Features/#663 update pypsa version

.github/workflows/ci.yaml

@@ -22,7 +22,6 @@ jobs:
         with:
           python-version: |
             3
-            3.8
             3.9
             3.10
             3.11

etrago/appl.py

@@ -679,6 +679,7 @@ def run_etrago(args, json_path):
 
     # spatial clustering
     etrago.spatial_clustering()
+
     etrago.spatial_clustering_gas()
 
     # snapshot clustering

etrago/cluster/electrical.py

@@ -27,9 +27,8 @@
     import logging
 
     from pypsa import Network
-    from pypsa.networkclustering import (
+    from pypsa.clustering.spatial import (
         aggregatebuses,
-        aggregategenerators,
         aggregateoneport,
         get_clustering_from_busmap,
     )
@@ -43,7 +42,9 @@
         group_links,
         kmean_clustering,
         kmedoids_dijkstra_clustering,
+        strategies_buses,
         strategies_generators,
+        strategies_lines,
         strategies_one_ports,
     )
     from etrago.tools.utilities import set_control_strategies
@@ -275,6 +276,8 @@ def cluster_on_extra_high_voltage(etrago, busmap, with_time=True):
         {
             "x": _leading(busmap, network.buses),
             "y": _leading(busmap, network.buses),
+            "geom": lambda x: np.nan,
+            "country": lambda x: "",
         },
     )
 
@@ -327,27 +330,20 @@ def cluster_on_extra_high_voltage(etrago, busmap, with_time=True):
                 io.import_series_from_dataframe(network_c, df, "Link", attr)
 
     # dealing with generators
-    network.generators["weight"] = 1
+    # network.generators["weight"] = 1
 
-    new_df, new_pnl = aggregategenerators(
-        network, busmap, with_time, custom_strategies=strategies_generators()
-    )
-    io.import_components_from_dataframe(network_c, new_df, "Generator")
-    for attr, df in iteritems(new_pnl):
-        io.import_series_from_dataframe(network_c, df, "Generator", attr)
-
-    # dealing with all other components
-    aggregate_one_ports = network.one_port_components.copy()
-    aggregate_one_ports.discard("Generator")
+    for one_port in network.one_port_components.copy():
+        if one_port == "Generator":
+            custom_strategies = strategies_generators()
 
-    for one_port in aggregate_one_ports:
-        one_port_strategies = strategies_one_ports()
+        else:
+            custom_strategies = strategies_one_ports().get(one_port, {})
         new_df, new_pnl = aggregateoneport(
             network,
             busmap,
             component=one_port,
             with_time=with_time,
-            custom_strategies=one_port_strategies.get(one_port, {}),
+            custom_strategies=custom_strategies,
         )
         io.import_components_from_dataframe(network_c, new_df, one_port)
         for attr, df in iteritems(new_pnl):
@@ -454,6 +450,13 @@ def ehv_clustering(self):
         self.update_busmap(busmap)
         self.buses_by_country()
 
+        # Drop nan values in timeseries after clustering
+        for c in self.network.iterate_components():
+            for pnl in c.attrs[
+                (c.attrs.status == "Output") & (c.attrs.varying)
+            ].index:
+                c.pnl[pnl] = pd.DataFrame(index=self.network.snapshots)
+
         logger.info("Network clustered to EHV-grid")
 
 
@@ -684,6 +687,7 @@ def preprocessing(etrago):
             [
                 "bus0",
                 "bus1",
+                "r",
                 "x",
                 "s_nom",
                 "capital_cost",
@@ -696,6 +700,7 @@ def preprocessing(etrago):
             x=network.transformers.x
             * (380.0 / transformer_voltages.max(axis=1)) ** 2,
             length=1,
+            v_nom=380.0,
         )
         .set_index("T" + trafo_index),
         "Line",
@@ -857,8 +862,22 @@ def postprocessing(etrago, busmap, busmap_foreign, medoid_idx=None):
         generator_strategies=strategies_generators(),
         aggregate_one_ports=aggregate_one_ports,
         line_length_factor=settings["line_length_factor"],
+        bus_strategies=strategies_buses(),
+        line_strategies=strategies_lines(),
     )
 
