feat: add function plot_bar_renewable_max_profile_actual

postreise/plot/plot_bar_renewable_max_profile_actual.py

@@ -0,0 +1,101 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+from powersimdata.network.model import ModelImmutables
+
+from postreise.analyze.generation.summarize import (
+    sum_generation_by_state,
+    sum_generation_by_type_zone,
+)
+
+
+def plot_bar_renewable_max_profile_actual(
+    scenario,
+    interconnect,
+    gen_type,
+    percentage=False,
+    show_as_state=True,
+    fontsize=15,
+    plot_show=True,
+):
+    """Generate for a given scenario the bar plot of total capacity vs. available energy
+    vs. actual generation for a specific renewable resource in an interconnection.
+
+    :param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
+    :param str interconnect: the interconnection name of interest.
+    :param str gen_type: renewable resource.
+    :param bool percentage: show bars in percentage of total capacity or not,
+        defaults to False.
+    :param bool show_as_state: each bar represents a state within the given
+        interconnection or not, defaults to True, if not, each bar will represent a
+        loadzone instead.
+    :param int/float fontsize: font size of the texts shown on the plot.
+    :param plot_show: show the plot or not, defaults to True.
+    :return: (*matplotlib.axes.Axes*) -- axes object of the plot.
+    :raises ValueError: if gen_type is not one of renewable resources.
+    """
+    mi = ModelImmutables(scenario.info["grid_model"])
+    if gen_type not in mi.plants["renewable_resources"]:
+        raise ValueError("gen_type must be one of renewable resources")
+    grid = scenario.state.get_grid()
+    plant = grid.plant[grid.plant.type == gen_type]
+    if "wind" in gen_type:
+        profile = scenario.state.get_wind().sum()
+    else:
+        profile = scenario.state.get_solar().sum()
+    hour_num = (
+        pd.Timestamp(scenario.info["end_date"])
+        - pd.Timestamp(scenario.info["start_date"])
+    ).total_seconds() / 3600 + 1
+    if show_as_state:
+        zone_list = [
+            mi.zones["abv2state"][abv]
+            for abv in mi.zones["interconnect2abv"][interconnect]
+        ]
+        group_criteria = plant.zone_name.map(mi.zones["loadzone2state"])
+        profile_gen = profile.groupby(group_criteria).sum().rename("profile")
+        total_capacity = plant.groupby(group_criteria)["Pmax"].sum() * hour_num
+        actual_gen = sum_generation_by_state(scenario)[gen_type] * 1000
+    else:
+        zone_list = mi.zones["interconnect2loadzone"][interconnect]
+        profile_gen = profile.groupby(plant.zone_name).sum().rename("profile")
+        total_capacity = plant.groupby("zone_name")["Pmax"].sum() * hour_num
+        actual_gen = (
+            sum_generation_by_type_zone(scenario)
+            .rename(columns=grid.id2zone)
+            .T[gen_type]
+        )
+    df = pd.concat([total_capacity, profile_gen, actual_gen], axis=1).loc[zone_list]
+    if percentage:
+        df_pct = df.copy()
+        df_pct["Pmax"] = df["Pmax"].apply(lambda x: 1 if x > 0 else 0)
+        df_pct["profile"] = df["profile"] / df["Pmax"]
+        df_pct[gen_type] = df[gen_type] / df["Pmax"]
+        df = df_pct
+    df.sort_index(inplace=True)
+
+    width = 0.8
+    fig, ax = plt.subplots(figsize=[30, 15])
+    df["Pmax"].plot(kind="bar", width=width, color="yellowgreen", ax=ax)
+    df["profile"].plot(kind="bar", width=width * 0.7, color="steelblue", ax=ax)
+    df[gen_type].plot(kind="bar", width=width * 0.4, color="firebrick", ax=ax)
+    ax.legend(
+        ["Total Capacity", "Available Energy", "Actual Generation"],
+        bbox_to_anchor=(0.5, 0.95),
+        loc="lower center",
+        ncol=3,
+        fontsize=fontsize,
+    )
+    ax.grid()
+    if percentage:
+        ax.set_title(f"{gen_type.capitalize()} Curtailment %")
+    else:
+        ax.set_title(f"{gen_type.capitalize()} Curtailment MWh")
+    for item in (
+        [ax.title, ax.xaxis.label, ax.yaxis.label]
+        + ax.get_xticklabels()
+        + ax.get_yticklabels()
+    ):
+        item.set_fontsize(fontsize)
+    if plot_show:
+        plt.show()
+    return ax