def test_power_curve_7(self):
"""TODO: Explain this test"""
self.parameters2["power_curve_values"] = np.array(
self.parameters2["power_curve_values"])
self.parameters2["power_curve_wind_speeds"] = np.array(
self.parameters2["power_curve_wind_speeds"])
assert_allclose(power_curve(**self.parameters2),
self.power_output_exp2)
assert isinstance(power_curve(**self.parameters2), np.ndarray)
def test_power_curve_3(self):
"""
Test wind_speed as np.array and power_curve as
pd.Series and np.array
"""
power_output_exp = np.array(self.power_output_exp1)
self.parameters2["wind_speed"] = np.array(
self.parameters2["wind_speed"])
assert_allclose(power_curve(**self.parameters2), power_output_exp)
assert isinstance(power_curve(**self.parameters2), np.ndarray)
def test_power_curve_5(self):
"""
Tests with density correction:
Test wind_speed as np.array with density and power_curve as pd.Series
and np.array
"""
power_output_exp = np.array(self.power_output_exp2)
self.parameters2["density_correction"] = True
assert_allclose(power_curve(**self.parameters2), power_output_exp)
assert isinstance(power_curve(**self.parameters2), np.ndarray)
def test_wrong_spelling_density_correction(self):
parameters = {
"wind_speed": pd.Series(data=[2.0, 5.5, 7.0]),
"density": pd.Series(data=[1.3, 1.3, 1.3]),
"power_curve_wind_speeds": pd.Series([4.0, 5.0, 6.0]),
"power_curve_values": pd.Series([300, 400, 500]),
}
msg = "is an invalid type. `density_correction` must be Boolean"
with pytest.raises(TypeError, match=msg):
parameters["density_correction"] = None
power_curve(**parameters)
def check_theoretical_power(df, year, start=None, end=None):
r"""
This function was used to compare the theoretical power of ArgeNetz wind
farms with the simulated power when the measured wind speed (of ArgeNetz
data) is used.
As no wind speed is added to the data of 2015 this function can only be
used for the year 2015.
"""
wind_farm_names = ['wf_1', 'wf_3', 'wf_4', 'wf_5']
wind_turbine_amount = [(0, 16), (4, 13), (0, 22), (0, 14)]
# Turbine data
enerconE70 = {
'turbine_name': 'ENERCON E 70 2300',
'hub_height': 64, # in m
'rotor_diameter': 71 # in m
}
enerconE66 = {
'turbine_name': 'ENERCON E 66 1800',
'hub_height': 65, # in m
'rotor_diameter': 70 # in m
}
# Initialize WindTurbine objects
e70 = wt.WindTurbine(**enerconE70)
e66 = wt.WindTurbine(**enerconE66)
for name, turbine_amount in zip(wind_farm_names, wind_turbine_amount):
indices = tools.get_indices_for_series(1, 'Europe/Berlin', year)
power_output_theo = df[name + '_theoretical_power'] / 1000
power_output_theo = pd.Series(data=power_output_theo.values,
index=indices)
power_output_by_wind_speed = (
turbine_amount[0] * power_output.power_curve(
df[name + '_wind_speed'], e66.power_curve['wind_speed'],
e66.power_curve['power']) +
turbine_amount[1] * power_output.power_curve(
df[name + '_wind_speed'], e70.power_curve['wind_speed'],
e70.power_curve['power'])) / (1*10**6)
power_output_by_wind_speed = pd.Series(
data=power_output_by_wind_speed.values, index=indices)
val_obj = analysis_tools.ValidationObject(
'validate_arge_4919', power_output_theo,
power_output_by_wind_speed,
weather_data_name='calculated by wind speed',
validation_name='P_W theoretical')
val_obj.output_method = 'power_output'
visualization_tools.plot_feedin_comparison(
val_obj, filename='../Plots/Test_Arge/{0}_{1}_feedin'.format(
year, name),
title='{0}'.format(name), start=start, end=end)
visualization_tools.plot_correlation(
val_obj, filename='../Plots/Test_Arge/{0}_{1}_corr'.format(
year, name),
title='{0}'.format(name))
def test_power_curve_density_correction(self):
"""TODO: Explain and split this test."""