+    # Drop nan values after clustering
+    clustering.network.links.min_up_time.fillna(0, inplace=True)
+    clustering.network.links.min_down_time.fillna(0, inplace=True)
+    clustering.network.links.up_time_before.fillna(0, inplace=True)
+    clustering.network.links.down_time_before.fillna(0, inplace=True)
+    # Drop nan values in timeseries after clustering
+    for c in clustering.network.iterate_components():
+        for pnl in c.attrs[
+            (c.attrs.status == "Output") & (c.attrs.varying)
+        ].index:
+            c.pnl[pnl] = pd.DataFrame(index=clustering.network.snapshots)
+
     if method == "kmedoids-dijkstra":
         for i in clustering.network.buses[
             clustering.network.buses.carrier == "AC"
@@ -928,41 +947,12 @@ def calc_availability_factor(gen):
         cannot be found in the dictionary, it is assumed to be 1.
 
         """
-
-        if gen["carrier"] in time_dependent:
+        if gen.name in network.generators_t.p_max_pu.columns:
             cf = network.generators_t["p_max_pu"].loc[:, gen.name].mean()
         else:
-            try:
-                cf = fixed_capacity_fac[gen["carrier"]]
-            except KeyError:
-                cf = 1
-        return cf
+            cf = network.generators.loc[gen.name, "p_max_pu"]
 
-    time_dependent = [
-        "solar_rooftop",
-        "solar",
-        "wind_onshore",
-        "wind_offshore",
-    ]
-    fixed_capacity_fac = {
-        # A value of 1 is given to power plants where its availability
-        # does not depend on the weather
-        "industrial_gas_CHP": 1,
-        "industrial_biomass_CHP": 1,
-        "biomass": 1,
-        "central_biomass_CHP": 1,
-        "central_gas_CHP": 1,
-        "OCGT": 1,
-        "other_non_renewable": 1,
-        "run_of_river": 0.50,
-        "reservoir": 1,
-        "gas": 1,
-        "oil": 1,
-        "others": 1,
-        "coal": 1,
-        "lignite": 1,
-        "nuclear": 1,
-    }
+        return cf
 
     gen = network.generators[network.generators.carrier != "load shedding"][
         ["bus", "carrier", "p_nom"]

etrago/cluster/gas.py

@@ -26,12 +26,12 @@
 if "READTHEDOCS" not in os.environ:
     import logging
 
-    from pypsa import Network
-    from pypsa.networkclustering import (
+    from pypsa.clustering.spatial import (
         aggregatebuses,
         aggregateoneport,
         busmap_by_kmeans,
     )
+    from pypsa.components import Network
     from six import iteritems
     import numpy as np
     import pandas as pd
@@ -84,10 +84,15 @@ def preprocessing(etrago):
     # Create network_ch4 (grid nodes in order to create the busmap basis)
     network_ch4 = Network()
 
-    buses_ch4 = etrago.network.buses
-    links_ch4 = etrago.network.links
+    buses_ch4 = etrago.network.buses.copy()
+    links_ch4 = etrago.network.links.copy()
+
     io.import_components_from_dataframe(network_ch4, buses_ch4, "Bus")
-    io.import_components_from_dataframe(network_ch4, links_ch4, "Link")
+    network_ch4.madd(
+        "Link", links_ch4.index, **links_ch4.loc[:, ~links_ch4.isna().any()]
+    )
+
+    network_ch4.buses["country"] = buses_ch4.country
 
     # Cluster ch4 buses
     settings = etrago.args["network_clustering"]
@@ -482,6 +487,7 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None):
         network_gasgrid_c, etrago.network.carriers, "Carrier"
     )
 
+    network_gasgrid_c.consistency_check()
     network_gasgrid_c.determine_network_topology()
 
