def test_create_loss_function_bass_link():
# Interconnector flow
flow = -433.0
# Arbitrary demand inputs
nsw_demand = 6000.0
qld_demand = 7000.0
demand = pd.DataFrame({
'region': ['VIC1', 'QLD1'],
'loss_function_demand': [nsw_demand, qld_demand]
})
# Loss model details from 2020 Jan NEM web files.
demand_coefficients = pd.DataFrame({
'interconnector': ['BL'],
'region': ['VIC1'],
'demand_coefficient': [0.0]
})
# Loss model details from 2020 Jan NEM web files.
interconnector_coefficients = pd.DataFrame({
'interconnector': ['BL'],
'loss_constant': [0.99608],
'flow_coefficient': [0.00020786],
'from_region_loss_share': [0.5]
})
loss_function = interconnectors.create_loss_functions(
interconnector_coefficients, demand_coefficients, demand)
output_losses = loss_function['loss_function'].loc[0](flow)
expected_losses = (-3.92E-3) * flow + (1.0393E-4) * flow**2
assert (pytest.approx(expected_losses, 0.0001) == output_losses)
def test_create_loss_function_vic_nsw():
# Interconnector flow
flow = 600.0
# Arbitrary demand inputs
vic_demand = 6000.0
nsw_demand = 7000.0
sa_demand = 3000.0
demand = pd.DataFrame({
'region': ['NSW1', 'VIC1', 'SA1'],
'loss_function_demand': [nsw_demand, vic_demand, sa_demand]
})
# Loss model details from 2020 Jan NEM web files.
demand_coefficients = pd.DataFrame({
'interconnector': ['VIC1-NSW1', 'VIC1-NSW1', 'VIC1-NSW1'],
'region': ['NSW1', 'VIC1', 'SA1'],
'demand_coefficient': [0.000021734, -0.000031523, -0.000065967]
})
# Loss model details from 2020 Jan NEM web files.
interconnector_coefficients = pd.DataFrame({
'interconnector': ['VIC1-NSW1'],
'loss_constant': [1.0657],
'flow_coefficient': [0.00017027],
'from_region_loss_share': [0.5]
})
loss_function = interconnectors.create_loss_functions(
interconnector_coefficients, demand_coefficients, demand)
output_losses = loss_function['loss_function'].loc[0](flow)
expected_losses = (0.0657 - 3.1523E-05 * vic_demand +
2.1734E-05 * nsw_demand -
6.5967E-05 * sa_demand) * flow + 8.5133E-05 * flow**2
assert (pytest.approx(expected_losses, 0.0001) == output_losses)
def test_create_loss_function():
# Interconnector flow
flow = 600.0
# Arbitrary demand inputs
nsw_demand = 7000.0
qld_demand = 5000.0
demand = pd.DataFrame({
'region': ['NSW1', 'QLD1'],
'loss_function_demand': [nsw_demand, qld_demand]
})
# Loss model details from 2020 Jan NEM web files.
demand_coefficients = pd.DataFrame({
'interconnector': ['NSW1-QLD1', 'NSW1-QLD1'],
'region': ['NSW1', 'QLD1'],
'demand_coefficient': [-0.00000035146, 0.000010044]
})
# Loss model details from 2020 Jan NEM web files.
interconnector_coefficients = pd.DataFrame({
'interconnector': ['NSW1-QLD1'],
'loss_constant': [0.9529],
'flow_coefficient': [0.00019617],
'from_region_loss_share': [0.5]
})
loss_function = interconnectors.create_loss_functions(
interconnector_coefficients, demand_coefficients, demand)
output_losses = loss_function['loss_function'].loc[0](flow)
expected_losses = (-0.0471 - 3.5146E-07 * nsw_demand +
1.0044E-05 * qld_demand) * flow + 9.8083E-05 * flow**2
assert (pytest.approx(expected_losses, 0.0001) == output_losses)
'interconnector': ['VIC1-NSW1', 'VIC1-NSW1'],
'region': ['NSW1', 'VIC1'],
'demand_coefficient': [0.000021734, -0.000031523]
})
# Specify the loss function constant and flow coefficient.
interconnector_coefficients = pd.DataFrame({
'interconnector': ['VIC1-NSW1'],
'loss_constant': [1.0657],
'flow_coefficient': [0.00017027],
'from_region_loss_share': [0.5]
})
# Create loss functions on per interconnector basis.
loss_functions = interconnector_inputs.create_loss_functions(
interconnector_coefficients, demand_coefficients,
demand.loc[:, ['region', 'loss_function_demand']])
# The points to linearly interpolate the loss function between.
interpolation_break_points = pd.DataFrame({
'interconnector':
'VIC1-NSW1',
'loss_segment': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
'break_point': [
-1200.0, -1000.0, -800.0, -600.0, -400.0, -200.0, 0.0, 200.0, 400.0,
600.0, 800.0, 1000
]
})
market.set_interconnector_losses(loss_functions, interpolation_break_points)