def pmin_constraints(scenario, epsilon=1e-3):
"""Identify time periods in which generators are at minimum power.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param float epsilon: allowable 'fuzz' for whether constraint is binding.
:return: (*pandas.DataFrame*) -- boolean data frame of same shape as PG.
"""
_check_scenario_is_in_analyze_state(scenario)
_check_epsilon(epsilon)
pg = scenario.state.get_pg()
grid = scenario.state.get_grid()
pmin = grid.plant["Pmin"]
binding_pmin_constraints = (pg - pmin) <= epsilon
return binding_pmin_constraints
def ramp_constraints(scenario, epsilon=1e-3):
"""Identify time periods in which generators have binding ramp constraints.
:param powersimdata.scenario.scenario.Scenario scenario: scenario instance.
:param float epsilon: allowable 'fuzz' for whether constraint is binding.
:return: (*pandas.DataFrame*) -- boolean data frame of same shape as PG.
.. note:: The first time period will always return ``False`` for each column.
"""
_check_scenario_is_in_analyze_state(scenario)
_check_epsilon(epsilon)
pg = scenario.state.get_pg()
grid = scenario.state.get_grid()
ramp = grid.plant["ramp_30"]
diff = pg.diff(axis=0)
binding_ramp_constraints = (ramp * 2 - abs(diff)) <= epsilon
return binding_ramp_constraints
def test_bad_value(self):
with self.assertRaises(ValueError):
_check_epsilon(-0.001)
def test_zero(self):
_check_epsilon(0)
def test_bad_type(self):
with self.assertRaises(TypeError):
_check_epsilon("0.001")
def test_good_int_value(self):
_check_epsilon(1)
def test_good_float_value(self):
_check_epsilon(5e-4)
def test_check_epsilon():
_check_epsilon(1e-2)
_check_epsilon(0.001)
def test_check_epsilon_argument_value():
with pytest.raises(ValueError):
_check_epsilon(-0.00001)
def test_check_epsilon_argument_type():
arg = ("1e-3", [0.0001])
for a in arg:
with pytest.raises(TypeError):
_check_epsilon()