def test_single_cc_load(self, model):
""" Test load from dict with 'control_curve' key
This is different to the above test by using singular 'control_curve' key in the dict
"""
m = model
m.scenarios.setup()
s = Storage(m, 'Storage', max_volume=100.0)
data = {
"type": "controlcurve",
"storage_node": "Storage",
"control_curve": 0.8,
}
s.cost = p = load_parameter(model, data)
assert isinstance(p, ControlCurveParameter)
s.setup(m) # Init memory view on storage (bypasses usual `Model.setup`)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
s.initial_volume = 90.0
m.reset()
assert_allclose(s.get_cost(m.timestepper.current, si), 0)
s.initial_volume = 70.0
m.reset()
assert_allclose(s.get_cost(m.timestepper.current, si), 1)
def test_scaled_profile_nested_load(model):
""" Test `ScaledProfileParameter` loading with `AggregatedParameter` """
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link')
data = {
'type': 'scaledprofile',
'scale': 50.0,
'profile': {
'type':
'aggregated',
'agg_func':
'product',
'parameters': [{
'type': 'monthlyprofile',
'values': [0.5] * 12
}, {
'type':
'monthlyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 12, [0.7] * np.arange(12), [0.3] * 12],
'storage_node':
'Storage'
}]
}
}
l.max_flow = p = load_parameter(model, data)
p.setup(model)
# Test correct aggregation is performed
model.scenarios.setup()
s.setup(
model) # Init memory view on storage (bypasses usual `Model.setup`)
s.initial_volume = 90.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 50.0 * 0.5 * 1.0)
s.initial_volume = 70.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si),
50.0 * 0.5 * 0.7 * (mth - 1))
s.initial_volume = 30.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 50.0 * 0.5 * 0.3)
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_with_nonstorage_load(self, model):
""" Test load from dict with 'storage_node' key. """
m = model
m.scenarios.setup()
s = Storage(m, 'Storage', max_volume=100.0)
l = Link(m, 'Link')
data = {
"type": "controlcurve",
"control_curve": 0.8,
"values": [10.0, 0.0],
"storage_node": "Storage"
}
l.cost = p = load_parameter(model, data)
assert isinstance(p, ControlCurveParameter)
s.setup(m) # Init memory view on storage (bypasses usual `Model.setup`)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
print(s.volume)
assert_allclose(l.get_cost(m.timestepper.current, si), 0.0)
# When storage volume changes, the cost of the link changes.
s.initial_volume = 90.0
m.reset()
assert_allclose(l.get_cost(m.timestepper.current, si), 10.0)
def test_daily_profile_control_curve(simple_linear_model):
""" Test `DailyProfileControlCurveParameter` """
model = simple_linear_model
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link2')
data = {
'type': 'dailyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 366, [0.7] * np.arange(366), [0.3] * 366],
'storage_node': 'Storage'
}
l.max_flow = p = load_parameter(model, data)
model.setup()
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
v = s.initial_volume
doy = timestep.dayofyear
if v >= 80.0:
expected = 1.0
elif v >= 60:
expected = 0.7 * (doy - 1)
else:
expected = 0.3
return expected
for initial_volume in (90, 70, 30):
s.initial_volume = initial_volume
model.run()
def test_daily_profile_control_curve(model):
""" Test `DailyProfileControlCurveParameter` """
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link')
data = {
'type': 'dailyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0]*366, [0.7]*np.arange(366), [0.3]*366],
'storage_node': 'Storage'
}
l.max_flow = p = load_parameter(model, data)
p.setup(model)
# Test correct aggregation is performed
model.scenarios.setup()
s.setup(model) # Init memory view on storage (bypasses usual `Model.setup`)
s.initial_volume = 90.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 1.0)
s.initial_volume = 70.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
doy = ts.datetime.dayofyear
np.testing.assert_allclose(p.value(ts, si), 0.7*(doy - 1))
s.initial_volume = 30.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 0.3)
def test_scaled_profile_nested_load(model):
""" Test `ScaledProfileParameter` loading with `AggregatedParameter` """
model.timestepper.delta = 15
s = Storage(model, 'Storage', max_volume=100.0, num_outputs=0)
d = Output(model, 'Link')
data = {
'type': 'scaledprofile',
'scale': 50.0,
'profile': {
'type':
'aggregated',
'agg_func':
'product',
'parameters': [{
'type': 'monthlyprofile',
'values': [0.5] * 12
}, {
'type':
'monthlyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 12, [0.7] * np.arange(12), [0.3] * 12],
'storage_node':
'Storage'
}]
}
}
s.connect(d)
d.max_flow = p = load_parameter(model, data)
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
if s.initial_volume == 90:
return 50.0 * 0.5 * 1.0
elif s.initial_volume == 70:
return 50.0 * 0.5 * 0.7 * (timestep.month - 1)
else:
return 50.0 * 0.5 * 0.3
for initial_volume in (90, 70, 30):
s.initial_volume = initial_volume
model.run()
def test_with_nonstorage(self, model):
""" Test usage on non-`Storage` node. """
# Now test if the parameter is used on a non storage node
m = model
m.scenarios.setup()
s = Storage(m, 'Storage', max_volume=100.0)
l = Link(m, 'Link')
cc = ConstantParameter(0.8)
l.cost = ControlCurveParameter(s, cc, [10.0, 0.0])
s.setup(m) # Init memory view on storage (bypasses usual `Model.setup`)
print(s.volume)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
assert_allclose(l.get_cost(m.timestepper.current, si), 0.0)
# When storage volume changes, the cost of the link changes.
s.initial_volume = 90.0
m.reset()
print(s.volume)
assert_allclose(l.get_cost(m.timestepper.current, si), 10.0)
