def get_curtailment_time_series(scenario, area, area_type=None):
"""Get hourly curtailment for each available resource(s) in area.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance
:param str area: one of *loadzone*, *state*, *state abbreviation*,
*interconnect*, *'all'*
:param str area_type: one of *'loadzone'*, *'state'*, *'state_abbr'*,
*'interconnect'*
:return: (*pandas.DataFrame*) -- index: timestamps, columns: available renewable
resource(s).
"""
renewables = ["wind", "wind_offshore", "solar"]
curtailment = get_generation_time_series_by_resources(scenario,
area,
renewables,
area_type=area_type)
renewable_profiles = pd.concat(
[scenario.state.get_wind(),
scenario.state.get_solar()], axis=1)
for r in renewables:
if r in curtailment.columns:
plant_id = get_plant_id_for_resources_in_area(scenario,
area,
r,
area_type=area_type)
curtailment[r] = renewable_profiles[plant_id].sum(
axis=1) - curtailment[r]
curtailment.rename(lambda x: x + "_curtailment",
axis="columns",
inplace=True)
return curtailment.clip(lower=0)
def get_capacity_by_resources(scenario, area, resources, area_type=None):
"""Get the total nameplate capacity for generator type(s) in an area
: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'*
:return: (*pandas.Series*) -- index: resources, column: total nameplate Pmax
capacity
"""
plant_id = get_plant_id_for_resources_in_area(scenario,
area,
resources,
area_type=area_type)
grid = scenario.state.get_grid()
return grid.plant.loc[plant_id].groupby("type")["Pmax"].sum()
def get_generation_time_series_by_resources(scenario, area, resources, area_type=None):
"""Get hourly total generation for generator type(s) in an area.
: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'*
:return: (*pandas.DataFrame*) -- times series of generation for each resource,
index: timestamps, columns: resources
"""
_check_scenario_is_in_analyze_state(scenario)
plant_id = get_plant_id_for_resources_in_area(
scenario, area, resources, area_type=area_type
)
pg = scenario.state.get_pg()[plant_id]
grid = scenario.state.get_grid()
return pg.groupby(grid.plant.loc[plant_id, "type"].values, axis=1).sum()
def get_capacity_factor_time_series(scenario, area, resources, area_type=None):
"""Get the hourly capacity factor of each generator fueled by resource(s) in an
area.
: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'*
:return: (*pandas.DataFrame*) -- index: timestamps, column: plant ids,
value: capacity factors
"""
_check_scenario_is_in_analyze_state(scenario)
plant_id = get_plant_id_for_resources_in_area(scenario,
area,
resources,
area_type=area_type)
pg = scenario.state.get_pg()[plant_id]
capacity = scenario.state.get_grid().plant.loc[plant_id, "Pmax"]
cf = (pg / capacity).replace(np.inf, 0).clip(0, 1)
return cf
def test_get_plant_id_for_resources_in_area():
arg = [(scenario, "Washington", "coal"), (scenario, "all", "coal")]
expected = [["A"], ["A", "C"]]
for a, e in zip(arg, expected):
plant_id = get_plant_id_for_resources_in_area(*a)
assert e == plant_id
def plot_scatter_capacity_vs_cost_curve_slope(
scenario,
area,
resources,
area_type=None,
markersize=50,
fontsize=20,
title=None,
show_plot=True,
):
"""Generate for a given scenario the scatter plot of the capacity (x-axis) vs
cost curve slope (y-axis) of generators located in area and fueled by resources
: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 int/float markersize: marker size, default to 50.
:param int/float fontsize: font size, default to 20.
:param str title: user specified figure title, default to None.
:param bool show_plot: show the plot or not, default to True.
:return: (*tuple*) -- the first entry is matplotlib.axes.Axes object of the plot,
the second entry is the capacity weighted average of cost curve slopes over the
selected time range.
:raises TypeError:
if markersize is not an integer or a float and/or
if fontsize is not an integer or a float and/or
if title is provided but not in a string format.
