Abstract
Wind power has risen continuously over the last 20 years and covered almost 25% of the total German power provision in 2019. To investigate the effects and challenges of increasing wind power on energy systems, spatiotemporally disaggregated data on the electricity production from wind turbines are often required. The lack of freely accessible feed-in time series from onshore turbines, e.g., due to data protection regulations, makes it necessary to determine the power generation for a certain region and period with the help of numerical simulations using publicly available plant and weather data. For this, a new approach is used for the wind power model which utilizes a sixth-order polynomial for the specific power curve of a turbine. After model validation with measured data from a single wind turbine, the simulations are carried out for an ensemble of 25,835 onshore turbines to determine the German wind power production for 2016. The resulting hourly resolved data are aggregated into a time series with daily resolution and compared with measured feed-in data of entire Germany which show a high degree of agreement. Such electricity generation data from onshore turbines can be applied to optimize and monitor renewable power systems on various spatiotemporal scales.
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