Reiner Lemoine Institut erstellt Elektromobilitätskonzepte für Städte in NRW31. Januar 2019
Travelling Conferences 27. Februar 2019
In this thesis an open source model for generating wind feed-in time series called
Windpowerlib is implemented in Python and validated with measured feed-in time series.
A basic version of the Windpowerlib that was developed at the Reiner Lemoine
Institute serves as basis of this work. Functionalities like wind speed height corrections,
density and power output calculations, power curve smoothing, aggregated
power curves and functionalities for the consideration of wake losses are presented,
implemented and their e_ect on simulation results evaluated. The validation is
carried out with measured feed-in time series of wind parks in Schleswig-Holstein
(coastal region) and Brandenburg (inland region) in Germany for the years 2015 and
2016. Moreover, the inuence of weather data on feed-in time series simulations is
examined by using two di_erent weather data sets. MERRA-2 data provided by the
NASA is compared with open FRED weather data that was especially created for
energy systems simulations.
Wind farm feed-in can be simulated with a deviation of 4.7 % (inland) and 3.4 %
(coastal region) from the measured annual energy output (overestimation) by the
Windpowerlib when using open FRED weather data. For MERRA-2 data the deviations
are about ten percentage points higher in Schleswig-Holstein (coastal region)
and about 26 percentage points higher in Brandenburg (inland region). All generated
time series attain strong correlations with the measured time series with
Pearson correlation coe_cients of about 0.7 to 0.9 while MERRA-2 data reaches
slightly higher correlations compared to open FRED data.