feedinlib.models.WindpowerlibTurbineCluster

class feedinlib.models.WindpowerlibTurbineCluster(**kwargs)

Model to determine the feed-in of a wind turbine cluster using the windpowerlib.

The windpowerlib [1] is a python library for simulating the performance of wind turbines and farms. For more information about the model check the documentation of the windpowerlib [2].

Notes

In order to use this model various power plant and model parameters have to be provided. See power_plant_requires as well as requires for further information. Furthermore, the weather data used to calculate the feed-in has to have a certain format. See feedin() for further information.

See also

Base WindpowerModelBase

References

[1]	windpowerlib on github

[2]	windpowerlib documentation

Methods

__init__(**kwargs)
feedin(weather, power_plant_parameters, **kwargs)	Calculates power plant feed-in in Watt.
instantiate_turbine(**kwargs)	Instantiates a windpowerlib.WindTurbine object.
instantiate_turbine_cluster(**kwargs)	Instantiates a windpowerlib.WindTurbineCluster object.
instantiate_windfarm(**kwargs)	Instantiates a windpowerlib.WindFarm object.

Attributes

nominal_power_wind_power_plant	Nominal power of wind turbine cluster in Watt.
power_plant_requires	The power plant parameters this model requires to calculate a feed-in.
requires	The parameters this model requires to calculate a feed-in.

__init__(**kwargs)

feedin(weather, power_plant_parameters, **kwargs)

Calculates power plant feed-in in Watt.

This function uses the windpowerlib’s TurbineClusterModelChain to calculate the feed-in for the given weather time series and wind farm or cluster.

Parameters:

• weather (pandas.DataFrame) – Weather time series used to calculate feed-in. See weather_df parameter in windpowerlib’s TurbineClusterModelChain run_model method for more information on required variables, units, etc.
• power_plant_parameters (dict) – Dictionary with either wind farm or wind turbine cluster specifications. For more information on wind farm parameters see kwargs in instantiate_windfarm(). For information on turbine cluster parameters see kwargs in instantiate_turbine_cluster().
• **kwargs – Keyword arguments can be used to overwrite the windpowerlib’s TurbineClusterModelChain parameters.

Returns:

Power plant feed-in time series in Watt.

Return type:

pandas.Series

instantiate_turbine(**kwargs)

Instantiates a windpowerlib.WindTurbine object.

Parameters:**kwargs – Dictionary with wind turbine specifications. Keys of the dictionary are the power plant parameter names, values of the dictionary hold the corresponding value. The dictionary must at least contain the required turbine parameters (see power_plant_requires) and may further contain optional power plant parameters (see windpowerlib.WindTurbine). Returns:Wind turbine in wind farm or turbine cluster. Return type:windpowerlib.WindTurbine

instantiate_turbine_cluster(**kwargs)

Instantiates a windpowerlib.WindTurbineCluster object.

Parameters:**kwargs – Dictionary with turbine cluster specifications. Keys of the dictionary are the parameter names, values of the dictionary hold the corresponding value. The dictionary must at least contain a list of wind farms (see ‘wind_farms’ specifications in power_plant_requires) and may further contain optional wind turbine cluster parameters (see windpowerlib.WindTurbineCluster). Returns: Return type:windpowerlib.WindTurbineCluster

instantiate_windfarm(**kwargs)

Instantiates a windpowerlib.WindFarm object.

Parameters:**kwargs – Dictionary with wind farm specifications. Keys of the dictionary are the parameter names, values of the dictionary hold the corresponding value. The dictionary must at least contain a wind turbine fleet (see ‘wind_turbine_fleet’ specifications in power_plant_requires) and may further contain optional wind farm parameters (see windpowerlib.WindFarm). Returns: Return type:windpowerlib.WindFarm

nominal_power_wind_power_plant

Nominal power of wind turbine cluster in Watt.

The nominal power is the sum of the nominal power of all turbines. See nominal_power of windpowerlib.WindFarm or windpowerlib.WindTurbineCluster for further information.

power_plant_requires

The power plant parameters this model requires to calculate a feed-in.

The required power plant parameters are:

wind_turbine_fleet/wind_farms

The windpowerlib differentiates between wind farms as a group of wind turbines (of the same or different type) in the same location and wind turbine clusters as wind farms and turbines that are assigned the same weather data point to obtain weather data for feed-in calculations and can therefore be clustered to speed up calculations. The WindpowerlibTurbineCluster class can be used for both windpowerlib.WindFarm and windpowerlib.WindTurbineCluster calculations. To set up a windpowerlib.WindFarm please provide a wind_turbine_fleet and to set up a windpowerlib.WindTurbineCluster please provide a list of wind_farms. See below for further information.

wind_turbine_fleet (pandas.DataFrame)

The wind turbine fleet specifies the turbine types and their corresponding number or total installed capacity in the wind farm. DataFrame must have columns ‘wind_turbine’ and either ‘number_of_turbines’ (number of wind turbines of the same turbine type in the wind farm, can be a float) or ‘total_capacity’ (installed capacity of wind turbines of the same turbine type in the wind farm in Watt).

The wind turbine in column ‘wind_turbine’ can be provided as a WindPowerPlant object, a dictionary with power plant parameters (see power_plant_requires for required parameters) or a windpowerlib.WindTurbine.

See also wind_turbine_fleet parameter of windpowerlib.WindFarm.

The wind turbine fleet may also be provided as a list of windpowerlib.WindTurbineGroup as described there.

wind_farms (list(dict) or list(windpowerlib.WindFarm))

List of wind farms in cluster. Wind farms in the list can either be provided as windpowerlib.WindFarm or as dictionaries where the keys of the dictionary are the wind farm parameter names and the values of the dictionary hold the corresponding value. The dictionary must at least contain a wind turbine fleet (see ‘wind_turbine_fleet’ parameter specifications above) and may further contain optional wind farm parameters (see windpowerlib.WindFarm).

requires

The parameters this model requires to calculate a feed-in.

This model does not require any additional model parameters.