     # Adjust x and y coordinates of 'CH4' and 'H2_grid' medoids
@@ -493,32 +499,34 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None):
             if cluster in busmap[medoid_idx].values:
                 medoid = busmap[medoid_idx][
                     busmap[medoid_idx] == cluster
-                ].index
+                ].index[0]
                 h2_idx = network_gasgrid_c.buses.loc[
                     (network_gasgrid_c.buses.carrier == "H2_grid")
                     & (
                         network_gasgrid_c.buses.y
-                        == network_gasgrid_c.buses.at[i, "y"]
+                        == network_gasgrid_c.buses.loc[i, "y"]
                     )
                     & (
                         network_gasgrid_c.buses.x
-                        == network_gasgrid_c.buses.at[i, "x"]
+                        == network_gasgrid_c.buses.loc[i, "x"]
                     )
                 ]
                 if len(h2_idx) > 0:
                     h2_idx = h2_idx.index.tolist()[0]
-                    network_gasgrid_c.buses.at[
+                    network_gasgrid_c.buses.loc[
                         h2_idx, "x"
                     ] = etrago.network.buses["x"].loc[medoid]
-                    network_gasgrid_c.buses.at[
+                    network_gasgrid_c.buses.loc[
                         h2_idx, "y"
                     ] = etrago.network.buses["y"].loc[medoid]
-                network_gasgrid_c.buses.at[i, "x"] = etrago.network.buses[
-                    "x"
-                ].loc[medoid]
-                network_gasgrid_c.buses.at[i, "y"] = etrago.network.buses[
-                    "y"
-                ].loc[medoid]
+
+                network_gasgrid_c.buses.loc[i, "x"] = etrago.network.buses.loc[
+                    medoid, "x"
+                ]
+                network_gasgrid_c.buses.loc[i, "y"] = etrago.network.buses.loc[
+                    medoid, "y"
+                ]
+
     return (network_gasgrid_c, busmap)
 
 
@@ -859,6 +867,7 @@ def get_clustering_from_busmap(
         A new gas grid pypsa.Network object with aggregated components based
         on the bus mapping.
     """
+
     network_gasgrid_c = Network()
 
     # Aggregate buses
@@ -919,14 +928,17 @@ def get_clustering_from_busmap(
 
     # import the links and the respective time series with the bus0 and bus1
     # values updated from the busmap
-    io.import_components_from_dataframe(network_gasgrid_c, new_links, "Link")
+    io.import_components_from_dataframe(
+        network_gasgrid_c, new_links.loc[:, ~new_links.isna().all()], "Link"
+    )
 
     if with_time:
         for attr, df in network.links_t.items():
             if not df.empty:
                 io.import_series_from_dataframe(
                     network_gasgrid_c, df, "Link", attr
                 )
+
     return network_gasgrid_c
 
 

etrago/cluster/spatial.py

@@ -29,10 +29,10 @@
     import multiprocessing as mp
 
     from networkx import NetworkXNoPath
-    from pypsa.networkclustering import (
-        _flatten_multiindex,
+    from pypsa.clustering.spatial import (
         busmap_by_kmeans,
         busmap_by_stubs,
+        flatten_multiindex,
         get_clustering_from_busmap,
     )
     from sklearn.cluster import KMeans
@@ -108,6 +108,18 @@ def sum_with_inf(x):
         return x.sum()
 
 
+def strategies_buses():
+    return {
+        "geom": nan_links,
+    }
+
+
+def strategies_lines():
+    return {
+        "geom": nan_links,
+    }
+
+
 def strategies_one_ports():
     return {
         "StorageUnit": {
@@ -128,6 +140,8 @@ def strategies_one_ports():
             "e_nom_min": np.sum,
             "e_nom_max": sum_with_inf,
             "e_initial": np.sum,
+            "e_min_pu": np.mean,
+            "e_max_pu": np.mean,
         },
     }
 
@@ -170,6 +184,11 @@ def strategies_links():
         "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,
     }
 
 
@@ -235,8 +254,11 @@ def arrange_dc_bus0_bus1(network):
     )
     strategies = strategies_links()
     strategies.update(cus_strateg)
+    strategies.pop("topo")
+    strategies.pop("geom")
+
     new_df = links.groupby(grouper, axis=0).agg(strategies)
-    new_df.index = _flatten_multiindex(new_df.index).rename("name")
+    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
     )
@@ -256,7 +278,7 @@ def arrange_dc_bus0_bus1(network):
                         weighting.loc[df_agg.columns], axis=1
                     )
                 pnl_df = df_agg.groupby(grouper, axis=1).sum()
-                pnl_df.columns = _flatten_multiindex(pnl_df.columns).rename(
+                pnl_df.columns = flatten_multiindex(pnl_df.columns).rename(
                     "name"
                 )
                 new_pnl[attr] = pd.concat(