merge master

Author: fneum

Snakefile

@@ -33,7 +33,6 @@ BENCHMARKS = "benchmarks/" + RDIR
 RESOURCES = "resources/" + RDIR if not run.get("shared_resources") else "resources/"
 RESULTS = "results/" + RDIR
 
-
 localrules:
     purge,
 

rules/plot.smk

@@ -52,6 +52,8 @@ rule plot_renewable_potential_unclustered:
     output:
         wind=multiext(RESOURCES + "graphics/wind-energy-density", ".png", ".pdf"),
         solar=multiext(RESOURCES + "graphics/solar-energy-density", ".png", ".pdf"),
+        wind_cf=multiext(RESOURCES + "graphics/wind-capacity-factor", ".png", ".pdf"),
+        solar_cf=multiext(RESOURCES + "graphics/solar-capacity-factor", ".png", ".pdf"),
     script:
         "../scripts/plot_renewable_potential_unclustered.py"
 

scripts/plot_renewable_potential_unclustered.py

@@ -13,6 +13,29 @@
 import pandas as pd
 import xarray as xr
 
+def plot_map(regions, color, cmap, label, vmin=None, vmax=None):
+
+    proj = ccrs.EqualEarth()
+    regions = regions.to_crs(proj.proj4_init)
+    fig, ax = plt.subplots(figsize=(7, 7), subplot_kw={"projection": proj})
+    regions.plot(
+        ax=ax,
+        column=color,
+        cmap=cmap,
+        linewidths=0,
+        legend=True,
+        vmin=vmin,
+        vmax=vmax,
+        legend_kwds={"label": label, "shrink": 0.8},
+    )
+    ax.add_feature(cartopy.feature.COASTLINE.with_scale("50m"), linewidth=0.5, zorder=4)
+    ax.add_feature(cartopy.feature.BORDERS.with_scale("50m"), linewidth=0.5, zorder=2)
+    ax.gridlines(linestyle=":")
+    ax.axis("off")
+
+    return fig, ax
+
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
@@ -41,49 +64,40 @@
     wind = wind.groupby(level=0).sum().reindex(regions.index, fill_value=0)
     wind_per_skm = wind / regions.Area / 1e3  # GWh
 
-    proj = ccrs.EqualEarth()
-    regions = regions.to_crs(proj.proj4_init)
-    fig, ax = plt.subplots(figsize=(7, 7), subplot_kw={"projection": proj})
-    regions.plot(
-        ax=ax,
-        column=wind_per_skm,
-        cmap="Blues",
-        linewidths=0,
-        legend=True,
-        legend_kwds={"label": r"Wind Energy Potential [GWh/a/km$^2$]", "shrink": 0.8},
-    )
-    ax.add_feature(cartopy.feature.COASTLINE.with_scale("50m"), linewidth=0.5, zorder=4)
-    ax.add_feature(cartopy.feature.BORDERS.with_scale("50m"), linewidth=0.5, zorder=2)
-    ax.gridlines(linestyle=":")
-    ax.axis("off")
+    fig, ax = plot_map(regions, wind_per_skm, "Blues", r"Wind Energy Potential [GWh/a/km$^2$]")
 
     for fn in snakemake.output["wind"]:
         plt.savefig(fn)
 
+    cf = pd.Series()
+    for profile in ["onwind", "offwind-ac", "offwind-dc"]:
+        ds = xr.open_dataset(snakemake.input[f"profile_{profile}"])
+        cf = pd.concat([cf, (ds.profile.mean("time") * 100).to_pandas()])
+    cf = cf.groupby(level=0).mean().reindex(regions.index, fill_value=0.)
+
+    fig, ax = plot_map(regions, cf, "Blues", r"Wind Capacity Factor [%]", vmin=0, vmax=60)
+
+    for fn in snakemake.output["wind_cf"]:
+        plt.savefig(fn)
+
     onregions = gpd.read_file(snakemake.input.regions_onshore).set_index("name")
     onregions["Area"] = onregions.to_crs(epsg=3035).area.div(1e6)
 
     ds = xr.open_dataset(snakemake.input.profile_solar)
     solar = (ds.p_nom_max * ds.profile.sum("time")).to_pandas()
 
-    solar = solar.groupby(level=0).sum().reindex(onregions.index, fill_value=0)
+    solar = solar.groupby(level=0).sum().reindex(onregions.index, fill_value=0.)
     solar_per_skm = solar / onregions.Area / 1e3  # GWh
 
-    proj = ccrs.EqualEarth()
-    onregions = onregions.to_crs(proj.proj4_init)
-    fig, ax = plt.subplots(figsize=(7, 7), subplot_kw={"projection": proj})
-    onregions.plot(
-        ax=ax,
-        column=solar_per_skm,
-        cmap="Reds",
-        linewidths=0,
-        legend=True,
-        legend_kwds={"label": r"Solar Energy Potential [GWh/a/km$^2$]", "shrink": 0.8},
-    )
-    ax.add_feature(cartopy.feature.COASTLINE.with_scale("50m"), linewidth=0.5, zorder=2)
-    ax.add_feature(cartopy.feature.BORDERS.with_scale("50m"), linewidth=0.5, zorder=2)
-    ax.gridlines(linestyle=":")
-    ax.axis("off")
+    fig, ax = plot_map(onregions, solar_per_skm, "Reds", r"Solar Energy Potential [GWh/a/km$^2$]")
 
     for fn in snakemake.output["solar"]:
         plt.savefig(fn)
+
+    cf = (ds.profile.mean("time") * 100).to_pandas()
+    cf = cf.groupby(level=0).mean().reindex(onregions.index, fill_value=0.)
+
+    fig, ax = plot_map(onregions, cf, "Reds", r"Solar Capacity Factor [%]", vmin=6, vmax=20)
+
+    for fn in snakemake.output["solar_cf"]:
+        plt.savefig(fn)