|
| 1 | +from matplotlib import pyplot as plt |
| 2 | +from matplotlib.cm import get_cmap, ScalarMappable |
| 3 | +from matplotlib.colors import ( |
| 4 | + TwoSlopeNorm, |
| 5 | +) |
| 6 | +from matplotlib.ticker import PercentFormatter |
| 7 | + |
| 8 | +from switch_model.tools.graph.main import GraphTools |
| 9 | +from papers.Martin_Staadecker_et_al_2022.util import ( |
| 10 | + get_scenario, |
| 11 | + set_style, save_figure, |
| 12 | +) |
| 13 | + |
| 14 | +scenarios_supplementary = [ |
| 15 | + get_scenario("1342", "Baseline"), |
| 16 | + get_scenario("T5", "10x Tx Build Costs"), |
| 17 | +] |
| 18 | + |
| 19 | +tools_supplementary = GraphTools(scenarios=scenarios_supplementary, set_style=False) |
| 20 | +tools_supplementary.pre_graphing(multi_scenario=True) |
| 21 | + |
| 22 | +# Uncomment to make supplementary figure |
| 23 | +tools = tools_supplementary |
| 24 | +zones_to_highlight = None |
| 25 | + |
| 26 | +n = len(scenarios_supplementary) |
| 27 | + |
| 28 | + |
| 29 | +# %% GET DATA |
| 30 | + |
| 31 | + |
| 32 | +def get_data(scenario_index): |
| 33 | + dispatch = tools.get_dataframe("dispatch_zonal_annual_summary.csv") |
| 34 | + dispatch = dispatch[dispatch.scenario_index == scenario_index] |
| 35 | + dispatch = tools.transform.gen_type(dispatch) |
| 36 | + dispatch = tools.transform.load_zone(dispatch, load_zone_col="gen_load_zone") |
| 37 | + dispatch = dispatch[dispatch.gen_type != "Storage"] |
| 38 | + dispatch = dispatch.groupby("gen_load_zone")[["Energy_GWh_typical_yr"]].sum() |
| 39 | + dispatch.columns = ["generation_gwh"] |
| 40 | + |
| 41 | + demand = tools.get_dataframe("load_balance_annual_zonal.csv") |
| 42 | + demand = demand[demand.scenario_index == scenario_index] |
| 43 | + demand = demand.set_index("load_zone") |
| 44 | + demand = demand["zone_demand_mw"] |
| 45 | + demand *= -1e-3 |
| 46 | + demand = demand.rename("demand_gwh") |
| 47 | + |
| 48 | + df = dispatch.join(demand) |
| 49 | + df["percent_gen"] = df["generation_gwh"] / df["demand_gwh"] * 100 |
| 50 | + df = df["percent_gen"] |
| 51 | + |
| 52 | + duration = tools.get_dataframe("storage_capacity.csv").rename( |
| 53 | + {"load_zone": "gen_load_zone"}, axis=1 |
| 54 | + ) |
| 55 | + duration = duration[duration.scenario_index == scenario_index] |
| 56 | + duration = duration[duration["period"] == 2050].drop(columns="period") |
| 57 | + duration = duration.groupby("gen_load_zone", as_index=False).sum() |
| 58 | + duration["value"] = ( |
| 59 | + duration["OnlineEnergyCapacityMWh"] / duration["OnlinePowerCapacityMW"] |
| 60 | + ) |
| 61 | + duration = duration[["gen_load_zone", "value", "OnlinePowerCapacityMW"]] |
| 62 | + duration["OnlinePowerCapacityMW"] *= 1e-3 |
| 63 | + |
| 64 | + demand = tools.get_dataframe("loads.csv", from_inputs=True) |
| 65 | + demand = demand[demand.scenario_index == scenario_index] |
| 66 | + demand = demand.groupby("LOAD_ZONE").zone_demand_mw.max() |
| 67 | + demand *= 1e-3 |
| 68 | + duration = duration.set_index("gen_load_zone") |
| 69 | + duration = duration.join(demand) |
| 70 | + duration = duration.reset_index() |
| 71 | + duration["percent_power"] = ( |
| 72 | + duration["OnlinePowerCapacityMW"] / duration["zone_demand_mw"] * 100 |
| 73 | + ) |
| 74 | + |
| 75 | + return df, duration |
| 76 | + |
| 77 | + |
| 78 | +data = [get_data(i) for i in range(n)] |
