def initialize_wind_farms(my_turbine, e126):
r"""
Initializes two :class:`~.wind_farm.WindFarm` objects.
This function shows how to initialize a WindFarm object. A WindFarm needs
a wind turbine fleet specifying the wind turbines and their number or
total installed capacity (in Watt) in the farm. Optionally, you can provide
a wind farm efficiency (which can be constant or dependent on the wind
speed) and a name as an identifier. See :class:`~.wind_farm.WindFarm` for
more information.
Parameters
----------
my_turbine : :class:`~.wind_turbine.WindTurbine`
WindTurbine object with self provided power curve.
e126 : :class:`~.wind_turbine.WindTurbine`
WindTurbine object with power curve from the OpenEnergy Database
turbine library.
Returns
-------
tuple(:class:`~.wind_farm.WindFarm`, :class:`~.wind_farm.WindFarm`)
"""
# specification of wind farm data where turbine fleet is provided in a
# pandas.DataFrame
# for each turbine type you can either specify the number of turbines of
# that type in the wind farm (float values are possible as well) or the
# total installed capacity of that turbine type in W
wind_turbine_fleet = pd.DataFrame(
{
"wind_turbine": [my_turbine, e126], # as windpowerlib.WindTurbine
"number_of_turbines": [6, None],
"total_capacity": [None, 12.6e6],
}
)
# initialize WindFarm object
example_farm = WindFarm(
name="example_farm", wind_turbine_fleet=wind_turbine_fleet
)
# specification of wind farm data (2) containing a wind farm efficiency
# wind turbine fleet is provided using the to_group function
example_farm_2_data = {
"name": "example_farm_2",
"wind_turbine_fleet": [
my_turbine.to_group(6),
e126.to_group(total_capacity=12.6e6),
],
"efficiency": 0.9,
}
# initialize WindFarm object
example_farm_2 = WindFarm(**example_farm_2_data)
return example_farm, example_farm_2
def test_wind_farm_efficiency_with_missing_efficiency(self):
"""Test WindFarm.assign_power_curve() with missing efficiency while
`wake_losses_model` is 'wind_farm_efficiency'."""
wind_turbine_fleet = [{
"wind_turbine": WindTurbine(**self.test_turbine),
"number_of_turbines": 3,
}]
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
msg = "If you use `wake_losses_model`"
with pytest.raises(ValueError, match=msg):
windfarm.assign_power_curve()
def test_repr(self):
"""Test string representation of WindFarm"""
test_fleet = [{
'wind_turbine': WindTurbine(**self.test_turbine),
'number_of_turbines': 2
}]
assert 'E-126/4200' in repr(WindFarm(wind_turbine_fleet=test_fleet))
def test_repr(self):
"""Test string representation of WindFarm"""
test_fleet = [{
"wind_turbine": WindTurbine(**self.test_turbine),
"number_of_turbines": 2,
}]
assert "E-126/4200" in repr(WindFarm(wind_turbine_fleet=test_fleet))
def test_initialization_4(self, recwarn):
"""test overwriting and raising warning when number_of_turbines and
total_capacity in wind turbine fleet do not fit"""
wt1 = WindTurbine(**self.test_turbine)
wt2 = WindTurbine(**self.test_turbine_2)
wind_turbine_fleet = pd.DataFrame(
data={
"wind_turbine": [wt1, wt2],
"number_of_turbines": [3, 2],
"total_capacity": [3, np.nan],
},
index=[0, 1],
)
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
total_cap_wt1_expected = (
wt1.nominal_power *
wind_turbine_fleet.loc[0, "number_of_turbines"])
assert (windfarm.wind_turbine_fleet.loc[0, "total_capacity"] ==
total_cap_wt1_expected)
total_cap_wt2_expected = (
wt2.nominal_power *
wind_turbine_fleet.loc[1, "number_of_turbines"])
assert (windfarm.wind_turbine_fleet.loc[1, "total_capacity"] ==
total_cap_wt2_expected)
assert recwarn.pop(WindpowerlibUserWarning)
def test_mean_hub_height(self):
"""tests mean_hub_height method"""
test_farm = {
"wind_turbine_fleet": [
{
"wind_turbine": WindTurbine(**self.test_turbine),
"number_of_turbines": 2,
},
{
"wind_turbine": WindTurbine(**self.test_turbine_2),
"total_capacity": 3e6,
},
]
}
windfarm = WindFarm(**test_farm)
assert 97.265 == pytest.approx(windfarm.mean_hub_height().hub_height,
1e-3)
def test_aggregation_of_power_curve_with_missing_power_curve(self):
"""Test WindFarm.assign_power_curve() with missing power_curve."""
