def test_battery_optimization_against_forecast():
model = energypylinear.Battery(power=4, capacity=6, efficiency=1.0)
info = model.optimize(prices=[10, 10, 10], forecasts=[10, 10, 10])
result = [res['Net [MW]'] for res in info]
unittest.TestCase().assertCountEqual(result, [0, 0, 0])
def test_carbon_optimization(prices, initial_charge, carbon):
power = 4
capacity = 6
model = energypylinear.Battery(power=power,
capacity=capacity,
efficiency=1.0)
p_info = model.optimize(prices=prices,
initial_charge=initial_charge,
freq="60T")
p_dispatch = [res["Actual Cost [$/60T]"] for res in p_info]
# now we optimize for carbon and see if our net cost was higher
c_info = model.optimize(
prices=prices,
carbon=carbon,
initial_charge=initial_charge,
freq="60T",
objective="carbon",
)
c_dispatch = [res["Actual Cost [$/60T]"] for res in c_info]
assert sum(c_dispatch) >= sum(p_dispatch)
def test_battery_energy_balance():
prices = np.random.uniform(-100, 100, 96)
power = 1.0
capacity = 1.0
timestep = '1hr'
efficiency = 0.9
initial_charge = 1.0
model = energypylinear.Battery(
power=power, capacity=capacity, efficiency=efficiency
)
info = model.optimize(
prices=prices, initial_charge=initial_charge, freq="60T"
)
energy_in = sum([res['Import [MW]'] for res in info])
energy_out = sum([res['Export [MW]'] for res in info])
initial_charge = info[0]['Initial Charge [MWh]']
final_charge = info[-1]['Final Charge [MWh]']
balance = energy_in - energy_out + (initial_charge - final_charge)
np.testing.assert_almost_equal(balance, 0)
def test_battery_optimization(prices, initial_charge, expected_dispatch):
power = 4
capacity = 6
mdl = energypylinear.Battery(power=power,
capacity=capacity,
efficiency=1.0)
info = mdl.optimize(prices=prices,
initial_charge=initial_charge,
freq="60T")
dispatch = [res["Net [MW]"] for res in info]
unittest.TestCase().assertCountEqual(dispatch, expected_dispatch)
def test_batt_efficiency(prices, initial_charge, efficiency,
expected_dispatch):
power = 1.0
capacity = 1.0
freq = "60T"
mdl = energypylinear.Battery(power=power,
capacity=capacity,
efficiency=efficiency)
info = mdl.optimize(prices=prices,
initial_charge=initial_charge,
freq=freq)
dispatch = [res["Net [MW]"] for res in info]
np.testing.assert_array_equal(dispatch, expected_dispatch)
def test_view_output():
"""Test that we output the expected view for a simple problem."""
prices = [10, 20, 10]
model = energypylinear.Battery(power=2, capacity=4, efficiency=1.0)
info = model.optimize(prices=prices, initial_charge=0, freq="60T")
expected_header = [
"Import [MW]",
"Export [MW]",
"Gross [MW]",
"Net [MW]",
"Losses [MW]",
"Initial Charge [MWh]",
"Final Charge [MWh]",
"Price [$/MWh]",
"Forecast Price [$/MWh]",
"Carbon Intensity [tC/MWh]",
"Actual Cost [$/60T]",
"Forecast Cost [$/60T]",
"Carbon Cost [tC/60T]",
]
csvfile = io.StringIO()
fieldnames = info[0].keys()
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for row in info:
writer.writerow(row)
csv_rows = csvfile.getvalue().split("\r\n")
for header in expected_header:
assert header in csv_rows[0]
# order of the csv columns is different than from expected_header
actual_header = [col for col in csv_rows[0].split(",")]
row_one = [col for col in csv_rows[1].split(",")]
row_two = [col for col in csv_rows[2].split(",")]
assert float(row_one[actual_header.index("Import [MW]")]) == 2.0
assert float(row_one[actual_header.index("Export [MW]")]) == 0.0
assert float(row_one[actual_header.index("Price [$/MWh]")]) == 10
assert float(row_two[actual_header.index("Import [MW]")]) == 0.0
assert float(row_two[actual_header.index("Export [MW]")]) == 2.0
assert float(row_two[actual_header.index("Price [$/MWh]")]) == 20
def test_batt_efficiency(prices, initial_charge, efficiency, expected_dispatch):
power = 1.0
capacity = 1.0
timestep = '1hr'
model = energypylinear.Battery(
power=power, capacity=capacity, efficiency=efficiency
)
info = model.optimize(
prices=prices, initial_charge=initial_charge, timestep=timestep
)
dispatch = [res['Net [MW]'] for res in info]
np.testing.assert_array_equal(
dispatch, expected_dispatch
)
def test_view_output():
"""Test that we output the expected view for a simple problem."""
