The WindpowerlibTurbine model can be used to determine the feed-in of a wind turbine using the windpowerlib.
The windpowerlib 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.
The following example shows you how to use the WindpowerlibTurbine model.
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from feedinlib import WindPowerPlant
The wind power plant must have all power plant parameters required by the WindpowerlibTurbine model. The required parameters can be looked up in the model's documentation.
The WindpowerlibTurbine model requires you to provide the turbine's hub height as well as the turbine's power curve or power coefficient curve. Alternatively to providing the curve(s) directly you can provide the turbine type which will retrieve the turbine's power and/or power coefficient curve from a wind turbine library provided along with the windpowerlib. For an overview of the provided wind turbines you can use the function get_power_plant_data().
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from feedinlib import get_power_plant_data
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# get wind turbines
turbine_df = get_power_plant_data(dataset='oedb_turbine_library')
# print the first four turbines
turbine_df.iloc[1:5, :]
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Now you can set up a wind turbine to calculate feed-in for:
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# set up wind turbine using the wind turbine library
turbine_data = {
'turbine_type': 'E-101/3050', # turbine name as in turbine library
'hub_height': 135 # in m
}
wind_turbine = WindPowerPlant(**turbine_data)
Besides setting up your wind turbine you have to provide weather data the feed-in is calculated with. This example uses open_FRED weather data. For more information on the data and download see the load_open_fred_weather_data Notebook.
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from feedinlib.open_FRED import Weather
from feedinlib.open_FRED import defaultdb
from shapely.geometry import Point
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# specify latitude and longitude of wind turbine location
location = Point(13.5, 52.4)
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# download weather data for June 2017
open_FRED_weather_data = Weather(
start='2017-06-01', stop='2017-07-01',
locations=[location],
heights=[140, 160],
variables="windpowerlib",
**defaultdb())
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# get weather data in windpowerlib format
weather_df = open_FRED_weather_data.df(location=location, lib="windpowerlib")
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# plot wind speed
import matplotlib.pyplot as plt
%matplotlib inline
weather_df.loc[:, ['wind_speed']].plot(title='Wind speed')
plt.xlabel('Time')
plt.ylabel('Wind speed in m/s');
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feedin = wind_turbine.feedin(
weather=weather_df)
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin.plot(title='Wind turbine feed-in')
plt.xlabel('Time')
plt.ylabel('Power in W');
Scaled feed-in
The wind turbine feed-in can also be automatically scaled by the turbine's nominal power.
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# calculate scaled feed-in
feedin_scaled = wind_turbine.feedin(
weather=weather_df,
scaling='nominal_power')
The turbine's nominal power can be retrieved as follows:
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wind_turbine.nominal_power
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_scaled.plot(title='Scaled wind turbine feed-in')
plt.xlabel('Time')
plt.ylabel('Power in W');
Feed-in with optional model parameters
In order to change the default calculation configurations of the WindpowerlibTurbine model to e.g. use the turbine's power coefficient curve instead of power curve you can pass further parameters to the feedin method. An overview of which further parameters may be provided is documented under the feedin method's kwargs.
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# use density corrected power curve to calculate feed-in
feedin_density_corrected = wind_turbine.feedin(
weather=weather_df,
density_correction=True)
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_density_corrected.plot(title='Wind turbine feed-in', legend=True,
label='density corrected power curve')
feedin.plot(legend=True, label='power curve')
plt.xlabel('Time')
plt.ylabel('Power in W');
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# use power coefficient curve to calculate feed-in
feedin_coefficient_curve = wind_turbine.feedin(
weather=weather_df,
power_output_model='power_coefficient_curve')
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# plot calculated feed-in
import matplotlib.pyplot as plt
%matplotlib inline
feedin_coefficient_curve.plot(title='Wind turbine feed-in', legend=True,
label='power coefficient curve')
feedin.plot(legend=True, label='power curve')
plt.xlabel('Time')
plt.ylabel('Power in W');
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