def test_start_off_with_initial_down_time_equal_to_min_down_time():
forward_data = pd.DataFrame({
'interval': [0, 1, 2],
'nsw-energy': [200, 200, 200]
})
p = planner.DispatchPlanner(dispatch_interval=60, planning_horizon=3)
u = units.GenericUnit(p, initial_dispatch=0.0)
u.set_service_region('energy', 'nsw')
u.add_to_market_energy_flow(100.0)
u.add_primary_energy_source(100.0)
u.add_unit_minimum_operating_level(min_loading=50.0,
shutdown_ramp_rate=100.0,
start_up_ramp_rate=100.0,
min_up_time=60,
min_down_time=120,
time_in_initial_state=120)
p.add_regional_market('nsw', 'energy', forward_data)
p.optimise()
dispatch = u.get_dispatch()
expect_dispatch = pd.DataFrame({
'interval': [0, 1, 2],
'net_dispatch': [100.0, 100.0, 100.0]
})
assert_frame_equal(expect_dispatch, dispatch)
def test_energy_storage_over_two_intervals_with_inelastic_prices():
forward_data = pd.DataFrame({'interval': [0, 1], 'nsw-energy': [100, 200]})
p = planner.DispatchPlanner(dispatch_interval=60, planning_horizon=2)
u = units.GenericUnit(p, initial_dispatch=0.0)
u.set_service_region('energy', 'nsw')
u.add_to_market_energy_flow(capacity=100.0)
u.add_from_market_energy_flow(capacity=100.0)
u.add_storage(mwh=100.0,
initial_mwh=0.0,
output_capacity=100.0,
input_capacity=100.0,
output_efficiency=1.0,
input_efficiency=1.0)
p.add_regional_market('nsw', 'energy', forecast=forward_data)
p.optimise()
dispatch = u.get_dispatch()
expect_dispatch = pd.DataFrame({
'interval': [0, 1],
'net_dispatch': [-100.0, 100.0]
})
assert_frame_equal(expect_dispatch, dispatch)
def test_energy_storage_over_three_intervals_with_elastic_prices():
historical_data = pd.DataFrame({
'interval':
np.linspace(0, 100, num=101).astype(int),
'nsw-energy':
np.linspace(0, 500, num=101),
'nsw-demand':
np.linspace(0, 500, num=101)
})
forward_data = pd.DataFrame({
'interval': [0, 1, 2],
'nsw-demand': [100.0, 400.0, 400.0]
})
f = planner.Forecaster()
f.train(historical_data,
train_sample_fraction=1.0,
target_col='nsw-energy')
price_forecast = f.price_forecast_with_generation_sensitivities(
forward_data,
region='nsw',
market='energy',
min_delta=-50,
max_delta=50,
steps=100)
p = planner.DispatchPlanner(dispatch_interval=60, planning_horizon=3)
u = units.GenericUnit(p, initial_dispatch=0.0)
u.set_service_region('energy', 'nsw')
u.add_to_market_energy_flow(capacity=50.0)
u.add_from_market_energy_flow(capacity=50.0)
u.add_storage(mwh=50.0,
initial_mwh=0.0,
output_capacity=50.0,
input_capacity=50.0,
output_efficiency=1.0,
input_efficiency=1.0)
p.add_regional_market('nsw', 'energy', forecast=price_forecast)
p.optimise()
dispatch = u.get_dispatch()
expect_dispatch = pd.DataFrame({
'interval': [0, 1, 2],
'net_dispatch': [-50.0, 25.0, 25.0]
})
assert_frame_equal(expect_dispatch, dispatch)
def test_initial_down_time_cold_start_costs_should_turn_on():
forward_data = pd.DataFrame({
'interval': [0, 1, 2, 3, 4, 5],
'nsw-energy': [200.0, 0.0, 0.0, 0.0, 0.0, 0.0]
})
p = planner.DispatchPlanner(dispatch_interval=60, planning_horizon=6)
u = units.GenericUnit(p, initial_dispatch=0.0)
u.set_service_region('energy', 'nsw')
u.add_to_market_energy_flow(100.0)
u.add_primary_energy_source(100.0, cost=100.0)
u.add_unit_minimum_operating_level(min_loading=50.0,
shutdown_ramp_rate=100.0,
start_up_ramp_rate=100.0,
min_up_time=60,
min_down_time=60,
time_in_initial_state=60 * 9)
hot_start_cost = 100 * 100 - 1
cold_start_cost = hot_start_cost * 2
u.add_startup_costs(hot_start_cost=hot_start_cost,
cold_start_cost=cold_start_cost,
time_to_go_cold=60 * 10)
p.add_regional_market('nsw', 'energy', forward_data)
p.optimise()
dispatch = u.get_dispatch()
expect_dispatch = pd.DataFrame({
'interval': [0, 1, 2, 3, 4, 5],
'net_dispatch': [100.0, 0.0, 0.0, 0.0, 0.0, 0.0]
})
assert_frame_equal(expect_dispatch, dispatch)
# Get historical price data for forecast period.
price_data = historical_inputs.get_regional_prices(start_time_forward_data,
end_time_forward_data,
raw_data_cache)
price_data = price_data.reset_index(drop=True)
price_data['interval'] = price_data.index
# Do dispatch planning assuming price taker assumption. Planner uses actual historical price data and assumes
# fleet dispatch does not impact price.
price_taker_planner = planner.DispatchPlanner(dispatch_interval=5,
planning_horizon=len(
forward_data.index))
# Add unit commitment model to dispatch planner
battery_storage = units.GenericUnit(price_taker_planner, initial_dispatch=0.0)
battery_storage.set_service_region('energy', 'nsw')
battery_storage.add_to_market_energy_flow(capacity=1000.0)
battery_storage.add_from_market_energy_flow(capacity=1000.0)
battery_storage.add_storage(mwh=4000.0,
initial_mwh=2000.0,
output_capacity=1000.0,
input_capacity=1000.0,
output_efficiency=0.9,
input_efficiency=0.9)
price_taker_planner.add_regional_market('nsw',
'energy',
forecast=baseline_forecast)
price_taker_planner.optimise()
