Flip hydro subplot, Add demand axis on state of charge plots

Author: staadecker

papers/Martin_Staadecker_et_al_2022/figure-2-analysis-of-4-factors.py

@@ -43,12 +43,12 @@
 # Define tools for hydro set
 tools_hydro = GraphTools(
     scenarios=[
-        get_scenario("H25", 0),
-        get_scenario("H025", 0.25),
+        get_scenario("H25", 1),
+        get_scenario("H025", 0.75),
         get_scenario("H050", 0.5),
-        get_scenario("H065", 0.65),
-        get_scenario("H085", 0.85),
-        get_scenario("1342", 1),
+        get_scenario("H065", 0.35),
+        get_scenario("H085", 0.15),
+        get_scenario("1342", 0),
     ], set_style=False
 )
 tools_hydro.pre_graphing(multi_scenario=True)
@@ -238,21 +238,23 @@ def plot_panel(ax, rax, rrax, rrrax, data, title=""):
 plot_panel(ax, None, None, None, data_ws, "Set A: Varying Wind-vs-Solar Share")
 
 ax.set_xticks([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
-ax.set_xticklabels(["90%-10%\nSolar-Wind", "", "70%-30%\nSolar-Wind", "", "50%-50%\nSolar-Wind", ""])
+ax.set_xticklabels(["90% Solar\n10% Wind", "", "70% Solar\n30% Wind", "", "50% Solar\n50% Wind", ""])
+ax.set_xlabel("Solar-Wind ratio")
 ax.axvline(baseline_ws_ratio, linestyle="dotted", color="dimgrey")
 ax.text(baseline_ws_ratio - 0.02, 0.1, "Baseline", rotation=90, color="dimgrey")
 
 fig.legend(loc="lower center", ncol=4)
 
 # %% PLOT HYDRO
-data_hy = get_data(tools_hydro, normalize_to_baseline=1)
+data_hy = get_data(tools_hydro, normalize_to_baseline=0)
 ax = ax_tr
 # rax = rax_top_right
 plot_panel(ax, None, None, None, data_hy, "Set B: Reducing Hydropower Generation")
 ax.tick_params(top=False, bottom=True, right=False, left=False, which="both")
 ax.set_xticks([0, 0.5, 1])
 ax.set_xticks([0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9], minor=True)
-ax.set_xticklabels(["No\nhydropower", "50%\nhydropower", "Baseline\nHydropower"])
+ax.set_xticklabels(["0%\n(Baseline)", "50%", "100%\n(No Hydro)"])
+ax.set_xlabel("Hydropower reduction")
 
 # %% PLOT TX
 ax = ax_bl
@@ -301,7 +303,7 @@ def plot_panel(ax, rax, rrax, rrrax, data, title=""):
 ax.axvline(baseline_energy_cost, linestyle="dotted", color="dimgrey")
 ax.text(baseline_energy_cost - 4, 0.1, "Baseline", rotation=90, color="dimgrey")
 
-plt.subplots_adjust(left=0.08, right=0.97, top=0.95, wspace=0.05)
+plt.subplots_adjust(left=0.08, right=0.97, top=0.95, wspace=0.05, hspace=0.25)
 
 # %% SAVE FIGURE
 save_figure("figure-2-analysis-of-4-factors.png")
@@ -372,3 +374,23 @@ def plot_panel(ax, rax, rrax, rrrax, data, title=""):
 total_power = df.groupby("scenario_name").OnlinePowerCapacityMW.sum()
 total_energy = df.groupby("scenario_name").OnlineEnergyCapacityMWh.sum()
 total_energy / total_power
+# %% California only costs
+df = tools_cost.get_dataframe("storage_capacity.csv")
+df = tools_cost.transform.load_zone(df)
+df = df[df.region == "CA"]
+df["duration"] = df["OnlineEnergyCapacityMWh"] / df["OnlinePowerCapacityMW"]
+print("max duration", df.groupby("scenario_name").duration.max())
+print("median duration", df.groupby("scenario_name").duration.median())
+print("total capacity", df.groupby("scenario_name").OnlineEnergyCapacityMWh.sum() / 1e6)
+
+cap = tools_cost.get_dataframe("gen_cap.csv")
+cap = tools_cost.transform.gen_type(cap)
+cap = cap.groupby(["scenario_index", "gen_type"], as_index=False)[
+    "GenCapacity"
+].sum()
+cap = cap.pivot(columns="gen_type", index="scenario_index", values="GenCapacity")
+cap *= 1e-3  # Convert to GW
+cap = cap.rename_axis("Technology", axis=1).rename_axis(None)
+cap = cap[["Solar"]]
+cap.index = cap.index.map(tools_cost.get_scenario_name)
+cap

papers/Martin_Staadecker_et_al_2022/figure-5-impact-of-ldes-on-grid.py

@@ -137,6 +137,8 @@
 # %% State of charge
 ax = ax3
 ax.clear()
+axr = ax.twinx()
+axr.grid(False)
 
 freq = "1D"
 
