Merge branch 'master' into scenario-management

doc/release_notes.rst

@@ -16,6 +16,8 @@ Upcoming Release
 
 * For industry distribution, use EPRTR as fallback if ETS data is not available.
 
+* The minimum capacity for renewable generators when using the myopic option has been fixed.
+
 PyPSA-Eur 0.8.1 (27th July 2023)
 ================================
 

rules/postprocess.smk

@@ -107,6 +107,8 @@ rule plot_summary:
         countries=config_provider("countries"),
         planning_horizons=config_provider("scenario", "planning_horizons"),
         sector_opts=config_provider("scenario", "sector_opts"),
+        emissions_scope=config_provider("energy", "emissions"),
+        eurostat_report_year=config_provider("energy", "eurostat_report_year"),
         plotting=config_provider("plotting"),
         RDIR=RDIR,
     input:
@@ -114,6 +116,7 @@ rule plot_summary:
         energy=RESULTS + "csvs/energy.csv",
         balances=RESULTS + "csvs/supply_energy.csv",
         eurostat=input_eurostat,
+        co2="data/eea/UNFCCC_v23.csv",
     output:
         costs=RESULTS + "graphs/costs.pdf",
         energy=RESULTS + "graphs/energy.pdf",

scripts/add_electricity.py

@@ -165,7 +165,7 @@ def sanitize_carriers(n, config):
     nice_names = (
         pd.Series(config["plotting"]["nice_names"])
         .reindex(carrier_i)
-        .fillna(carrier_i.to_series().str.title())
+        .fillna(carrier_i.to_series())
     )
     n.carriers["nice_name"] = n.carriers.nice_name.where(
         n.carriers.nice_name != "", nice_names

scripts/add_existing_baseyear.py

@@ -435,15 +435,20 @@ def add_heating_capacities_installed_before_baseyear(
 
     # split existing capacities between residential and services
     # proportional to energy demand
+    p_set_sum = n.loads_t.p_set.sum()
     ratio_residential = pd.Series(
         [
             (
-                n.loads_t.p_set.sum()[f"{node} residential rural heat"]
+                p_set_sum[f"{node} residential rural heat"]
                 / (
-                    n.loads_t.p_set.sum()[f"{node} residential rural heat"]
+                    p_set_sum[f"{node} residential rural heat"]
-                    + n.loads_t.p_set.sum()[f"{node} services rural heat"]
+                    + p_set_sum[f"{node} services rural heat"]
                 )
             )
+            # if rural heating demand for one of the nodes doesn't exist,
+            # then columns were dropped before and heating demand share should be 0.0
+            if all(f"{node} {service} rural heat" in p_set_sum.index for service in ["residential", "services"])
+            else 0.
             for node in nodal_df.index
         ],
         index=nodal_df.index,

scripts/build_industrial_distribution_key.py

@@ -17,6 +17,8 @@ import geopandas as gpd
 import pandas as pd
 from packaging.version import Version, parse
 
+import country_converter as coco
+cc = coco.CountryConverter()
 
 def locate_missing_industrial_sites(df):
     """
@@ -93,6 +95,17 @@ def prepare_hotmaps_database(regions):
     gdf.rename(columns={"index_right": "bus"}, inplace=True)
     gdf["country"] = gdf.bus.str[:2]
 
+    # the .sjoin can lead to duplicates if a geom is in two overlapping regions
+    if gdf.index.duplicated().any():
+        # get all duplicated entries
+        duplicated_i = gdf.index[gdf.index.duplicated()]
+        # convert from raw data country name to iso-2-code
+        code = cc.convert(gdf.loc[duplicated_i, "Country"], to="iso2")
+        # screen out malformed country allocation
+        gdf_filtered = gdf.loc[duplicated_i].query("country == @code")
+        # concat not duplicated and filtered gdf
+        gdf = pd.concat([gdf.drop(duplicated_i), gdf_filtered])
+
     return gdf
 
 

scripts/make_summary.py

@@ -711,5 +711,5 @@ if __name__ == "__main__":
     if snakemake.params.foresight == "myopic":
         cumulative_cost = calculate_cumulative_cost()
         cumulative_cost.to_csv(
-            "results/" + snakemake.params.RDIR + "/csvs/cumulative_cost.csv"
+            "results/" + snakemake.params.RDIR + "csvs/cumulative_cost.csv"
         )

scripts/plot_summary.py

@@ -387,6 +387,9 @@ def historical_emissions(countries):
         countries.remove("GB")
         countries.append("UK")
 
+    # remove countries which are not included in eea historical emission dataset
+    countries_to_remove = {"AL", "BA", "ME", "MK", "RS"}
+    countries = list(set(countries) - countries_to_remove)
     year = np.arange(1990, 2018).tolist()
 
     idx = pd.IndexSlice
@@ -457,9 +460,20 @@ def plot_carbon_budget_distribution(input_eurostat):
     ax1.set_ylim([0, 5])
     ax1.set_xlim([1990, snakemake.params.planning_horizons[-1] + 1])
 