"""
_check_scenario_is_in_analyze_state(scenario)
if not isinstance(markersize, (int, float)):
raise TypeError("markersize should be either an integer or a float")
if not isinstance(fontsize, (int, float)):
raise TypeError("fontsize should be either an integer or a float")
if title is not None and not isinstance(title, str):
raise TypeError("title should be a string")
plant_id = get_plant_id_for_resources_in_area(scenario,
area,
resources,
area_type=area_type)
grid = scenario.state.get_grid()
plant_df = grid.plant.loc[plant_id]
cost_df = grid.gencost["after"].loc[plant_id]
slope = (cost_df["f2"] - cost_df["f1"]) / (cost_df["p2"] - cost_df["p1"])
total_cap = plant_df["Pmax"].sum()
if total_cap == 0:
data_avg = 0
else:
data_avg = (plant_df["Pmax"] * slope).sum() / total_cap
_, ax = plt.subplots(figsize=[20, 10])
ax.scatter(plant_df["Pmax"], slope, s=markersize)
ax.plot(plant_df["Pmax"], [data_avg] * len(plant_df.index), c="red")
ax.grid()
if title is None:
ax.set_title(
f'{area} {" ".join(resources) if isinstance(resources, list) else resources}'
)
else:
ax.set_title(title)
ax.set_xlabel("Capacity (MW)")
ax.set_ylabel("Slope")
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(fontsize)
if show_plot:
plt.show()
return ax, data_avg
def plot_scatter_capacity_vs_curtailment(
scenario,
area,
resources,
time_zone="utc",
time_range=None,
area_type=None,
between_time=None,
dayofweek=None,
markersize=50,
fontsize=20,
title=None,
percentage=False,
show_plot=True,
):
"""Generate for a given scenario the scatter plot of the capacity (x-axis) vs
curtailment as a fraction of available resources (y-axis) of generators
located in area and fueled by resources over a time range.
: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 time_zone: new time zone, default to be *'utc'*.
: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 area_type: one of *'loadzone'*, *'state'*, *'state_abbr'*,
*'interconnect'*
:param list between_time: specify the start hour and end hour of each day
inclusively, default to None, which includes every hour of a day. Note that if
the end hour is set before the start hour, the complementary hours of a day are
picked.
:param set dayofweek: specify the interest days of week, which is a subset of
integers in [0, 6] with 0 being Monday and 6 being Sunday, default to None,
which includes every day of a week.
:param int/float markersize: marker size, default to 50.
:param int/float fontsize: font size, default to 20.
:param str title: user specified figure title, default to None.
:param bool percentage: show capacity factor in percentage or not, default to False
:param bool show_plot: show the plot or not, default to True.
:return: (*tuple*) -- the first entry is matplotlib.axes.Axes object of the plot,
the second entry is the capacity weighted average of curtailment over the
selected time range.
:raises TypeError:
if markersize is not an integer or a float and/or
if fontsize is not an integer or a float and/or
if title is provided but not in a string format.
"""
if not isinstance(markersize, (int, float)):
raise TypeError("markersize should be either an integer or a float")
if not isinstance(fontsize, (int, float)):
raise TypeError("fontsize should be either an integer or a float")
if title is not None and not isinstance(title, str):
raise TypeError("title should be a string")
resources = _check_resources_are_renewable_and_format(
resources, grid_model=scenario.info["grid_model"])
curtailment = calculate_curtailment_time_series(scenario)
plant_list = get_plant_id_for_resources_in_area(scenario,
area,
resources,
area_type=area_type)
curtailment = curtailment[set(plant_list) & set(curtailment.columns)]
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"),
)
curtailment = slice_time_series(
curtailment,
time_range[0],
time_range[1],
between_time=between_time,
dayofweek=dayofweek,
)
profiles = pd.concat(
[scenario.state.get_solar(),
scenario.state.get_wind()], axis=1)
curtailment = curtailment.sum() / profiles[curtailment.columns].sum()
if percentage:
curtailment = (curtailment * 100).round(2)
total_cap = get_capacity_by_resources(scenario,
area,
resources,
area_type=area_type).sum()
plant_df = scenario.state.get_grid().plant.loc[plant_list]
if total_cap == 0:
data_avg = 0
else:
data_avg = (plant_df["Pmax"] * curtailment).sum() / total_cap
_, ax = plt.subplots(figsize=[20, 10])
ax.scatter(plant_df["Pmax"], curtailment, s=markersize)
ax.plot(plant_df["Pmax"], [data_avg] * len(plant_df.index), c="red")
ax.grid()
if title is None:
ax.set_title(
f"{area} "
f'{" ".join(resources) if isinstance(resources, (list, set)) else resources}'
)
else:
ax.set_title(title)
ax.set_xlabel("Capacity (MW)")
if percentage:
ax.set_ylabel("Curtailment %")
else:
ax.set_ylabel("Curtailment")
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(fontsize)
if show_plot:
plt.show()
return ax, data_avg