| 79 | + |
| 80 | +# %% DEFINE FIGURE AND PLOTTING FUNCTIONS |
| 81 | +set_style() |
| 82 | +plt.close() |
| 83 | +fig = plt.figure() |
| 84 | + |
| 85 | +# Define axes |
| 86 | +axes = [] |
| 87 | +for i in range(n): |
| 88 | + axes.append(fig.add_subplot(1, n, i + 1, projection=tools.maps.get_projection())) |
| 89 | + |
| 90 | +plt.subplots_adjust(left=0.02, right=0.98, wspace=0.05) |
| 91 | + |
| 92 | +Y_LIM = 11 * 100 |
| 93 | +cmap = get_cmap("bwr") |
| 94 | + |
| 95 | +normalizer = TwoSlopeNorm(vmin=0, vcenter=100, vmax=Y_LIM) |
| 96 | + |
| 97 | + |
| 98 | +def percent_to_color(percent): |
| 99 | + return cmap(normalizer(percent)) |
| 100 | + |
| 101 | + |
| 102 | +def plot(ax, data, legend): |
| 103 | + percent_gen, duration = data |
| 104 | + |
| 105 | + max_size = 400 |
| 106 | + max = 50 |
| 107 | + duration["size"] = duration["OnlinePowerCapacityMW"] / max * max_size |
| 108 | + tools.maps.draw_base_map(ax) |
| 109 | + percent_gen = percent_gen.apply(percent_to_color) |
| 110 | + tools.maps.graph_load_zone_colors(percent_gen, ax) |
| 111 | + legend_handles = tools.maps.graph_duration( |
| 112 | + duration, ax=ax, legend=False, bins=(0, 6, 10, 20, float("inf")) |
| 113 | + ) |
| 114 | + |
| 115 | + if legend: |
| 116 | + fig.legend( |
| 117 | + title="Storage Duration (h)", |
| 118 | + handles=legend_handles, |
| 119 | + bbox_to_anchor=(0.6, 0), |
| 120 | + loc="lower center", |
| 121 | + fontsize="small", |
| 122 | + title_fontsize="small", |
| 123 | + ncol=4, |
| 124 | + ) |
| 125 | + # Add legend for power capacity |
| 126 | + sizes = [5, 10, 30] |
| 127 | + fig.legend( |
| 128 | + title="Storage Capacity (GW)", |
| 129 | + handles=[ |
| 130 | + tools.plt.lines.Line2D( |
| 131 | + [], |
| 132 | + [], |
| 133 | + color="dimgray", |
| 134 | + marker=".", |
| 135 | + markersize=l, |
| 136 | + label=l, |
| 137 | + linestyle="None", |
| 138 | + markeredgewidth=1, |
| 139 | + markeredgecolor="dimgray", |
| 140 | + ) |
| 141 | + for l, s in zip(sizes, [x / max * max_size for x in sizes]) |
| 142 | + ], |
| 143 | + bbox_to_anchor=(0.35, 0), |
| 144 | + loc="lower center", |
| 145 | + fontsize="small", |
| 146 | + title_fontsize="small", |
| 147 | + ncol=3, |
| 148 | + labelspacing=1.5, |
| 149 | + ) |
| 150 | + |
| 151 | + |
| 152 | +for i, ax in enumerate(axes): |
| 153 | + plot(ax, data[i], legend=(i == n - 1)) |
| 154 | + ax.set_title(tools.scenarios[i].name) |
| 155 | + |
| 156 | +fig.colorbar( |
| 157 | + ScalarMappable(norm=normalizer, cmap=cmap), |
| 158 | + format=PercentFormatter(), |
| 159 | + ticks=[0, 20, 40, 60, 80, 100, 300, 500, 700, 900, 1100], |
| 160 | + extend="max", |
| 161 | + ax=axes, |
| 162 | + location="right", |
| 163 | + label="Yearly Generation / Yearly Demand", |
| 164 | +) |
| 165 | + |
| 166 | + |
| 167 | +def highlight_zones(zones, ax): |
| 168 | + if zones is None: |
| 169 | + return |
| 170 | + for _, lz in tools.maps._wecc_lz.iterrows(): |
| 171 | + if lz.gen_load_zone in zones: |
| 172 | + # Add load zone borders |
| 173 | + ax.add_geometries( |
| 174 | + lz.geometry, |
| 175 | + crs=tools.maps.get_projection(), |
| 176 | + facecolor=(0, 0, 0, 0), # Transparent |
| 177 | + edgecolor="tab:green", |
| 178 | + linewidth=1, |
| 179 | + # linestyle="--", |
| 180 | + # alpha=0, |
| 181 | + ) |
| 182 | + |
| 183 | + |
| 184 | +highlight_zones(zones_to_highlight, axes[0]) |
