added pipeline to plot capacities

config/config.yaml

@@ -36,6 +36,14 @@ scenario:
       # LW: Leading the Way,
       # ST: System Transformation,
       # CT: Consumer Transformation,
+  planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
+  - 2035
+  # for example, set to
+  # - 2020
+  # - 2030
+  # - 2040
+  # - 2050
+  # for myopic foresight
   dt_event:
   - '1' # selects file for turndown timing and magnitude in data/turndown_events/
   opts: # only relevant for PyPSA-Eur
@@ -58,14 +66,6 @@ scenario:
   # planning_horizons), be:beta decay; ex:exponential decay
   # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
   # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
-  - 2035
-  # for example, set to
-  # - 2020
-  # - 2030
-  # - 2040
-  # - 2050
-  # for myopic foresight
 
 # here is a list of all possible countries: 
 # countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']
@@ -730,8 +730,10 @@ plotting:
   eu_node_location:
     x: -5.5
     y: 46.
-  costs_max: 1000
-  costs_threshold: 1
+  costs_max: 50
+  capacities_max: 1000
+  costs_threshold: 0
+  capacities_threshold: 0
   energy_max: 20000
   energy_min: -20000
   energy_threshold: 50.
@@ -874,6 +876,7 @@ plotting:
     electricity distribution grid: '#97ad8c'
     # electricity demand
     Electric load: '#110d63'
+    load: '#110d63'
     electric demand: '#110d63'
     electricity: '#110d63'
     industry electricity: '#2d2a66'
@@ -931,8 +934,10 @@ plotting:
     H2 pipeline: '#f081dc'
     H2 pipeline retrofitted: '#ba99b5'
     H2 Fuel Cell: '#c251ae'
+    H2 fuel cell: '#c251ae'
     H2 turbine: '#991f83'
     H2 Electrolysis: '#ff29d9'
+    H2 electrolysis: '#ff29d9'
     # ammonia
     NH3: '#46caf0'
     ammonia: '#46caf0'

rules/postprocess.smk

@@ -78,11 +78,11 @@ rule make_summary:
         costs="data/costs_{}.csv".format(config["costs"]["year"])
         if config["foresight"] == "overnight"
         else "data/costs_{}.csv".format(config["scenario"]["planning_horizons"][0]),
-        plots=expand(
-            RESULTS
-            + "maps/elec_s{simpl}_{gb_regions}_ec_l{ll}_{opts}_-costs-all_{fes_scenario}_{planning_horizons}.pdf",
-            **config["scenario"]
-        ),
+        # plots=expand(
+        #     RESULTS
+        #     + "maps/elec_s{simpl}_{gb_regions}_ec_l{ll}_{opts}_-costs-all_{fes_scenario}_{planning_horizons}.pdf",
+        #     **config["scenario"]
+        # ),
     output:
         nodal_costs=RESULTS + "csvs/nodal_costs.csv",
         nodal_capacities=RESULTS + "csvs/nodal_capacities.csv",
@@ -117,13 +117,16 @@ rule plot_summary:
         RDIR=RDIR,
     input:
         costs=RESULTS + "csvs/costs.csv",
+        nodal_costs=RESULTS + "csvs/nodal_costs.csv",
         energy=RESULTS + "csvs/energy.csv",
         balances=RESULTS + "csvs/supply_energy.csv",
+        nodal_capacities=RESULTS + "csvs/nodal_capacities.csv",
         # eurostat=input_eurostat,
     output:
         costs=RESULTS + "graphs/costs.pdf",
         energy=RESULTS + "graphs/energy.pdf",
         balances=RESULTS + "graphs/balances-energy.pdf",
+        capacities=RESULTS + "graphs/capacities.pdf",    
     threads: 2
     resources:
         mem_mb=10000,

scripts/make_summary.py

@@ -242,9 +242,35 @@ def calculate_nodal_capacities(n, label, nodal_capacities):
         index = pd.MultiIndex.from_tuples(
             [(c.list_name,) + t for t in nodal_capacities_c.index.to_list()]
         )
+
+        extendables = (
+            c.df.loc[getattr(
+                    c.df, opt_name.get(c.name, "p") + "_nom_extendable"
+                    )]
+            .groupby(["location", "carrier"])[
+            opt_name.get(c.name, "p") + "_nom_opt"
+        ].sum()
+        )
+
+        non_extendables = (
+            c.df.loc[~getattr(
+                    c.df, opt_name.get(c.name, "p") + "_nom_extendable"
+                    )]
+            .groupby(["location", "carrier"])[
+            opt_name.get(c.name, "p") + "_nom"
+        ].sum()
+        )
+
+        nodal_capacities_c = pd.concat((extendables, non_extendables))
+
+        index = pd.MultiIndex.from_tuples(
+                [(c.list_name,) + t for t in nodal_capacities_c.index.to_list()]
+            )
+
         nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
         nodal_capacities.loc[index, label] = nodal_capacities_c.values
 