@@ -164,8 +166,8 @@
 demand = demand.set_index("datetime")["value"]
 demand *= 4 * 1e-6  # Each timestep is 4 hours, converting to TWh
 total_demand = demand.sum()
-demand = demand.resample(freq).mean()
-demand = demand * 60 / demand.max()
+print(total_demand)
+demand = demand.resample(freq).sum()
 
 state_of_charge.plot(
     ax=ax,
@@ -174,12 +176,7 @@
     xlabel="Time of year",
     legend=False,
 )
-plt.colorbar(
-    cm.ScalarMappable(norm=Normalize(1.94, 64), cmap="viridis"),
-    ax=ax,
-    label="Storage Capacity (TWh)",
-    fraction=0.1,
-)
+
 
 lines = ax.get_lines()
 x_label = {
@@ -197,12 +194,26 @@
         continue
     labellines.labelLine(line, state_of_charge.index[x_label[label]], linespacing=1, outline_width=1, label=str(int(label))+"TWh", align=False, color='k', fontsize="small")
 
-demand_lines = ax.plot(demand, c="dimgray", linestyle="--", alpha=0.5)
-ax.legend(demand_lines, [f"Demand ({total_demand:.0f} TWh/year)"])
+demand = demand.iloc[1:-1]
+demand_lines = axr.plot(demand, c="dimgray", linestyle="--", alpha=0.5)
+axr.legend(demand_lines, [f"Demand ({total_demand:.0f} TWh/year)"])
+
+ax.set_ylim(0, 65)
+axr.set_ylim(0, 65 / 10)
+axr.set_ylabel("Demand (TWh/day)")
 
 ax.set_title("C. State of charge throughout the year")
-# %% SAVE FIGURE
+
 plt.tight_layout()
+
+plt.colorbar(
+    cm.ScalarMappable(norm=Normalize(1.94, 64), cmap="viridis"),
+    ax=ax,
+    label="Storage Capacity (TWh)",
+    fraction=0.1,
+    pad=0.1
+)
+# %% SAVE FIGURE
 save_figure("figure-5-impact-of-ldes-on-grid.png")
 
 # %% CALCULATIONS

papers/Martin_Staadecker_et_al_2022/figure-s3-state-of-charge-under-different-costs.py

@@ -31,6 +31,8 @@
 fig = plt.figure()
 # fig.set_size_inches(12, 6)
 ax = fig.gca()
+axr = ax.twinx()
+axr.grid(False)
 
 freq = "1D"
 
@@ -58,8 +60,8 @@
 demand = demand.set_index("datetime")["value"]
 demand *= 4 * 1e-6  # Each timestep is 4 hours, converting to TWh
 total_demand = demand.sum()
-demand = demand.resample(freq).mean()
-demand = demand * 35 / demand.max()
+demand = demand.resample(freq).sum()
+demand = demand.iloc[1:-1]
 
 state_of_charge.plot(
     ax=ax,
@@ -68,12 +70,6 @@
     xlabel="Time of year",
     legend=False,
 )
-plt.colorbar(
-    cm.ScalarMappable(norm=Normalize(0.5, 102), cmap="viridis"),
-    ax=ax,
-    label="Energy Storage Capacity Costs ($/KWh)",
-    fraction=0.1,
-)
 
 lines = ax.get_lines()
 x_label = {
@@ -88,10 +84,22 @@
         continue
     labellines.labelLine(line, state_of_charge.index[x_label[label]], linespacing=1, outline_width=1, label=str(label)+"$/KWh", align=False, color='k', fontsize="small")
 
-demand_lines = ax.plot(demand, c="dimgray", linestyle="--", alpha=0.5)
-ax.legend(demand_lines, [f"Demand ({total_demand:.0f} TWh/year)"])
+demand_lines = axr.plot(demand, c="dimgray", linestyle="--", alpha=0.5)
+axr.legend(demand_lines, [f"Demand ({total_demand:.0f} TWh/year)"])
+axr.set_ylabel("Demand (TWh/day)")
+
+ax.set_ylim(0, 36)
+axr.set_ylim(0, 36 / 5)
 
 plt.tight_layout()
+
+plt.colorbar(
+    cm.ScalarMappable(norm=Normalize(0.5, 102), cmap="viridis"),
+    ax=ax,
+    label="Energy Storage Capacity Costs ($/KWh)",
+    pad=0.08,
+    fraction=0.05,
+)
 # %% SAVE FIGURE
 save_figure("figure-s3-state-of-charge-under-different-costs.png")