| 185 | + |
| 186 | +# %% SAVE FIGURE |
| 187 | +save_figure("figure-s2-impact-of-10x-tx.png") |
| 188 | + |
| 189 | +# %% |
| 190 | +df = tools_supplementary.get_dataframe("storage_capacity.csv") |
| 191 | +df = df.set_index("load_zone") |
| 192 | +df_baseline = df[df.scenario_index == 0] |
| 193 | +df_compare = df[df.scenario_index == 1] |
| 194 | +df = df_baseline.join(df_compare, lsuffix="_base", rsuffix="_compare") |
| 195 | +# df["change_in_cap"] = ( |
| 196 | +# df["OnlineEnergyCapacityMWh_compare"] - df["OnlineEnergyCapacityMWh_base"] |
| 197 | +# ) * 1e-3 |
| 198 | +df["change_in_cap"] = ( |
| 199 | + df["OnlineEnergyCapacityMWh_compare"] / df["OnlineEnergyCapacityMWh_base"] |
| 200 | + ) * 100 - 100 |
| 201 | +df = df["change_in_cap"] |
| 202 | +# df = df[df > 0] |
| 203 | +# df.sum() |
| 204 | +# df_compare["OnlineEnergyCapacityMWh"].sum() / df_baseline[ |
| 205 | +# "OnlineEnergyCapacityMWh" |
| 206 | +# ].sum() * 100 - 100 |
| 207 | +# df_compare["OnlineEnergyCapacityMWh"].sum() - df_baseline[ |
| 208 | +# "OnlineEnergyCapacityMWh" |
| 209 | +# ].sum() |
| 210 | +df |
| 211 | + |
| 212 | +# %% Num of load zones generating less than 25% of demand |
| 213 | +scenario_index = 0 |
| 214 | +# scenario_index = 1 # For baseline |
| 215 | +df = data[scenario_index][0].copy() |
| 216 | +df = df[df < 50] |
| 217 | +len(df) |
| 218 | + |
| 219 | +# %% Contribution of zones to highlight |
| 220 | + |
| 221 | +scenario_index = 0 |
| 222 | +# scenario_index = 1 # For baseline |
| 223 | +dispatch = tools.get_dataframe("dispatch_zonal_annual_summary.csv") |
| 224 | +dispatch = dispatch[dispatch.scenario_index == scenario_index] |
| 225 | +dispatch = tools.transform.gen_type(dispatch) |
| 226 | +dispatch = dispatch[dispatch.gen_type != "Storage"] |
| 227 | +dispatch = dispatch.groupby("gen_load_zone")[["Energy_GWh_typical_yr"]].sum() |
| 228 | +dispatch.columns = ["generation_gwh"] |
| 229 | +dispatch = dispatch.reset_index() |
| 230 | +dispatch.sort_values("generation_gwh") |
| 231 | +total = dispatch.generation_gwh.sum() |
| 232 | +total_for_zone = dispatch[ |
| 233 | + dispatch.gen_load_zone.isin(zones_to_highlight) |
| 234 | +].generation_gwh.sum() |
| 235 | +total_for_zone / total |
| 236 | + |
| 237 | +# %% Num zones to highlight |
| 238 | +len(zones_to_highlight) |
| 239 | + |
| 240 | +# %% Power contribution for load zones |
| 241 | +df = tools.get_dataframe("storage_capacity.csv") |
| 242 | +df = df.set_index("load_zone") |
| 243 | +cities = ["CA_LADWP", "WA_SEATAC", "CA_PGE_BAY", "CA_SCE_S", "AZ_PHX"] |
| 244 | +# df = df.loc[cities] |
| 245 | +df["OnlineEnergyCapacityMWh"] *= 1e-3 |
| 246 | +df_compare = df[df.scenario_index == 0] |
| 247 | +df_baseline = df[df.scenario_index == 1] |
| 248 | +df = df_baseline.join(df_compare, lsuffix="_base", rsuffix="_compare") |
| 249 | +df["change_in_cap"] = ( |
| 250 | + df["OnlineEnergyCapacityMWh_compare"] - df["OnlineEnergyCapacityMWh_base"] |
| 251 | +) |
| 252 | +# df["change_in_cap"] = (df["OnlineEnergyCapacityMWh_compare"] / df["OnlineEnergyCapacityMWh_base"]) * 100 |
| 253 | +df = df["change_in_cap"] |
| 254 | +df.sort_values() |
| 255 | +# df.sum() |
| 256 | +# df_compare["OnlineEnergyCapacityMWh"].sum() / df_baseline["OnlineEnergyCapacityMWh"].sum() * 100 |
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