Add calculate_nodal_supply_energy in make summary

doc/release_notes.rst

@@ -181,6 +181,8 @@ Upcoming Release
 
 * Fix custom busmap read in `cluster_network`.
 
+* Add `nodal_supply_energy` to `make_summary`.
+
 PyPSA-Eur 0.10.0 (19th February 2024)
 =====================================
 

rules/postprocess.smk

@@ -199,6 +199,7 @@ rule make_summary:
         energy=RESULTS + "csvs/energy.csv",
         supply=RESULTS + "csvs/supply.csv",
         supply_energy=RESULTS + "csvs/supply_energy.csv",
+        nodal_supply_energy=RESULTS + "csvs/nodal_supply_energy.csv",
         prices=RESULTS + "csvs/prices.csv",
         weighted_prices=RESULTS + "csvs/weighted_prices.csv",
         market_values=RESULTS + "csvs/market_values.csv",

scripts/make_summary.py

@@ -413,6 +413,76 @@ def calculate_supply_energy(n, label, supply_energy):
     return supply_energy
 
 
+def calculate_nodal_supply_energy(n, label, nodal_supply_energy):
+    """
+    Calculate the total energy supply/consumption of each component at the buses
+    aggregated by carrier and node.
+    """
+
+    bus_carriers = n.buses.carrier.unique()
+
+    for i in bus_carriers:
+        bus_map = n.buses.carrier == i
+        bus_map.at[""] = False
+
+        for c in n.iterate_components(n.one_port_components):
+            items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
+
+            if len(items) == 0:
+                continue
+
+            s = (
+                pd.concat([
+                    (
+                        c.pnl.p[items]
+                        .multiply(n.snapshot_weightings.generators, axis=0)
+                        .sum()
+                        .multiply(c.df.loc[items, "sign"])
+                    ),
+                    c.df.loc[items][["bus", "carrier"]]
+                ], axis=1)
+                .groupby(by=["bus", "carrier"])
+                .sum()[0]
+            )
+            s = pd.concat([s], keys=[c.list_name])
+            s = pd.concat([s], keys=[i])
+
+            nodal_supply_energy = nodal_supply_energy.reindex(s.index.union(nodal_supply_energy.index))
+            nodal_supply_energy.loc[s.index, label] = s
+
+        for c in n.iterate_components(n.branch_components):
+            for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
+                items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
+
+                if (len(items) == 0) or c.pnl["p" + end].empty:
+                    continue
+
+                s = (
+                    pd.concat([
+                        (
+                            (-1) * c.pnl["p" + end][items]
+                            .multiply(n.snapshot_weightings.generators, axis=0)
+                            .sum()
+                        ),
+                        c.df.loc[items][["bus0", "carrier"]]
+                    ], axis=1)
+                    .groupby(by=["bus0", "carrier"])
+                    .sum()[0]
+                )
+
+                s.index = s.index.map(lambda x: (x[0], x[1] + end))
+                s = pd.concat([s], keys=[c.list_name])
+                s = pd.concat([s], keys=[i])
+
+                nodal_supply_energy = nodal_supply_energy.reindex(
+                    s.index.union(nodal_supply_energy.index)
+                )
+
+                nodal_supply_energy.loc[s.index, label] = s
+
+    return nodal_supply_energy
+
+
 def calculate_metrics(n, label, metrics):
     metrics_list = [
         "line_volume",
@@ -637,6 +707,7 @@ def make_summaries(networks_dict):
         "energy",
         "supply",
         "supply_energy",
+        "nodal_supply_energy",
         "prices",
         "weighted_prices",
         "price_statistics",