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_storage_max_volume_param(solver):
"""Test a that an max_volume with a Parameter results in the correct current_pc
"""
model = Model(solver=solver,
start=pandas.to_datetime('2016-01-01'),
end=pandas.to_datetime('2016-01-01'))
storage = Storage(model, 'storage', num_inputs=1, num_outputs=0)
otpt = Output(model, 'output', max_flow=99999, cost=-99999)
storage.connect(otpt)
p = ConstantParameter(model, 20.0)
storage.max_volume = p
storage.initial_volume = 10.0
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.5)
model.run()
p.update(np.asarray([
40.0,
]))
model.reset()
np.testing.assert_allclose(storage.current_pc, 0.25)
def test_storage_initial_volume_pc(solver):
"""Test that setting initial volume as a percentage works as expected.
"""
model = Model(solver=solver,
start=pandas.to_datetime('2016-01-01'),
end=pandas.to_datetime('2016-01-01'))
storage = Storage(model, 'storage', num_inputs=1, num_outputs=0)
otpt = Output(model, 'output', max_flow=99999, cost=-99999)
storage.connect(otpt)
p = ConstantParameter(model, 20.0)
storage.max_volume = p
storage.initial_volume_pc = 0.5
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.5)
np.testing.assert_allclose(storage.volume, 10.0)
model.run()
p.update(np.asarray([
40.0,
]))
model.reset()
np.testing.assert_allclose(storage.current_pc, 0.5)
np.testing.assert_allclose(storage.volume, 20.0)
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()