parameters = {
"wind_speed": pd.Series(data=[2.0, 5.5, 7.0]),
"density": pd.Series(data=[1.3, 1.3, 1.3]),
"power_curve_wind_speeds": pd.Series([4.0, 5.0, 6.0]),
"power_curve_values": pd.Series([300, 400, 500]),
}
# Test wind_speed as pd.Series with density and power_curve as
# pd.Series and np.array
power_output_exp = pd.Series(data=[0.0, 461.00290572, 0.0],
name="feedin_power_plant")
assert_series_equal(power_curve_density_correction(**parameters),
power_output_exp)
parameters["density"] = np.array(parameters["density"])
assert_series_equal(power_curve_density_correction(**parameters),
power_output_exp)
parameters["power_curve_values"] = np.array(
parameters["power_curve_values"])
parameters["power_curve_wind_speeds"] = np.array(
parameters["power_curve_wind_speeds"])
assert_series_equal(power_curve_density_correction(**parameters),
power_output_exp)
# Test wind_speed as np.array with density and power_curve as np.array
# and pd.Series
parameters["wind_speed"] = np.array(parameters["wind_speed"])
power_output_exp = np.array([0.0, 461.00290572, 0.0])
assert_allclose(power_curve_density_correction(**parameters),
power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters["density"] = pd.Series(data=parameters["density"])
assert_allclose(power_curve_density_correction(**parameters),
power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters["power_curve_wind_speeds"] = pd.Series(
data=parameters["power_curve_wind_speeds"])
parameters["power_curve_values"] = pd.Series(
data=parameters["power_curve_values"])
assert_allclose(power_curve_density_correction(**parameters),
power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
# Raise TypeError due to density is None
with pytest.raises(TypeError):
parameters["density"] = None
power_curve_density_correction(**parameters)
def test_power_curve_density_correction(self):
parameters = {'wind_speed': pd.Series(data=[2.0, 5.5, 7.0]),
'density': pd.Series(data=[1.3, 1.3, 1.3]),
'power_curve_wind_speeds':
pd.Series([4.0, 5.0, 6.0]),
'power_curve_values':
pd.Series([300, 400, 500])
}
# Test wind_speed as pd.Series with density and power_curve as
# pd.Series and np.array
power_output_exp = pd.Series(data=[0.0, 461.00290572, 0.0],
name='feedin_power_plant')
assert_series_equal(power_curve_density_correction(**parameters),
power_output_exp)
parameters['density'] = np.array(parameters['density'])
assert_series_equal(power_curve_density_correction(**parameters),
power_output_exp)
parameters['power_curve_values'] = np.array(
parameters['power_curve_values'])
parameters['power_curve_wind_speeds'] = np.array(
parameters['power_curve_wind_speeds'])
assert_series_equal(power_curve_density_correction(**parameters),
power_output_exp)
# Test wind_speed as np.array with density and power_curve as np.array
# and pd.Series
parameters['wind_speed'] = np.array(parameters['wind_speed'])
power_output_exp = np.array([0.0, 461.00290572, 0.0])
assert_allclose(power_curve_density_correction(**parameters),
power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters['density'] = pd.Series(data=parameters['density'])
assert_allclose(power_curve_density_correction(**parameters),
power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters['power_curve_wind_speeds'] = pd.Series(
data=parameters['power_curve_wind_speeds'])
parameters['power_curve_values'] = pd.Series(
data=parameters['power_curve_values'])
assert_allclose(power_curve_density_correction(**parameters),
power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
# Raise TypeError due to density is None
with pytest.raises(TypeError):
parameters['density'] = None
power_curve_density_correction(**parameters)
def test_power_curve_1(self):
# Tests without density correction:
# Test wind_speed as pd.Series and power_curve as pd.Series and
# np.array
assert_series_equal(power_curve(**self.parameters2),
self.power_output_exp1)
def test_power_curve_2(self):
"""TODO: Explain this test"""
self.parameters2["power_curve_values"] = np.array(
self.parameters2["power_curve_values"])
self.parameters2["power_curve_wind_speeds"] = np.array(
self.parameters2["power_curve_wind_speeds"])
assert_series_equal(power_curve(**self.parameters2),
self.power_output_exp1)
def test_power_curve_10(self):
"""TODO: Explain this test"""
self.parameters2["power_curve_wind_speeds"] = pd.Series(
data=self.parameters2["power_curve_wind_speeds"])
self.parameters2["power_curve_values"] = pd.Series(
data=self.parameters2["power_curve_values"])
assert_series_equal(power_curve(**self.parameters2),
self.power_output_exp2)
def test_power_curve_8(self):
"""
Test wind_speed as pd.Series with density and power_curve as np. array
and pd.Series
"""
self.parameters2["wind_speed"] = pd.Series(
data=self.parameters2["wind_speed"])
assert_series_equal(power_curve(**self.parameters2),
self.power_output_exp2)
def calculate_power_output(self, wind_speed_hub, density_hub):
r"""
Calculates the power output of the wind power plant.
