def test_check_gencost_argument_type(mock_grid):
gencost_n = mock_grid.gencost["after"].copy()
gencost_n.n = gencost_n.n.astype(float)
arg = (1, gencost_n)
for a in arg:
with pytest.raises(TypeError):
_check_gencost(a)
def calculate_costs(scenario=None,
pg=None,
gencost=None,
decommit=False,
decommit_threshold=1):
"""Calculate individual generator costs at given powers. If decommit is
True, costs will be zero below the decommit threshold (1 MW).
Either ``scenario`` XOR (``pg`` AND ``gencost``) must be specified.
:param powersimdata.scenario.scenario.Scenario scenario: scenario to analyze.
:param pandas.DataFrame pg: Generation solution data frame.
:param pandas.DataFrame gencost: cost curve polynomials.
:param bool decommit: Whether to decommit generator at low power.
:param int/float decommit_threshold: The power (MW) below which generators are
assumed to be 'decommitted', and costs are zero (if ``decommit`` is True).
:raises ValueError: if not (``scenario`` XOR (``pg`` AND ``gencost``)) is specified,
or if ``pg`` is passed and has negative values.
:return: (*pandas.DataFrame*) -- data frame of costs.
Index is hours, columns are plant IDs, values are $/hour.
"""
# Check that we've appropriately specified `scenario` XOR (`pg` AND `gencost`)
if not ((scenario is not None) ^ (pg is not None and gencost is not None)):
raise ValueError(
"Either scenario XOR (pg AND gencost) must be specified")
if scenario is not None:
_check_scenario_is_in_analyze_state(scenario)
pg = scenario.state.get_pg()
gencost = scenario.state.get_grid().gencost["before"]
else:
_check_gencost(gencost)
_check_time_series(pg, "PG")
if (pg < -1e-3).any(axis=None):
raise ValueError("PG must be non-negative")
# get ordered polynomial coefficients in columns, discarding non-coefficient data
num_coefficients = gencost["n"].iloc[0]
coefficient_columns = [f"c{i}" for i in range(num_coefficients)]
coefficients = gencost[coefficient_columns].to_numpy().T
# elementwise, evaluate polynomial where x = value
costs = polyval(pg.to_numpy(), coefficients, tensor=False)
if decommit:
# mask values where pg is 0 to 0 cost (assume uncommitted, no cost)
costs = np.where(pg.to_numpy() < decommit_threshold, 0, costs)
# Finally, convert to dataframe with shape that matches `pg`
costs = pd.DataFrame(costs, columns=pg.columns, index=pg.index)
return costs
def test_check_gencost_argument_value(mock_grid):
gencost = mock_grid.gencost["after"]
gencost_n = mock_grid.gencost["after"].copy()
gencost_n.loc[0, "n"] = 10
gencost_type = mock_grid.gencost["after"].copy()
gencost_type.loc[3, "type"] = 3
arg = (
gencost.drop(columns=["type"]),
gencost.drop(columns=["n"]),
gencost_type,
gencost_n,
)
for a in arg:
with pytest.raises(ValueError):
_check_gencost(a)
def test_check_gencost(mock_grid):
gencost = mock_grid.gencost["after"]
_check_gencost(gencost)