def test_tc_modelchain_with_power_curve_as_dict(self):
"""Test power curves as dict in TurbineClusterModelChain.run_model()"""
my_turbine = {
'nominal_power': 3e6,
'hub_height': 105,
'power_curve': {
'value':
[p * 1000 for p in [0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],
'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]
}
}
my_farm = {
'wind_turbine_fleet': [{
'wind_turbine': wt.WindTurbine(**my_turbine),
'number_of_turbines': 3
}, {
'wind_turbine':
wt.WindTurbine(**self.test_turbine),
'number_of_turbines':
3
}]
}
my_cluster = {
'wind_farms':
[wf.WindFarm(**my_farm),
wf.WindFarm(**self.test_farm)]
}
power_output_exp = pd.Series(
data=[10853277.966972714, 21731814.593688786],
name='feedin_power_plant')
# run model with my_cluster
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wtc.WindTurbineCluster(**my_cluster))
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
def test_error_raising(self):
# Raise ValueError when aggregated wind farm power curve needs to be
# calculated but turbine does not have a power curve
test_turbine_data = {
"hub_height": 100,
"rotor_diameter": 98,
"turbine_type": "V90/2000",
}
test_turbine = wt.WindTurbine(**test_turbine_data)
test_turbine.power_curve = True
test_farm = {
"wind_turbine_fleet": [
{
"wind_turbine": wt.WindTurbine(**self.test_turbine),
"number_of_turbines": 3,
},
{
"wind_turbine": test_turbine,
"number_of_turbines": 3
},
]
}
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=wf.WindFarm(
**test_farm))
with pytest.raises(ValueError):
test_tc_mc.run_model(self.weather_df)
# Raise ValueError when neither turbulence intensity nor roughness
# length are provided to apply power curve smoothing with standard
# deviation method 'turbulence_intensity'
parameters = {
"smoothing": True,
"standard_deviation_method": "turbulence_intensity",
}
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wf.WindFarm(**self.test_farm), **parameters)
weather_df = self.weather_df.copy()
weather_df.pop("roughness_length")
with pytest.raises(ValueError):
test_tc_mc.run_model(weather_df)
def test_error_raising(self):
# Raise ValueError when aggregated wind farm power curve needs to be
# calculated but turbine does not have a power curve
test_turbine_data = {
'hub_height': 100,
'rotor_diameter': 98,
'turbine_type': 'V90/2000'
}
test_turbine = wt.WindTurbine(**test_turbine_data)
test_turbine.power_curve = True
test_farm = {
'wind_turbine_fleet': [{
'wind_turbine':
wt.WindTurbine(**self.test_turbine),
'number_of_turbines':
3
}, {
'wind_turbine': test_turbine,
'number_of_turbines': 3
}]
}
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=wf.WindFarm(
**test_farm))
with pytest.raises(ValueError):
test_tc_mc.run_model(self.weather_df)
# Raise ValueError when neither turbulence intensity nor roughness
# length are provided to apply power curve smoothing with standard
# deviation method 'turbulence_intensity'
parameters = {
'smoothing': True,
'standard_deviation_method': 'turbulence_intensity'
}
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wf.WindFarm(**self.test_farm), **parameters)
weather_df = self.weather_df.copy()
weather_df.pop('roughness_length')
with pytest.raises(ValueError):
test_tc_mc.run_model(weather_df)
def test_ignore_wake_losses(self):
"""Run model without wake losses."""
parameters = {
'wake_losses_model': None,
'smoothing': False,
'standard_deviation_method': 'turbulence_intensity',
'smoothing_order': 'wind_farm_power_curves'
}
# Test modelchain with default values
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wtc.WindTurbineCluster(**self.test_cluster),
**parameters)
test_tc_mc.run_model(self.weather_df)
def test_heigths_as_string(self):
"""Test run_model if data heights are of type string."""