The method specified by the parameter `power_output_model` is used.
Parameters
----------
wind_speed_hub : :pandas:`pandas.Series<series>` or numpy.array
Wind speed at hub height in m/s.
density_hub : :pandas:`pandas.Series<series>` or numpy.array
Density of air at hub height in kg/m³.
Returns
-------
:pandas:`pandas.Series<series>`
Electrical power output of the wind turbine in W.
"""
if self.power_output_model == "power_curve":
if self.power_plant.power_curve is None:
raise TypeError("Power curve values of {} are missing.".format(
self.power_plant))
logging.debug("Calculating power output using power curve.")
return power_output.power_curve(
wind_speed_hub,
self.power_plant.power_curve["wind_speed"],
self.power_plant.power_curve["value"],
density_hub,
self.density_correction,
)
elif self.power_output_model == "power_coefficient_curve":
if self.power_plant.power_coefficient_curve is None:
raise TypeError("Power coefficient curve values of {} are "
"missing.".format(self.power_plant))
logging.debug("Calculating power output using power coefficient "
"curve.")
return power_output.power_coefficient_curve(
wind_speed_hub,
self.power_plant.power_coefficient_curve["wind_speed"],
self.power_plant.power_coefficient_curve["value"],
self.power_plant.rotor_diameter,
density_hub,
)
else:
raise ValueError(
"'{0}' is an invalid value. ".format(self.power_output_model) +
"`power_output_model` must be " +
"'power_curve' or 'power_coefficient_curve'.")
def power_output_simple(wind_turbine_fleet, weather_df, data_height):
# TODO: add weather_data_name to these functions and use modelchain for
# open_FRED data as there are different heights and this is easier
# with the multiindex dataframe
r"""
Calculate power output of several wind turbines by a simple method.
Simplest way to calculate the power output of a wind farm or other
gathering of wind turbines. For the power_output of the single turbines
a power_curve without density correction is used. The wind speed at hub
height is calculated by the logarithmic wind profile. The power output of
the wind farm is calculated by aggregation of the power output of the
single turbines.
Parameters
----------
wind_turbine_fleet : List of Dictionaries
Wind turbines of wind farm. Dictionaries must have 'wind_turbine'
(contains wind turbine object) and 'number_of_turbines' (number of
turbine type in wind farm) as keys.
weather_df : pandas.DataFrame
DataFrame with time series for wind speed `wind_speed` in m/s and
roughness length `roughness_length` in m.
data_height : Dictionary
Contains the heights of the weather measurements or weather
model in meters with the keys of the data parameter.
Returns
-------
pd.Series
Simulated power output of wind farm.
"""
for turbine_type in wind_turbine_fleet:
wind_speed_hub = wind_speed.logarithmic_profile(
weather_df.wind_speed,
data_height['wind_speed'],
turbine_type['wind_turbine'].hub_height,
weather_df.roughness_length,
obstacle_height=0.0)
turbine_type['wind_turbine'].power_output = (power_output.power_curve(
wind_speed_hub,
turbine_type['wind_turbine'].power_curve['wind_speed'],
turbine_type['wind_turbine'].power_curve['power']))
return power_output_simple_aggregation(wind_turbine_fleet)
def calculate_power_output(self, wind_speed_hub, density_hub):
r"""
Calculates the power output of the wind power plant.
The method specified by the parameter `power_output_model` is used.
Parameters
----------
wind_speed_hub : pandas.Series or numpy.array
Wind speed at hub height in m/s.
density_hub : pandas.Series or numpy.array
Density of air at hub height in kg/m³.
Returns
-------
pandas.Series
Electrical power output of the wind turbine in W.
