Final changes to figures

Author: staadecker

papers/Martin_Staadecker_et_al_2022/analysis.py

@@ -27,7 +27,12 @@
 prebuilt = prebuilt[(prebuilt.build_year + prebuilt.gen_max_age) > 2051]
 print("prebuilt alive :", len(prebuilt))
 
+print("prebuild by tech")
+prebuilt_by_tech = prebuilt.groupby(["gen_energy_source", "gen_tech"]).GENERATION_PROJECT.count()
+print(prebuilt_by_tech)
+
 prebuilt_by_tech = prebuilt.groupby(["gen_energy_source", "gen_tech"]).gen_capacity_limit_mw.sum() / 1000
+print("prebuilt by tech capacity")
 print(prebuilt_by_tech.sort_values(ascending=False))
 print(prebuilt_by_tech.sum())
 

papers/Martin_Staadecker_et_al_2022/figure-1-baseline.py

@@ -148,11 +148,11 @@ def rolling_avg(df):
         color=colors[columnName],
         label=columnName + " (no curtail.)",
     )
-ax_right.plot(duals, label="Estimated LMP", color="red")
+ax_right.plot(duals, label="Marginal Price", color="red")
 lines += ax.plot(load, color="orange", label="Demand")
 ax.set_title("A. Seasonal Profiles in the Baseline")
 ax.set_ylabel("Dispatch (TWh/day)")
-ax_right.set_ylabel(u"Estimated Locational Marginal Price ($/MWh)")
+ax_right.set_ylabel(u"Marginal Price of Electricity ($/MWh)")
 locator = mdates.MonthLocator()
 ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator))
 ax.set_ylim(-0.1, 4.7)

papers/Martin_Staadecker_et_al_2022/reference-map-of-load-zones-with-names.py renamed to papers/Martin_Staadecker_et_al_2022/figure-s5-map-of-load-zones.py

@@ -6,7 +6,7 @@
 
 from matplotlib import pyplot as plt
 
-from papers.Martin_Staadecker_et_al_2022.util import get_scenario, set_style
+from papers.Martin_Staadecker_et_al_2022.util import get_scenario, set_style, save_figure
 from switch_model.tools.graph.main import GraphTools
 
 tools = GraphTools([get_scenario("1342")], set_style=False)
@@ -16,14 +16,29 @@
 set_style()
 plt.close()
 fig = plt.figure()
-fig.set_size_inches(8, 8)
+fig.set_size_inches(6.850394, 6.850394)
 ax = fig.add_subplot(1, 1, 1, projection=tools.maps.get_projection())
 
 tools.maps.draw_base_map(ax)
 
+centers = {}
+
 for _, lz in tools.maps._center_points.iterrows():
     center = lz.geometry.centroid
-    ax.scatter(center.x, center.y, color="k", s=10, alpha=0.5)
-    ax.text(center.x, center.y, lz.gen_load_zone, fontsize="x-small")
+    ax.scatter(center.x, center.y, color="k", s=5, alpha=0.5)
+    ax.text(center.x, center.y, lz.gen_load_zone, fontsize="small")
+    centers[lz.gen_load_zone] = center
+
+tx = tools.get_dataframe("transmission_lines.csv", from_inputs=True)
+tx = tx[["trans_lz1", "trans_lz2"]]
+for _, line in tx.iterrows():
+    from_center = centers[line["trans_lz1"]]
+    to_center = centers[line["trans_lz2"]]
+    ax.plot([from_center.x, to_center.x], [from_center.y, to_center.y], color="k", linestyle="--", linewidth=1, alpha=0.3)
+
+for lz, center in centers.items():
+    ax.text(center.x, center.y, lz, fontsize="small")
 
-plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
+plt.tight_layout()
+# %%
+save_figure("figure-s5-map-of-load-zones.png")

papers/Martin_Staadecker_et_al_2022/figure-s5-duration-cdf-cost-scenarios.py renamed to papers/Martin_Staadecker_et_al_2022/figure-x1-duration-cdf-cost-scenarios.py