wt1 = WindTurbine(**self.test_turbine)
wt1.power_curve = None
wind_turbine_fleet = [{
'wind_turbine': wt1,
'number_of_turbines': 3
}, {
'wind_turbine':
WindTurbine(**self.test_turbine_2),
'number_of_turbines':
2
}]
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
msg = 'For an aggregated wind farm power curve each wind'
with pytest.raises(ValueError, match=msg):
windfarm.assign_power_curve()
def test_mean_hub_height(self):
"""tests mean_hub_height method"""
test_farm = {
'wind_turbine_fleet': [{
'wind_turbine':
WindTurbine(**self.test_turbine),
'number_of_turbines':
2
}, {
'wind_turbine':
WindTurbine(**self.test_turbine_2),
'total_capacity':
3e6
}]
}
windfarm = WindFarm(**test_farm)
assert 97.265 == pytest.approx(windfarm.mean_hub_height().hub_height,
1e-3)
def test_initialization_1(self):
"""test catching error when wind_turbine_fleet not provided as list"""
msg = 'Wind turbine must be provided as WindTurbine object'
with pytest.raises(ValueError, match=msg):
WindFarm(wind_turbine_fleet={
'wind_turbine': 'turbine',
'number_of_turbines': 2
},
name='dummy')
def test_initialization_1(self):
"""test catching error when wind_turbine_fleet not provided as list"""
msg = "Wind turbine must be provided as WindTurbine object"
with pytest.raises(ValueError, match=msg):
WindFarm(
wind_turbine_fleet={
"wind_turbine": "turbine",
"number_of_turbines": 2,
},
name="dummy",
)
def test_initialization_dataframe(self):
"""test simple initialization with wind turbine fleet dataframe"""
wind_turbine_fleet = pd.DataFrame(
data={
'wind_turbine': [
WindTurbine(**self.test_turbine),
WindTurbine(**self.test_turbine_2)
],
'number_of_turbines': [3, 2]
})
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
assert 3 * 4.2e6 + 2 * 2e6 == windfarm.nominal_power
def test_initialization_2(self):
"""test catching error when WindTurbine in wind_turbine_fleet
not initialized"""
test_farm = {
'wind_turbine_fleet': [{
'wind_turbine': None,
'number_of_turbines': 3
}]
}
msg = 'Wind turbine must be provided as WindTurbine object'
with pytest.raises(ValueError, match=msg):
WindFarm(**test_farm)
def test_initialization_list(self):
"""test simple initialization with wind turbine fleet list"""
wind_turbine_fleet = [{
'wind_turbine': WindTurbine(**self.test_turbine),
'number_of_turbines': 3
}, {
'wind_turbine':
WindTurbine(**self.test_turbine_2),
'number_of_turbines':
2
}]
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
assert 3 * 4.2e6 + 2 * 2e6 == windfarm.nominal_power
def test_initialization_5(self):
"""test catching error when number of turbines cannot be deduced"""
wt = WindTurbine(**self.test_turbine)
wt.nominal_power = None
test_farm = {
"wind_turbine_fleet": [{
"wind_turbine": wt,
"total_capacity": 3e6
}]
}
msg = "Number of turbines of type"
with pytest.raises(ValueError, match=msg):
WindFarm(**test_farm)
def test_initialization_7(self):
"""test catching error when total capacity cannot be deduced"""
wt = WindTurbine(**self.test_turbine)
wt.nominal_power = None
test_farm = {
'wind_turbine_fleet': [{
'wind_turbine': wt,
'number_of_turbines': 3
}]
}
msg = 'Total capacity of turbines of type'
with pytest.raises(ValueError, match=msg):
WindFarm(**test_farm)
def test_initialization_3(self):
"""test catching error when wind_turbine not specified in
wind_turbine_fleet"""
wind_turbine_fleet = pd.DataFrame(
data={
'wind_turbines': [
WindTurbine(**self.test_turbine),
WindTurbine(**self.test_turbine_2)
],
'number_of_turbines': [3, 2]
})
msg = 'Missing wind_turbine key/column in wind_turbine_fleet'
with pytest.raises(KeyError, match=msg):
WindFarm(wind_turbine_fleet=wind_turbine_fleet)
def test_initialization_6(self):
"""test catching error when neither number_of_turbines nor
total_capacity is provided"""
test_farm = {
'wind_turbine_fleet': [{
'wind_turbine':
WindTurbine(**self.test_turbine),
'number_of_turbine':
3e6
}]
}
msg = 'Number of turbines of type '
with pytest.raises(ValueError, match=msg):
WindFarm(**test_farm)
def test_initialization_list_2(self):
"""test simple initialization with wind turbine fleet list where
once number of turbines and once total capacity is provided"""
wind_turbine_fleet = [{
'wind_turbine': WindTurbine(**self.test_turbine),
'number_of_turbines': 3
}, {
'wind_turbine':
WindTurbine(**self.test_turbine_2),
'total_capacity':
2 * 2e6
}]
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
assert 3 * 4.2e6 + 2 * 2e6 == windfarm.nominal_power
def test_initialization_4(self, recwarn):
"""test overwriting and raising warning when number_of_turbines and
total_capacity in wind turbine fleet do not fit"""
wt1 = WindTurbine(**self.test_turbine)
wt2 = WindTurbine(**self.test_turbine_2)
wind_turbine_fleet = pd.DataFrame(data={
'wind_turbine': [wt1, wt2],
'number_of_turbines': [3, 2],
'total_capacity': [3, np.nan]
},
index=[0, 1])
windfarm = WindFarm(wind_turbine_fleet=wind_turbine_fleet)
total_cap_wt1_expected = \
wt1.nominal_power * wind_turbine_fleet.loc[0, 'number_of_turbines']
assert windfarm.wind_turbine_fleet.loc[0, 'total_capacity'] == \
total_cap_wt1_expected
total_cap_wt2_expected = \
wt2.nominal_power * wind_turbine_fleet.loc[1, 'number_of_turbines']
assert windfarm.wind_turbine_fleet.loc[1, 'total_capacity'] == \
total_cap_wt2_expected
assert recwarn.pop(WindpowerlibUserWarning)
def initialize_wind_farms(my_turbine, e126):
r"""
Initializes two :class:`~.wind_farm.WindFarm` objects.
