def test_control_curve_interpolated(model):
m = model
m.scenarios.setup()
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
s = Storage(m, 'Storage', max_volume=100.0)
cc = ConstantParameter(0.8)
values = [20.0, 5.0, 0.0]
s.cost = ControlCurveInterpolatedParameter(s, cc, values)
s.setup(m)
for v in (0.0, 10.0, 50.0, 80.0, 90.0, 100.0):
s.initial_volume = v
s.reset()
assert_allclose(s.get_cost(m.timestepper.current, si), np.interp(v/100.0, [0.0, 0.8, 1.0], values[::-1]))
# special case when control curve is 100%
cc.update(np.array([1.0,]))
s.initial_volume == 100.0
s.reset()
assert_allclose(s.get_cost(m.timestepper.current, si), values[1])
# special case when control curve is 0%
cc.update(np.array([0.0,]))
s.initial_volume == 0.0
s.reset()
assert_allclose(s.get_cost(m.timestepper.current, si), values[0])
def test_control_curve_interpolated(model):
m = model
m.timestepper.delta = 200
s = m.nodes['Storage']
o = m.nodes['Output']
s.connect(o)
cc = ConstantParameter(model, 0.8)
values = [20.0, 5.0, 0.0]
s.cost = p = ControlCurveInterpolatedParameter(model, s, cc, values)
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
v = s.initial_volume
c = cc.value(timestep, scenario_index)
if c == 1.0 and v == 100.0:
expected = values[1]
elif c == 0.0 and v == 0.0:
expected = values[1]
else:
expected = np.interp(v / 100.0, [0.0, c, 1.0], values[::-1])
return expected
for control_curve in (0.0, 0.8, 1.0):
cc.update(np.array([
control_curve,
]))
for initial_volume in (0.0, 10.0, 50.0, 80.0, 90.0, 100.0):
s.initial_volume = initial_volume
model.run()
def test_control_curve_interpolated(model, use_parameters):
m = model
m.timestepper.delta = 200
s = m.nodes['Storage']
o = m.nodes['Output']
s.connect(o)
cc = ConstantParameter(model, 0.8)
values = [20.0, 5.0, 0.0]
if use_parameters:
# Create the parameter using parameters for the values
parameters = [ConstantParameter(model, v) for v in values]
s.cost = p = ControlCurveInterpolatedParameter(model,
s,
cc,
parameters=parameters)
else:
# Create the parameter using a list of values
s.cost = p = ControlCurveInterpolatedParameter(model, s, cc, values)
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
v = s.initial_volume
c = cc.value(timestep, scenario_index)
if c == 1.0 and v == 100.0:
expected = values[1]
elif c == 0.0 and v == 0.0:
expected = values[1]
else:
expected = np.interp(v / 100.0, [0.0, c, 1.0], values[::-1])
return expected
for control_curve in (0.0, 0.8, 1.0):
cc.set_double_variables(np.array([
control_curve,
]))
for initial_volume in (0.0, 10.0, 50.0, 80.0, 90.0, 100.0):
s.initial_volume = initial_volume
model.run()
def __init__(self,
model,
name,
storage_node,
release_values,
scenario=None,
**kwargs):
if scenario is None:
# Only one control curve should be defined. Get it explicitly
control_curves = release_values['volume'].iloc[1:-1].astype(
np.float64)
values = release_values['value'].astype(np.float64)
max_flow_param = ControlCurveInterpolatedParameter(
model, storage_node, control_curves, values)
else:
# There should be multiple control curves defined.
if release_values.shape[1] % 2 != 0:
raise ValueError(
"An even number of columns (i.e. pairs) is required for the release rules "
"when using a scenario.")
ncurves = release_values.shape[1] // 2
if ncurves != scenario.size:
raise ValueError(
f"The number of curves ({ncurves}) should equal the size of the "
f"scenario ({scenario.size}).")
curves = []
for i in range(ncurves):
volume = release_values.iloc[1:-1, i * 2]
values = release_values.iloc[:, i * 2 + 1]
control_curve = ControlCurveInterpolatedParameter(
model, storage_node, volume, values)
curves.append(control_curve)
max_flow_param = ScenarioWrapperParameter(model, scenario, curves)
self.scenario = scenario
super().__init__(model, name, max_flow=max_flow_param, **kwargs)