"""
if self.power_output_model == 'power_curve':
if self.power_plant.power_curve is None:
raise TypeError("Power curve values of " +
self.power_plant.name + " are missing.")
logging.debug('Calculating power output using power curve.')
return (power_output.power_curve(
wind_speed_hub, self.power_plant.power_curve['wind_speed'],
self.power_plant.power_curve['value'], density_hub,
self.density_correction))
elif self.power_output_model == 'power_coefficient_curve':
if self.power_plant.power_coefficient_curve is None:
raise TypeError("Power coefficient curve values of " +
self.power_plant.name + " are missing.")
logging.debug('Calculating power output using power coefficient '
'curve.')
return (power_output.power_coefficient_curve(
wind_speed_hub,
self.power_plant.power_coefficient_curve['wind_speed'],
self.power_plant.power_coefficient_curve['value'],
self.power_plant.rotor_diameter, density_hub))
else:
raise ValueError(
"'{0}' is an invalid value. ".format(self.power_output_model) +
"`power_output_model` must be " +
"'power_curve' or 'power_coefficient_curve'.")
def test_power_curve(self):
parameters = {'wind_speed': pd.Series(data=[2.0, 5.5, 7.0]),
'density': pd.Series(data=[1.3, 1.3, 1.3]),
'density_correction': False,
'power_curve_wind_speeds':
pd.Series([4.0, 5.0, 6.0]),
'power_curve_values':
pd.Series([300, 400, 500])
}
# Tests without density correction:
# Test wind_speed as pd.Series and power_curve as pd.Series and
# np.array
power_output_exp = pd.Series(data=[0.0, 450.0, 0.0],
name='feedin_power_plant')
assert_series_equal(power_curve(**parameters), power_output_exp)
parameters['power_curve_values'] = np.array(
parameters['power_curve_values'])
parameters['power_curve_wind_speeds'] = np.array(
parameters['power_curve_wind_speeds'])
assert_series_equal(power_curve(**parameters),
power_output_exp)
# Test wind_speed as np.array and power_curve as pd.Series and np.array
power_output_exp = np.array([0.0, 450.0, 0.0])
parameters['wind_speed'] = np.array(parameters['wind_speed'])
assert_allclose(power_curve(**parameters), power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters['power_curve_wind_speeds'] = pd.Series(
data=parameters['power_curve_wind_speeds'])
parameters['power_curve_values'] = pd.Series(
data=parameters['power_curve_values'])
assert_allclose(power_curve(**parameters), power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
# Tests with density correction:
# Test wind_speed as np.array with density and power_curve as pd.Series
# and np.array
power_output_exp = np.array([0.0, 461.00290572, 0.0])
parameters['density_correction'] = True
assert_allclose(power_curve(**parameters), power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters['density'] = np.array(parameters['density'])
assert_allclose(power_curve(**parameters), power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
parameters['power_curve_values'] = np.array(
parameters['power_curve_values'])
parameters['power_curve_wind_speeds'] = np.array(
parameters['power_curve_wind_speeds'])
assert_allclose(power_curve(**parameters), power_output_exp)
assert isinstance(power_curve(**parameters), np.ndarray)
# Test wind_speed as pd.Series with density and power_curve as
# np. array and pd.Series
power_output_exp = pd.Series(data=[0.0, 461.00290572, 0.0],
name='feedin_power_plant')
parameters['wind_speed'] = pd.Series(data=parameters['wind_speed'])
assert_series_equal(power_curve(**parameters), power_output_exp)
parameters['density'] = pd.Series(data=parameters['density'])
assert_series_equal(power_curve(**parameters),
power_output_exp)
parameters['power_curve_wind_speeds'] = pd.Series(
data=parameters['power_curve_wind_speeds'])
parameters['power_curve_values'] = pd.Series(
data=parameters['power_curve_values'])
assert_series_equal(power_curve(**parameters),
power_output_exp)
# Raise TypeErrors due to wrong type of `density_correction`
with pytest.raises(TypeError):
parameters['density'] = 'wrong_type'
power_curve(**parameters)
def test_power_curve_6(self):
"""TODO: Explain this test"""
self.parameters2["density"] = np.array(self.parameters2["density"])
assert_allclose(power_curve(**self.parameters2),
self.power_output_exp2)
assert isinstance(power_curve(**self.parameters2), np.ndarray)
def test_power_curve_9(self):
"""TODO: Explain this test"""
self.parameters2["density"] = pd.Series(
data=self.parameters2["density"])
assert_series_equal(power_curve(**self.parameters2),
self.power_output_exp2)