This function shows how to initialize a WindFarm object. You need to
provide at least a name and a the wind farm's wind turbine fleet as done
below for 'example_farm'. Optionally you can provide a wind farm efficiency
(which can be constant or dependent on the wind speed) and coordinates as
done for 'example_farm_2'. In this example the coordinates are not being
used as just a single weather data set is provided as example data.
Parameters
----------
my_turbine : WindTurbine
WindTurbine object with self provided power curve.
e126 : WindTurbine
WindTurbine object with power curve from data file provided by the
windpowerlib.
Returns
-------
Tuple (WindFarm, WindFarm)
"""
# specification of wind farm data
example_farm_data = {
'name':
'example_farm',
'wind_turbine_fleet': [{
'wind_turbine': my_turbine,
'number_of_turbines': 6
}, {
'wind_turbine': e126,
'number_of_turbines': 3
}]
}
# initialize WindFarm object
example_farm = WindFarm(**example_farm_data)
# specification of wind farm data (2) containing a wind farm efficiency
# and coordinates
example_farm_2_data = {
'name':
'example_farm_2',
'wind_turbine_fleet': [{
'wind_turbine': my_turbine,
'number_of_turbines': 6
}, {
'wind_turbine': e126,
'number_of_turbines': 3
}],
'efficiency':
0.9,
'coordinates': [52.2, 13.1]
}
# initialize WindFarm object
example_farm_2 = WindFarm(**example_farm_2_data)
return example_farm, example_farm_2
def __init__(
self,
wind_turbines: list = [],
pv_arrays: list = [],
latitude: float = 57.6568,
longitude: float = -3.5818,
altitude: float = 10,
roughness_length: float = 0.15, # roughness length (bit of a guess)
hellman_exp: float = 0.2):
""" Set up the renewable energy generation
"""
# This needs to be repeated in every forecast
self.roughness_length = roughness_length
# Initialise empty forecast dataframe, just so nothing complains
self.wind_forecast = pd.DataFrame()
self.pv_forecast = pd.DataFrame()
# Wind turbine(s)
turbines = []
for turbine in wind_turbines:
turbines.append({
'wind_turbine':
WindTurbine(turbine['name'],
turbine['hub_height'],
nominal_power=turbine['nominal_power'],
rotor_diameter=turbine['rotor_diameter'],
power_curve=turbine['power_curve']),
'number_of_turbines':
turbine['qty']
})
local_wind_farm = WindFarm('Local windfarm', turbines,
[latitude, longitude])
# TODO - check for learned local data & overwrite power_curve
self.wind_modelchain = TurbineClusterModelChain(
local_wind_farm,
smoothing=False,
hellman_exp=hellman_exp,
)
# Initialise PV models
self.pv_location = Location(latitude=latitude,
longitude=longitude,
altitude=altitude)
# Now set up the PV array & system.
cec_pv_model_params = pvlib.pvsystem.retrieve_sam('CECMod')
sandia_pv_model_params = pvlib.pvsystem.retrieve_sam('SandiaMod')
cec_inverter_model_params = pvlib.pvsystem.retrieve_sam('CECInverter')
adr_inverter_model_params = pvlib.pvsystem.retrieve_sam('ADRInverter')
self.pv_modelchains = {}
for pv_array in pv_arrays:
# Try to find the module names in the libraries
if pv_array['module_name'] in cec_pv_model_params:
pv_array['module_parameters'] = cec_pv_model_params[
pv_array['module_name']]
elif pv_array['module_name'] in sandia_pv_model_params:
pv_array['module_parameters'] = sandia_pv_model_params[
pv_array['module_name']]
else:
raise RenewablesException('Could not retrieve PV module data')
# Do the same with the inverter(s)
if pv_array['inverter_name'] in cec_inverter_model_params:
pv_array['inverter_parameters'] = cec_inverter_model_params[
pv_array['inverter_name']]
elif pv_array['inverter_name'] in adr_inverter_model_params:
pv_array['inverter_parameters'] = adr_inverter_model_params[
pv_array['inverter_name']]
else:
raise RenewablesException('Could not retrieve PV module data')
self.pv_modelchains[pv_array['name']] = ModelChain(
PVSystem(**pv_array),
self.pv_location,
aoi_model='physical',
spectral_model='no_loss')