-    path_cb = "results/" + snakemake.params.RDIR + "/csvs/"
+    path_cb = "results/" + snakemake.params.RDIR + "csvs/"
     countries = snakemake.params.countries
-    e_1990 = co2_emissions_year(countries, input_eurostat, opts, year=1990)
+    emissions_scope = snakemake.params.emissions_scope
+    report_year = snakemake.params.eurostat_report_year
+    input_co2 = snakemake.input.co2
+    e_1990 = co2_emissions_year(
+        countries,
+        input_eurostat,
+        opts,
+        emissions_scope,
+        report_year,
+        input_co2,
+        year=1990,
+    )
     CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)
 
     ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None)
@@ -535,7 +549,7 @@ def plot_carbon_budget_distribution(input_eurostat):
         fancybox=True, fontsize=18, loc=(0.01, 0.01), facecolor="white", frameon=True
     )
 
-    path_cb_plot = "results/" + snakemake.params.RDIR + "/graphs/"
+    path_cb_plot = "results/" + snakemake.params.RDIR + "graphs/"
     plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
 
 

scripts/prepare_sector_network.py

@@ -196,17 +196,15 @@ def get(item, investment_year=None):
 
 
 def co2_emissions_year(
-    countries, input_eurostat, opts, emissions_scope, report_year, year
+    countries, input_eurostat, opts, emissions_scope, report_year, input_co2, year
 ):
     """
     Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
     """
-    emissions_scope = snakemake.params.energy["emissions"]
+    eea_co2 = build_eea_co2(input_co2, year, emissions_scope)
-    eea_co2 = build_eea_co2(snakemake.input.co2, year, emissions_scope)
 
     # TODO: read Eurostat data from year > 2014
     # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
-    report_year = snakemake.params.energy["eurostat_report_year"]
     if year > 2014:
         eurostat_co2 = build_eurostat_co2(
             input_eurostat, countries, report_year, year=2014
@@ -227,7 +225,7 @@ def co2_emissions_year(
 
 
 # TODO: move to own rule with sector-opts wildcard?
-def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year):
+def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year, input_co2):
     """
     Distribute carbon budget following beta or exponential transition path.
     """
@@ -245,12 +243,24 @@ def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year):
     countries = snakemake.params.countries
 
     e_1990 = co2_emissions_year(
-        countries, input_eurostat, opts, emissions_scope, report_year, year=1990
+        countries,
+        input_eurostat,
+        opts,
+        emissions_scope,
+        report_year,
+        input_co2,
+        year=1990,
     )
 
     # emissions at the beginning of the path (last year available 2018)
     e_0 = co2_emissions_year(
-        countries, input_eurostat, opts, emissions_scope, report_year, year=2018
+        countries,
+        input_eurostat,
+        opts,
+        emissions_scope,
+        report_year,
+        input_co2,
+        year=2018,
     )
 
     planning_horizons = snakemake.params.planning_horizons
@@ -3398,12 +3408,18 @@ if __name__ == "__main__":
         if "cb" not in o:
             continue
         limit_type = "carbon budget"
-        fn = "results/" + snakemake.params.RDIR + "/csvs/carbon_budget_distribution.csv"
+        fn = "results/" + snakemake.params.RDIR + "csvs/carbon_budget_distribution.csv"
         if not os.path.exists(fn):
             emissions_scope = snakemake.params.emissions_scope
             report_year = snakemake.params.eurostat_report_year
+            input_co2 = snakemake.input.co2
             build_carbon_budget(
-                o, snakemake.input.eurostat, fn, emissions_scope, report_year
+                o,
+                snakemake.input.eurostat,
+                fn,
+                emissions_scope,
+                report_year,
+                input_co2,
             )
         co2_cap = pd.read_csv(fn, index_col=0).squeeze()
         limit = co2_cap.loc[investment_year]

scripts/solve_network.py

@@ -55,6 +55,9 @@ def _add_land_use_constraint(n):
     # warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
 
     for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
+        extendable_i = (n.generators.carrier == carrier) & n.generators.p_nom_extendable
+        n.generators.loc[extendable_i, "p_nom_min"] = 0
+
         ext_i = (n.generators.carrier == carrier) & ~n.generators.p_nom_extendable
         existing = (
             n.generators.loc[ext_i, "p_nom"]
@@ -71,7 +74,7 @@ def _add_land_use_constraint(n):
     if len(existing_large):
         logger.warning(
             f"Existing capacities larger than technical potential for {existing_large},\
-                       adjust technical potential to existing capacities"
+                        adjust technical potential to existing capacities"
         )
         n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
             existing_large, "p_nom_min"