def calculate_NLDC(scenario, resources, hours=100): # noqa: N802
"""Calculate the capacity value of a class of resources by comparing the
mean of the top N hour of absolute demand to the mean of the top N hours of
net demand. NLDC = 'Net Load Duration Curve'.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param str/list/tuple/set resources: one or more resources to analyze.
:param int hours: number of hours to analyze.
:return: (*float*) -- difference between peak demand and peak net demand.
"""
_check_scenario_is_in_analyze_state(scenario)
grid = scenario.state.get_grid()
resources = _check_resources_are_in_grid_and_format(resources, grid)
_check_number_hours_to_analyze(scenario, hours)
# Then calculate capacity value
total_demand = scenario.state.get_demand().sum(axis=1)
prev_peak = total_demand.sort_values(ascending=False).head(hours).mean()
plant_groupby = grid.plant.groupby("type")
plant_indices = sum(
[plant_groupby.get_group(r).index.tolist() for r in resources], [])
resource_generation = scenario.state.get_pg()[plant_indices].sum(axis=1)
net_demand = total_demand - resource_generation
net_peak = net_demand.sort_values(ascending=False).head(hours).mean()
return prev_peak - net_peak
def calculate_net_load_peak(scenario, resources, hours=100):
"""Calculate the capacity value of a class of resources by averaging the
power generated in the top N hours of net load peak.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param str/list/tuple/set resources: one or more resources to analyze.
:param int hours: number of hours to analyze.
:return: (*float*) -- resource capacity during hours of peak net demand.
"""
_check_scenario_is_in_analyze_state(scenario)
grid = scenario.state.get_grid()
resources = _check_resources_are_in_grid_and_format(resources, grid)
_check_number_hours_to_analyze(scenario, hours)
# Then calculate capacity value
total_demand = scenario.state.get_demand().sum(axis=1)
plant_groupby = grid.plant.groupby("type")
plant_indices = sum(
[plant_groupby.get_group(r).index.tolist() for r in resources], [])
resource_generation = scenario.state.get_pg()[plant_indices].sum(axis=1)
net_demand = total_demand - resource_generation
top_hours = net_demand.sort_values(ascending=False).head(hours).index
return resource_generation[top_hours].mean()
def test_check_number_hours_to_analyze_argument_value(mock_scenario):
arg = ((mock_scenario, -10), (mock_scenario, 15 * 24))
for a in arg:
with pytest.raises(ValueError):
_check_number_hours_to_analyze(a[0], a[1])
def test_check_number_hours_to_analyze(mock_scenario):
_check_number_hours_to_analyze(mock_scenario, 24)
def test_check_number_hours_to_analyze_argument_type(mock_scenario):
arg = ((mock_scenario, "100"), (mock_scenario, [100]), (mock_scenario, {100, 50}))
for a in arg:
with pytest.raises(TypeError):
_check_number_hours_to_analyze(a[0], a[1])