import os
import matplotlib.pyplot as plt
import pandas as pd
from powersimdata.input.check import _check_resources_and_format
from powersimdata.network.model import ModelImmutables
from powersimdata.scenario.check import _check_scenario_is_in_analyze_state
from postreise.analyze.demand import get_demand_time_series
from postreise.analyze.generation.curtailment import get_curtailment_time_series
from postreise.analyze.generation.summarize import (
get_generation_time_series_by_resources,
)
from postreise.analyze.time import (
change_time_zone,
resample_time_series,
slice_time_series,
)
[docs]def plot_curtailment_time_series(
scenario,
area,
resources,
area_type=None,
time_range=None,
time_zone="utc",
time_freq="H",
show_demand=True,
percentage=True,
t2c=None,
t2l=None,
title=None,
label_fontsize=20,
title_fontsize=22,
tick_fontsize=15,
legend_fontsize=18,
save=False,
filename=None,
filepath=None,
):
"""Generate time series curtailment plot of each specified resource in a certain
area of a scenario.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance
:param str area: one of *loadzone*, *state*, *state abbreviation*,
*interconnect*, *'all'*
:param str/list resources: one or a list of resources.
:param str area_type: one of *'loadzone'*, *'state'*, *'state_abbr'*,
*'interconnect'*
:param tuple time_range: [start_timestamp, end_timestamp] where each time stamp
is pandas.Timestamp/numpy.datetime64/datetime.datetime. If None, the entire
time range is used for the given scenario.
:param str time_zone: new time zone.
:param str time_freq: frequency. Either *'D'* (day), *'W'* (week), *'M'* (month).
:param bool show_demand: show demand line in the plot or not, default is True.
:param bool percentage: plot the curtailment in terms of percentage or not,
default is True.
:param dict t2c: user specified color of resource type to overwrite type2color
default dict. key: resource type, value: color code.
:param dict t2l: user specified label of resource type to overwrite type2label
default dict. key: resource type, value: label.
:param str title: user specified title of the figure.
:param float label_fontsize: user specified label fontsize, default is 20.
:param float title_fontsize: user specified title fontsize, default is 22.
:param float tick_fontsize: user specified ticks of axes fontsize, default is 15.
:param float legend_fontsize: user specified legend fontsize, default is 18.
:param bool save: save the generated figure or not, default is False.
:param str filename: if save is True, user specified filename, use area if None.
:param str filepath: if save is True, user specified filepath, use current
directory if None.
"""
_check_scenario_is_in_analyze_state(scenario)
resources = _check_resources_and_format(
resources, mi=ModelImmutables(scenario.info["grid_model"])
)
mi = ModelImmutables(scenario.info["grid_model"])
type2color = mi.plants["type2color"]
type2color.update(
{k + "_curtailment": v for k, v in mi.plants["curtailable2color"].items()}
)
type2label = mi.plants["type2label"]
type2label.update(
{k + "_curtailment": v for k, v in mi.plants["curtailable2label"].items()}
)
if t2c:
type2color.update(t2c)
if t2l:
type2label.update(t2l)
resource_pg = get_generation_time_series_by_resources(
scenario, area, resources, area_type=area_type
)
demand = get_demand_time_series(scenario, area, area_type=area_type)
curtailment = get_curtailment_time_series(scenario, area, area_type=area_type)
resource_pg = change_time_zone(resource_pg, time_zone)
demand = change_time_zone(demand, time_zone)
curtailment = change_time_zone(curtailment, time_zone)
if not time_range:
time_range = (
pd.Timestamp(scenario.info["start_date"], tz="utc"),
pd.Timestamp(scenario.info["end_date"], tz="utc"),
)
resource_pg = slice_time_series(resource_pg, time_range[0], time_range[1])
demand = slice_time_series(demand, time_range[0], time_range[1])
curtailment = slice_time_series(curtailment, time_range[0], time_range[1])
if time_freq != "H":
resource_pg = resample_time_series(resource_pg, time_freq)
demand = resample_time_series(demand, time_freq)
curtailment = resample_time_series(curtailment, time_freq)
for r in resource_pg.columns:
curtailment[r + "_curtailment" + "_mean"] = curtailment[
r + "_curtailment"
].mean()
curtailment[r + "_available"] = curtailment[r + "_curtailment"] + resource_pg[r]
curtailment[r + "_curtailment" + "_percentage"] = (
curtailment[r + "_curtailment"] / curtailment[r + "_available"] * 100
)
curtailment[r + "_curtailment" + "_percentage" + "_mean"] = curtailment[
r + "_curtailment" + "_percentage"
].mean()
for r in resources:
if r not in resource_pg.columns:
raise ValueError(f"{r} is invalid in {area}!")
fig = plt.figure(figsize=(20, 10))
ax = fig.gca()
title_text = f"{area} {r.capitalize()}" if not title else title
plt.title(title_text, fontsize=title_fontsize)
cr = r + "_curtailment"
if percentage:
key1, key2 = f"{cr}_percentage", f"{cr}_percentage_mean"
else:
key1, key2 = cr, f"{cr}_mean"
curtailment[key1].plot(
ax=ax,
lw=4,
alpha=0.7,
color=type2color[cr],
label=type2label[cr],
)
curtailment[key2].plot(
ax=ax,
ls="--",
lw=4,
alpha=0.7,
color=type2color[cr],
label=type2label[cr] + " Mean",
)
ax_twin = ax.twinx()
curtailment[r + "_available"].plot(
ax=ax_twin,
lw=4,
alpha=0.7,
color=type2color[r],
label=f"{type2label[r]} Energy Available",
)
if show_demand:
demand.plot(ax=ax_twin, lw=4, alpha=0.7, color="red", label="Demand")
# Erase year in xticklabels
xt_with_year = list(ax_twin.__dict__["date_axis_info"][0])
xt_with_year[-1] = b"%b"
ax_twin.__dict__["date_axis_info"][0] = tuple(xt_with_year)
ax_twin.set_xlabel("")
ax_twin.tick_params(which="both", labelsize=tick_fontsize)
ax_twin.yaxis.get_offset_text().set_fontsize(tick_fontsize)
ax_twin.set_ylabel("MWh", fontsize=label_fontsize)
ax_twin.legend(loc="upper right", prop={"size": legend_fontsize})
ax.tick_params(which="both", labelsize=tick_fontsize)
ax.yaxis.get_offset_text().set_fontsize(tick_fontsize)
ax.grid(color="black", axis="y")
ax.set_xlabel("")
if percentage:
ax.set_ylabel("Curtailment [%]", fontsize=label_fontsize)
else:
ax.set_ylabel("Curtailment", fontsize=label_fontsize)
ax.legend(loc="upper left", prop={"size": legend_fontsize})
if save:
if not filename:
filename = f"{area.lower()}_{r}_curtailment"
if not filepath:
filepath = os.path.join(os.getcwd(), filename)
plt.savefig(f"{filepath}.pdf", bbox_inches="tight", pad_inches=0)