@@ -0,0 +1,42 @@
+from papers.Martin_Staadecker_et_al_2022.util import get_scenario
+from switch_model.tools.graph.main import GraphTools
+
+tools = GraphTools(scenarios=[
+    get_scenario("1342", "1342")
+])
+tools.pre_graphing(multi_scenario=False)
+
+projects = tools.get_dataframe("generation_projects_info.csv", from_inputs=True, convert_dot_to_na=True)
+costs = tools.get_dataframe("gen_build_costs.csv", from_inputs=True, convert_dot_to_na=True)
+predetermined = tools.get_dataframe("gen_build_predetermined", from_inputs=True, convert_dot_to_na=True)
+
+projects = projects.merge(
+    costs,
+    on=["GENERATION_PROJECT"],
+)
+
+projects = projects.merge(
+    predetermined,
+    on=["GENERATION_PROJECT", "build_year"],
+    how="left"  # Makes a left join
+)
+
+prebuilt = projects[projects.build_year != 2050]
+prebuilt_by_tech = (prebuilt.groupby(["gen_energy_source", "gen_tech"]).gen_capacity_limit_mw.sum() / 1000).round(1)
+print("prebuilt by tech capacity")
+print(prebuilt_by_tech)
+print(prebuilt_by_tech.sum())
+
+
+prebuilt = prebuilt[(prebuilt.build_year + prebuilt.gen_max_age) > 2051]
+prebuilt_by_tech = (prebuilt.groupby(["gen_energy_source", "gen_tech"]).gen_capacity_limit_mw.sum() / 1000).round(1)
+print("prebuilt by tech capacity still online")
+print(prebuilt_by_tech)
+print(prebuilt_by_tech.sum())
+
+candidate = projects[projects.build_year == 2050]
+candidate.gen_capacity_limit_mw = candidate.gen_capacity_limit_mw.fillna(-999999999)
+print("candidate projects aggregated: ", len(candidate))
+candidate_by_tech = (candidate.groupby(["gen_energy_source", "gen_tech"]).gen_capacity_limit_mw.sum() / 1000).round(1)
+print(candidate_by_tech)
+print(candidate_by_tech[candidate_by_tech > 0].sum())

papers/Martin_Staadecker_et_al_2022/figure-s6-duration-cdf-cost-scenarios-ca-only.py renamed to papers/Martin_Staadecker_et_al_2022/figure-x2-duration-cdf-cost-scenarios-ca-only.py

@@ -0,0 +1,33 @@
+from papers.Martin_Staadecker_et_al_2022.util import get_scenario
+from switch_model.tools.graph.main import GraphTools
+import pandas as pd
+
+tools = GraphTools(scenarios=[
+    get_scenario("1342", "1342")
+])
+tools.pre_graphing(multi_scenario=False)
+
+projects = tools.get_dataframe("generation_projects_info.csv", from_inputs=True, convert_dot_to_na=True)
+costs = tools.get_dataframe("gen_build_costs.csv", from_inputs=True, convert_dot_to_na=True)
+predetermined = tools.get_dataframe("gen_build_predetermined", from_inputs=True, convert_dot_to_na=True)
+
+projects = projects.merge(
+    costs,
+    on=["GENERATION_PROJECT"],
+)
+
+projects = projects.merge(
+    predetermined,
+    on=["GENERATION_PROJECT", "build_year"],
+    how="left"  # Makes a left join
+)
+
+# prebuilt = projects[projects.build_year != 2050]
+age = (projects.groupby(["gen_energy_source", "gen_tech"]).gen_max_age.unique())
+
+forced_outage_rate = (projects.groupby(["gen_energy_source", "gen_tech"]).gen_forced_outage_rate.unique())
+
+scheduled_outage_rate = (projects.groupby(["gen_energy_source", "gen_tech"]).gen_scheduled_outage_rate.unique())
+
+all_data = pd.concat([age, forced_outage_rate, scheduled_outage_rate], axis=1)
+print(all_data)

papers/Martin_Staadecker_et_al_2022/table-s2-capacity-by-tech.py

@@ -0,0 +1,33 @@
+from papers.Martin_Staadecker_et_al_2022.util import get_scenario
+from switch_model.tools.graph.main import GraphTools
+import pandas as pd
+
+tools = GraphTools(scenarios=[
+    get_scenario("1342", "1342")
+])
+tools.pre_graphing(multi_scenario=False)
+
+projects = tools.get_dataframe("generation_projects_info.csv", from_inputs=True, convert_dot_to_na=True)
+costs = tools.get_dataframe("gen_build_costs.csv", from_inputs=True, convert_dot_to_na=True)
+predetermined = tools.get_dataframe("gen_build_predetermined", from_inputs=True, convert_dot_to_na=True)
+
+projects = projects.merge(
+    costs,
+    on=["GENERATION_PROJECT"],
+)
+
+projects = projects.merge(
+    predetermined,
+    on=["GENERATION_PROJECT", "build_year"],
+    how="left"  # Makes a left join
+)
+
+projects = projects[projects.build_year == 2050]
+group = projects.groupby(["gen_energy_source", "gen_tech"])
+
+overnight = group.gen_overnight_cost.mean()
+fixed = group.gen_fixed_om.mean()
+variable = group.gen_variable_om.mean()
+
+all_data = pd.concat([overnight, fixed, variable], axis=1).round(0)
+print(all_data)