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()