Abstract/Summary

Understanding near-surface wind variability is crucial to support wind power penetration on national electrical grids. High-resolution numerical simulations are often proposed as the best solution to study the fluctuation of wind resources. We compare Weather Research and Forecasting (WRF) and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) bias-corrected wind speeds at hub height at different spatial resolutions and transform them to wind power production using a logistic power curve fitted to wind power measurements; the comparisons are based on error statistics and time series spectral analysis. The results show that numerical models reproduce observed wind speeds with correlations higher than 0.9 for WRF and 0.8 for MERRA-2. Moreover, annual observed wind power is reproduced with a maximum difference from observations of 0.011. However, each resolution reproduces the magnitudes of high-resolution periodicities differently so that there is a clear relationship between grid size and signal variance at high frequencies, as variance is indirectly proportional to frequency. This relationship is expected for wind speed, but based on results, it can be associated also for capacity factor sampled at hourly intervals. Therefore, the main benefit of high spatial resolution lies in the added variance in frequencies at sub-daily time scales. The study of the added value of high-resolution simulations in this region contributes to current efforts to develop reliable forecasting tools and strategies to support the development of wind power as a reliable energy source.