+    nodal_capacities.drop("load", level=2, inplace=True)
     return nodal_capacities
 
 
@@ -654,7 +680,8 @@ def make_summaries(networks_dict):
     ]
 
     columns = pd.MultiIndex.from_tuples(
-        networks_dict.keys(), names=["cluster", "ll", "opt", "planning_horizon"]
+        networks_dict.keys(),
+        names=["gb_regions", "ll", "opt", "fes_scenario", "planning_horizon"]
     )
 
     df = {}

scripts/plot_summary.py

@@ -131,27 +131,36 @@ def rename_techs(label):
 
 
 def plot_costs():
-    cost_df = pd.read_csv(
-        snakemake.input.costs, index_col=list(range(3)), header=list(range(n_header))
+    nodal_cost_df = pd.read_csv(
+        snakemake.input.nodal_costs, index_col=list(range(4)), header=list(range(n_header))
     )
+    idx = pd.IndexSlice
+
+    gb_buses = nodal_cost_df.index.get_level_values(2).unique()
+    gb_buses = gb_buses[gb_buses.fillna("nan").str.contains("GB")]
 
-    df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()
+    df = nodal_cost_df.loc[idx[:, :, gb_buses, :]]
 
+    df = (
+        df
+        .groupby(df.index.get_level_values(3))
+        .sum()
+        .droplevel([0,1,2], axis=1)
+        .drop("load")
+    )
     # convert to billions
     df = df / 1e9
 
     df = df.groupby(df.index.map(rename_techs)).sum()
 
     to_drop = df.index[df.max(axis=1) < snakemake.config["plotting"]["costs_threshold"]]
-
     logger.info(
         f"Dropping technology with costs below {snakemake.config['plotting']['costs_threshold']} EUR billion per year"
     )
     logger.debug(df.loc[to_drop])
-
     df = df.drop(to_drop)
 
-    logger.info(f"Total system cost of {round(df.sum()[0])} EUR billion per year")
+    logger.info(f"Average total system cost of {round(df.sum().mean())} EUR billion per year")
 
     new_index = preferred_order.intersection(df.index).append(
         df.index.difference(preferred_order)
@@ -188,6 +197,66 @@ def plot_costs():
     fig.savefig(snakemake.output.costs, bbox_inches="tight")
 
 
+def plot_capacities():
+    nodal_caps_df = pd.read_csv(
+        snakemake.input.nodal_capacities, index_col=list(range(3)), header=list(range(n_header))
+    )
+    idx = pd.IndexSlice
+
+    gb_buses = nodal_caps_df.index.get_level_values(1).unique()
+    gb_buses = gb_buses[gb_buses.fillna("nan").str.contains("GB")]
+
+    df = nodal_caps_df.loc[idx[:, gb_buses, :]]
+    df.drop("stores", level=0, inplace=True)
+
+    df = (
+        df
+        .groupby(df.index.get_level_values(2))
+        .sum()
+        .droplevel([0,1,2], axis=1)
+    )
+    # convert to GW 
+    df = df / 1e3
+
+    df = df.groupby(df.index.map(rename_techs)).sum()
+
+    logger.warning("Dropping of techs not implemented for plot_capacities()")
+
+    new_index = preferred_order.intersection(df.index).append(
+        df.index.difference(preferred_order)
+    )
+
+    new_columns = df.sum().sort_values().index
+
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    df.loc[new_index, new_columns].T.plot(
+        kind="bar",
+        ax=ax,
+        stacked=True,
+        color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
+    )
+
+    handles, labels = ax.get_legend_handles_labels()
+
+    handles.reverse()
+    labels.reverse()
+
+    ax.set_ylim([0, snakemake.config["plotting"]["capacities_max"]])
+
+    ax.set_ylabel("Installed Capacity [GW]")
+
+    ax.set_xlabel("")
+
+    ax.grid(axis="x")
+
+    ax.legend(
+        handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False
+    )
+
+    fig.savefig(snakemake.output.capacities, bbox_inches="tight")
+
+
 def plot_energy():
     energy_df = pd.read_csv(
         snakemake.input.energy, index_col=list(range(2)), header=list(range(n_header))
@@ -547,7 +616,9 @@ def plot_carbon_budget_distribution(input_eurostat):
 
     logging.basicConfig(level=snakemake.config["logging"]["level"])
 
-    n_header = 4
+    n_header = 5
+
+    plot_capacities()    
 
     plot_costs()