# Convert data heights to str
string_weather = self.weather_df.copy()
string_weather.columns = pd.MultiIndex.from_arrays([
string_weather.columns.get_level_values(0),
string_weather.columns.get_level_values(1).astype(str),
])
# Heights in the original DataFrame are of type np.int64
assert isinstance(
self.weather_df.columns.get_level_values(1)[0], np.int64)
assert isinstance(string_weather.columns.get_level_values(1)[0], str)
test_mc = tc_mc.TurbineClusterModelChain(
power_plant=wtc.WindTurbineCluster(**self.test_cluster))
test_mc.run_model(string_weather)
def test_tc_modelchain_with_power_curve_as_dict(self):
"""Test power curves as dict in TurbineClusterModelChain.run_model()"""
my_turbine = {
"nominal_power": 3e6,
"hub_height": 105,
"power_curve": {
"value":
[p * 1000 for p in [0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],
"wind_speed": [0.0, 3.0, 5.0, 10.0, 15.0, 25.0],
},
}
my_farm = {
"wind_turbine_fleet": [
{
"wind_turbine": wt.WindTurbine(**my_turbine),
"number_of_turbines": 3,
},
{
"wind_turbine": wt.WindTurbine(**self.test_turbine),
"number_of_turbines": 3,
},
]
}
my_cluster = {
"wind_farms": [
wf.WindFarm(**my_farm),
wf.WindFarm(**self.test_farm),
]
}
power_output_exp = pd.Series(
data=[10853277.966972714, 21731814.593688786],
name="feedin_power_plant",
)
# run model with my_cluster
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wtc.WindTurbineCluster(**my_cluster))
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
def test_run_model(self):
parameters = {
'wake_losses_model': 'dena_mean',
'smoothing': False,
'standard_deviation_method': 'turbulence_intensity',
'smoothing_order': 'wind_farm_power_curves'
}
# Test modelchain with default values
power_output_exp = pd.Series(
data=[4198361.4830405945, 8697966.121234536],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wf.WindFarm(**self.test_farm), **parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test constant efficiency
parameters['wake_losses_model'] = 'wind_farm_efficiency'
test_wind_farm = wf.WindFarm(**self.test_farm)
test_wind_farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[4420994.806920091, 8516983.651623568],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_wind_farm,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test smoothing
parameters['smoothing'] = 'True'
test_wind_farm = wf.WindFarm(**self.test_farm)
test_wind_farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[4581109.03847444, 8145581.914240712],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_wind_farm,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test wind farm with different turbine types (smoothing)
test_wind_farm = wf.WindFarm(**self.test_farm_2)
test_wind_farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[6777087.9658657005, 12180374.036660176],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_wind_farm,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test other smoothing order
parameters['smoothing_order'] = 'turbine_power_curves'
test_wind_farm = wf.WindFarm(**self.test_farm_2)
test_wind_farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[6790706.001026006, 12179417.461328149],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_wind_farm,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
def test_run_model_turbine_cluster(self):
parameters = {
'wake_losses_model': 'dena_mean',
'smoothing': False,
'standard_deviation_method': 'turbulence_intensity',
'smoothing_order': 'wind_farm_power_curves'
}
# Test modelchain with default values
power_output_exp = pd.Series(
data=[10363047.755401008, 21694496.68221325],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(
power_plant=wtc.WindTurbineCluster(**self.test_cluster),
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test constant efficiency
parameters['wake_losses_model'] = 'wind_farm_efficiency'
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
for farm in test_cluster.wind_farms:
farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[10920128.570572512, 21273144.336885825],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_cluster,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test smoothing
parameters['smoothing'] = 'True'
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
for farm in test_cluster.wind_farms:
farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[11360309.77979467, 20328652.64490018],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_cluster,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test wind farm with different turbine types (smoothing)
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
for farm in test_cluster.wind_farms:
farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[11360309.77979467, 20328652.64490018],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_cluster,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)
# Test other smoothing order
parameters['smoothing_order'] = 'turbine_power_curves'
test_cluster = wtc.WindTurbineCluster(**self.test_cluster)
for farm in test_cluster.wind_farms:
farm.efficiency = 0.9
power_output_exp = pd.Series(
data=[11373183.797085874, 20325877.105744187],
name='feedin_power_plant')
test_tc_mc = tc_mc.TurbineClusterModelChain(power_plant=test_cluster,
**parameters)
test_tc_mc.run_model(self.weather_df)
assert_series_equal(test_tc_mc.power_output, power_output_exp)