The role of spatial resolution of climate data for the quality of simulated wind power generation – A multi-country analysis

Integration of a high share of renewables into the energy system comes with its implications. In order to study long and short-term effects on the electrical system, long time series of power generation with high spatial resolution are necessary. In recent years, reanalysis data have become a popular resource for obtaining these power generation datasets, however with the drawback of a rather coarse spatial resolution of several kilometres (MERRA-2: approx. 50km, ERA5: approx. 31 km). In order to overcome this limitation, reanalysis datasets can be combined with other datasets with a higher spatial resolution.

In the present study, we assess whether applying the Global Wind Atlas (GWA) developed by the Technical University of Denmark with a spatial resolution of 1 km can improve wind power generation simulated from two reanalysis (MERRA-2 and ERA5)  datasets when compared to observed power generation. Furthermore, these two reanalysis datasets are compared to determine how different spatial resolution of underlying reanalysis datasets affects the resulting time series. Wind power generation is simulated from reanalysis wind speeds based on a physical model. For wind speed bias correction to specific locations, mean wind speeds are approximated to GWA wind speeds. A turbine-specific power curve model scaled by the turbine specific power is applied to account for different technical performance. The analysis is conducted for different regions of the world (USA, Brazil, Austria, South Africa) and for different spatial and temporal levels, to determine how different datasets perform on different spatio-temporal scales.

Preliminary results show that bias correction with the GWA has a positive impact on simulation results for MERRA-2, the dataset with lower spatial resolution, while the effect for ERA5 is ambiguous. The error between simulated and observed wind power generation time series can be decreased by spatial and temporal aggregation and a positive, but not very strong correlation between system size (defined by a wind-correlation indicator) and simulation quality (higher correlation, lower error measures) could be identified.

Based on these results, we recommend applying additional wind speed bias correction on datasets with rather coarse spatial resolution, while the quality of newer datasets with high spatial resolution may be sufficient to be used without additional bias correction.