",
         "country": nan_links,
-        "build_year": np.mean,
-        "lifetime": np.mean,
-        "min_up_time": np.mean,
-        "min_down_time": np.mean,
-        "up_time_before": np.mean,
-        "down_time_before": np.mean,
-        "committable": np.all,
+        "build_year": "mean",
+        "lifetime": "mean",
+        "min_up_time": "mean",
+        "min_down_time": "mean",
+        "up_time_before": "mean",
+        "down_time_before": "mean",
+        "committable": "all",
     }
 
 
@@ -255,7 +255,7 @@ def arrange_dc_bus0_bus1(network):
     strategies.pop("topo")
     strategies.pop("geom")
 
-    new_df = links.groupby(grouper, axis=0).agg(strategies)
+    new_df = links.groupby(grouper).agg(strategies)
     new_df.index = flatten_multiindex(new_df.index).rename("name")
     new_df = pd.concat(
         [new_df, network.links.loc[~links_agg_b]], axis=0, sort=False
@@ -275,7 +275,7 @@ def arrange_dc_bus0_bus1(network):
                     df_agg = df_agg.multiply(
                         weighting.loc[df_agg.columns], axis=1
                     )
-                pnl_df = df_agg.groupby(grouper, axis=1).sum()
+                pnl_df = df_agg.T.groupby(grouper).sum().T
                 pnl_df.columns = flatten_multiindex(pnl_df.columns).rename(
                     "name"
                 )
@@ -759,7 +759,7 @@ def kmedoids_dijkstra_clustering(
         kmeans.fit(points)
 
         busmap = pd.Series(
-            data=kmeans.predict(buses.loc[buses_i, ["x", "y"]]),
+            data=kmeans.predict(buses.loc[buses_i, ["x", "y"]].values),
             index=buses_i,
             dtype=object,
         )

etrago/execute/market_optimization.py

@@ -209,7 +209,6 @@ def optimize_with_rolling_horizon(
             n.storage_units.state_of_charge_initial = (
                 n.storage_units_t.state_of_charge.loc[snapshots[start - 1]]
             )
-            print(i)
             # Make sure that state of charge of batteries and pumped hydro
             # plants are cyclic over the year by using the state_of_charges
             # from the pre_market_model
@@ -302,7 +301,7 @@ def build_market_model(self):
 
     logger.info("Start market zone specifc clustering")
 
-    self.clustering, busmap = postprocessing(
+    clustering, busmap = postprocessing(
         self,
         busmap,
         busmap_foreign,
@@ -312,9 +311,7 @@ def build_market_model(self):
         apply_on="market_model",
     )
 
-    self.update_busmap(busmap)
-
-    net = self.clustering.network
+    net = clustering.network
     # links_col = net.links.columns
     ac = net.lines[net.lines.carrier == "AC"]
     str1 = "transshipment_"

etrago/tools/utilities.py

@@ -24,10 +24,13 @@
 
 from collections.abc import Mapping
 from copy import deepcopy
+from pathlib import Path
+from urllib.request import urlretrieve
 import json
 import logging
 import math
 import os
+import zipfile
 
 from pyomo.environ import Constraint, PositiveReals, Var
 import numpy as np
@@ -287,9 +290,27 @@ def buses_by_country(self, apply_on="grid_model"):
     con = self.engine
     germany_sh = gpd.read_postgis(query, con, geom_col="geometry")
 
-    path = gpd.datasets.get_path("naturalearth_lowres")
-    shapes = gpd.read_file(path)
-    shapes = shapes[shapes.name.isin([*countries])].set_index(keys="name")
+    # read Europe borders. Original data downloaded from naturalearthdata.com/
+    # under Public Domain license
+    path_countries = Path(".") / "data" / "shapes_europe"
+
+    if not os.path.exists(path_countries):
+        path_countries.mkdir(exist_ok=True, parents=True)
+        url_countries = (
+            "https://naciscdn.org/naturalearth/110m/cultural/"
+            + "ne_110m_admin_0_countries.zip"
+        )
+        urlretrieve(url_countries, path_countries / "shape_countries.zip")
+        with zipfile.ZipFile(
+            path_countries / "shape_countries.zip", "r"
+        ) as zip_ref:
+            zip_ref.extractall(path_countries)
+
+    shapes = (
+        gpd.read_file(path_countries)
+        .rename(columns={"NAME": "name"})
+        .set_index("name")
+    )
 
     # Use Germany borders from egon-data if not using the SH test case
     if len(germany_sh.gen.unique()) > 1: