add sanitize_locations function and apply it

address some deprecation warnings

config/test/config.perfect.yaml

@@ -18,7 +18,7 @@ scenario:
   clusters:
   - 5
   sector_opts:
-  - 8760H-T-H-B-I-A-dist1
+  - 8760h-T-H-B-I-A-dist1
   planning_horizons:
   - 2030
   - 2040

scripts/add_electricity.py

@@ -178,6 +178,15 @@ def sanitize_carriers(n, config):
     n.carriers["color"] = n.carriers.color.where(n.carriers.color != "", colors)
 
 
+def sanitize_locations(n):
+    n.buses["x"] = n.buses.x.where(n.buses.x != 0, n.buses.location.map(n.buses.x))
+    n.buses["y"] = n.buses.y.where(n.buses.y != 0, n.buses.location.map(n.buses.y))
+    n.buses["country"] = n.buses.country.where(
+        n.buses.country.ne("") & n.buses.country.notnull(),
+        n.buses.location.map(n.buses.country),
+    )
+
+
 def add_co2_emissions(n, costs, carriers):
     """
     Add CO2 emissions to the network's carriers attribute.

scripts/add_extra_components.py

@@ -56,7 +56,7 @@ import numpy as np
 import pandas as pd
 import pypsa
 from _helpers import configure_logging
-from add_electricity import load_costs, sanitize_carriers
+from add_electricity import load_costs, sanitize_carriers, sanitize_locations
 
 idx = pd.IndexSlice
 
@@ -100,10 +100,9 @@ def attach_stores(n, costs, extendable_carriers):
     n.madd("Carrier", carriers)
 
     buses_i = n.buses.index
-    bus_sub_dict = {k: n.buses[k].values for k in ["x", "y", "country"]}
 
     if "H2" in carriers:
-        h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", **bus_sub_dict)
+        h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", location=buses_i)
 
         n.madd(
             "Store",
@@ -143,7 +142,7 @@ def attach_stores(n, costs, extendable_carriers):
 
     if "battery" in carriers:
         b_buses_i = n.madd(
-            "Bus", buses_i + " battery", carrier="battery", **bus_sub_dict
+            "Bus", buses_i + " battery", carrier="battery", location=buses_i
         )
 
         n.madd(
@@ -246,6 +245,7 @@ if __name__ == "__main__":
     attach_hydrogen_pipelines(n, costs, extendable_carriers)
 
     sanitize_carriers(n, snakemake.config)
+    sanitize_locations(n)
 
     n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output[0])

scripts/make_summary.py

@@ -507,7 +507,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
         if carrier in ["H2", "gas"]:
             load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.0)
         else:
-            load = n.loads_t.p_set[buses]
+            load = n.loads_t.p_set[buses.intersection(n.loads.index)]
 
         for tech in value:
             names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
@@ -560,7 +560,10 @@ def calculate_market_values(n, label, market_values):
         )
         revenue = dispatch * n.buses_t.marginal_price[buses]
 
-        market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
+        if total_dispatch := dispatch.sum().sum():
+            market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
+        else:
+            market_values.at[tech, label] = np.nan
 
     ## Now do market value of links ##
 
@@ -583,7 +586,10 @@ def calculate_market_values(n, label, market_values):
 
             revenue = dispatch * n.buses_t.marginal_price[buses]
 
-            market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
+            if total_dispatch := dispatch.sum().sum():
+                market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
+            else:
+                market_values.at[tech, label] = np.nan
 
     return market_values
 

scripts/make_summary_perfect.py

@@ -265,7 +265,7 @@ def calculate_energy(n, label, energy):
                 totals[no_bus] = float(
                     n.component_attrs[c.name].loc["p" + port, "default"]
                 )
-                c_energies -= totals.groupby(c.df.carrier, axis=1).sum()
+                c_energies -= totals.T.groupby(c.df.carrier).sum().T
 
         c_energies = pd.concat([c_energies.T], keys=[c.list_name])
 
@@ -376,9 +376,8 @@ def calculate_supply_energy(n, label, supply_energy):
                 .groupby(level=0)
                 .sum()
                 .multiply(c.df.loc[items, "sign"])
-                .groupby(c.df.loc[items, "carrier"], axis=1)
+                .T.groupby(c.df.loc[items, "carrier"])
                 .sum()
-                .T
             )
             s = pd.concat([s], keys=[c.list_name])
             s = pd.concat([s], keys=[i])
@@ -525,9 +524,12 @@ def calculate_weighted_prices(n, label, weighted_prices):
         #    stores[stores > 0.] = 0.
         #    load += -stores
 
