Assessing simulation and bias correction methods for wind power generation in Brazil - can global datasets compete with local measurements

04/30/2019 ∙ by Katharina Gruber, et al. ∙ 0

For the integration of higher shares of volatile renewables, it is important to analyse long- and short-term variations. We propose and evaluate different methods for wind power simulation on four spatial resolution levels in Brazil using NASA's MERRA-2 reanalysis data set. In particular we assess spatial interpolation methods and spatial as well as spatiotemporal wind speed bias correction using wind speed measurement data and mean wind speeds. The resulting time series are validated with daily wind power generation. The main purpose of our analysis is to understand whether global information on wind speeds can compete with locally measured wind speed data as a source of bias correction, and which combinations of methods work best. Results show that more sophisticated methods, for both spatial interpolation and wind speed correction, are not always necessary to obtain good simulation results. Global Wind Atlas data can compete with measured wind speeds as a source for bias correction, a result which, due to the global availability and high spatial resolution, paves the way for bias-corrected global wind power simulation. Only on the level of wind parks, bias correction did not improve results. To achieve better results here, higher spatial resolution reanalysis data would be needed.

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