prices = [10, 20, 10]
model = energypylinear.Battery(
power=2, capacity=4, efficiency=1.0
)
info = model.optimize(
prices=prices, initial_charge=0, timestep='1hr'
)
expected_header = ['Import [MW]', 'Export [MW]', 'Gross [MW]', 'Net [MW]',
'Losses [MW]', 'Initial charge [MWh]', 'Final charge [MWh]',
'Prices [$/MWh]',
'Forecast [$/MWh]', 'Actual [$/1hr]',
'Forecast [$/1hr]']
csvfile = io.StringIO()
fieldnames = info[0].keys()
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for row in info:
writer.writerow(row)
csv_rows = csvfile.getvalue().split('\r\n')
for header in expected_header:
assert(header in csv_rows[0])
# order of the csv columns is different than from expected_header
actual_header = [col for col in csv_rows[0].split(',')]
row_one = [col for col in csv_rows[1].split(',')]
row_two = [col for col in csv_rows[2].split(',')]
assert float(row_one[actual_header.index('Import [MW]')]) == 2.0
assert float(row_one[actual_header.index('Export [MW]')]) == 0.0
assert float(row_one[actual_header.index('Prices [$/MWh]')]) == 10
assert float(row_two[actual_header.index('Import [MW]')]) == 0.0
assert float(row_two[actual_header.index('Export [MW]')]) == 2.0
assert float(row_two[actual_header.index('Prices [$/MWh]')]) == 20
def test_cost_calculation(power, capacity, initial_charge, timestep):
prices = [10, 20, 30, -90, 50, 2000, -1]
model = energypylinear.Battery(power=power,
capacity=capacity,
efficiency=1.0)
info = model.optimize(prices=prices,
initial_charge=initial_charge,
timestep=timestep)
dispatch = [res['Net [MW]'] for res in info if res['Net [MW]'] is not None]
timestep = model.timestep
step = model.step
check_actual_costs = sum(
[d * p for d, p in zip(dispatch[:-1], prices[:-1])]) / step
actual = 'Actual [$/{}]'.format(timestep)
actual_costs = [res[actual] for res in info if res[actual] is not None]
assert round(check_actual_costs, 6) == round(sum(actual_costs[:-1]), 6)
def test_power_capacity_initial_charge(power, capacity, initial_charge):
prices = [10, 20, 30, 40, 10, 50]
model = energypylinear.Battery(power=power, capacity=capacity)
info = model.optimize(prices=prices, initial_charge=initial_charge)
# check we don't charge or discharge more than battery rating
dispatch = map_values(info, "Gross [MW]")
assert max(dispatch) <= power
assert min(dispatch) >= -power
# check we don't exceed battery capacity
i_charges = map_values(info, "Initial Charge [MWh]")
assert max(i_charges) <= capacity
assert min(i_charges) >= 0
f_charges = map_values(info, "Final Charge [MWh]")
assert max(f_charges) <= capacity
assert min(f_charges) >= 0
# check we set initial charge correctly
assert i_charges[0] == initial_charge
# check gross is greater or eq to net
gross = [abs(x) for x in map_values(info, "Gross [MW]")]
net = [abs(x) for x in map_values(info, "Net [MW]")]
losses = map_values(info, "Losses [MW]")
assert all([g >= n for g, n in zip(gross, net)])
# check losses are smaller or eq to export
export = map_values(info, "Export [MW]")
losses = map_values(info, "Losses [MW]")
assert all([l <= e for l, e in zip(losses, export)])
def test_carbon_optimization():
prices = [10, 50, 10, 50, 10]
forecasts = [50, 10, 50, 10, 50]
carbons = [0.1, 0.2, 0.3, 0.4, 0.5]
model = epl.Battery(power=2, capacity=4, efficiency=1.0)
price_opt = model.optimize(
prices.copy(),
forecasts=forecasts.copy(),
carbon=carbons.copy(),
freq="30T",
objective="price",
)
price_cost = sum([d["Actual Cost [$/30T]"] for d in price_opt])
forecast_opt = model.optimize(
prices.copy(),
forecasts=forecasts.copy(),
carbon=carbons.copy(),
freq="30T",
objective="carbon",
)
forecast_cost = sum([d["Actual Cost [$/30T]"] for d in forecast_opt])
carbon_opt = model.optimize(
prices.copy(),
forecasts=forecasts.copy(),
carbon=carbons.copy(),
freq="30T",
objective="carbon",
)
carbon_cost = sum([d["Actual Cost [$/30T]"] for d in carbon_opt])
assert price_cost < carbon_cost
assert forecast_cost > price_cost