+        if total_load := load.sum().sum():
             weighted_prices.loc[carrier, label] = (
                 load * n.buses_t.marginal_price[buses]
-        ).sum().sum() / load.sum().sum()
+            ).sum().sum() / total_load
+        else:
+            weighted_prices.loc[carrier, label] = np.nan
 
         if carrier[:5] == "space":
             print(load * n.buses_t.marginal_price[buses])
@@ -562,7 +564,10 @@ def calculate_market_values(n, label, market_values):
 
         revenue = dispatch * n.buses_t.marginal_price[buses]
 
-        market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
+        if total_dispatch := dispatch.sum().sum():
+            market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
+        else:
+            market_values.at[tech, label] = np.nan
 
     ## Now do market value of links ##
 
@@ -585,7 +590,10 @@ def calculate_market_values(n, label, market_values):
 
             revenue = dispatch * n.buses_t.marginal_price[buses]
 
-            market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
+            if total_dispatch := dispatch.sum().sum():
+                market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
+            else:
+                market_values.at[tech, label] = np.nan
 
     return market_values
 

scripts/plot_hydrogen_network.py

@@ -36,7 +36,9 @@ def group_pipes(df, drop_direction=False):
         lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
         axis=1,
     )
-    return df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
+    return df.groupby(level=0).agg(
+        {"p_nom_opt": "sum", "bus0": "first", "bus1": "first"}
+    )
 
 
 def plot_h2_map(n, regions):

scripts/prepare_perfect_foresight.py

@@ -221,7 +221,7 @@ def concat_networks(years):
     # set investment periods
     n.investment_periods = n.snapshots.levels[0]
     # weighting of the investment period -> assuming last period same weighting as the period before
-    time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill")
+    time_w = n.investment_periods.to_series().diff().shift(-1).ffill()
     n.investment_period_weightings["years"] = time_w
     # set objective weightings
     objective_w = get_investment_weighting(

scripts/prepare_sector_network.py

@@ -19,7 +19,7 @@ import pandas as pd
 import pypsa
 import xarray as xr
 from _helpers import update_config_with_sector_opts
-from add_electricity import calculate_annuity, sanitize_carriers
+from add_electricity import calculate_annuity, sanitize_carriers, sanitize_locations
 from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
 from networkx.algorithms import complement
 from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
@@ -546,6 +546,14 @@ def patch_electricity_network(n):
     n.loads_t.p_set.rename(lambda x: x.strip(), axis=1, inplace=True)
 
 
+def add_eu_bus(n, x=-5.5, y=46):
+    """
+    Add EU bus to the network.
+    """
+    n.add("Bus", "EU", location="EU", x=x, y=y, carrier="none")
+    n.add("Carrier", "none")
+
+
 def add_co2_tracking(n, costs, options):
     # minus sign because opposite to how fossil fuels used:
     # CH4 burning puts CH4 down, atmosphere up
@@ -1005,6 +1013,7 @@ def insert_electricity_distribution_grid(n, costs):
         "Store",
         nodes + " home battery",
         bus=nodes + " home battery",
+        location=nodes,
         e_cyclic=True,
         e_nom_extendable=True,
         carrier="home battery",
@@ -3527,8 +3536,8 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}):
     rev_links["reversed"] = True
     rev_links.index = rev_links.index.map(lambda x: x + "-reversed")
 
+    n.links["reversed"] = False
     n.links = pd.concat([n.links, rev_links], sort=False)
-    n.links["reversed"] = n.links["reversed"].fillna(False)
     n.links["length_original"] = n.links["length_original"].fillna(n.links.length)
 
     # do compression losses after concatenation to take electricity consumption at bus0 in either direction
@@ -3594,6 +3603,8 @@ if __name__ == "__main__":
         for carrier in conventional:
             add_carrier_buses(n, carrier)
 
+    add_eu_bus(n)
+
     add_co2_tracking(n, costs, options)
 
     add_generation(n, costs)
@@ -3733,5 +3744,6 @@ if __name__ == "__main__":
     n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
 
     sanitize_carriers(n, snakemake.config)
+    sanitize_locations(n)
 
     n.export_to_netcdf(snakemake.output[0])