Statistical downscaling of mesoscale wind time series for microscale resource assessment

With the increasing share of renewables in the European grid, predicting time series for wind speed and direction is becoming vital for accurate wind resource assessment. During periods of strong winds, energy prices are low, meaning one must account for the fact that the energy produced is less "valuable." Traditionally, Annual Energy Production (AEP) is calculated using Weibull distributions and a power curve; however, this approach ignores the impact of varying energy prices.

To further complicate matters, the onshore wind resource is spatially highly heterogeneous. Numerical models capable of predicting flow at microscale resolution are computationally expensive. Therefore, we introduce a method to statistically downscale mesoscale time series to the microscale (50 m grid spacing).

The method is based on wind-speed-independent roughness and orographic speedup factors, and a statistical representation of stability effects. The stability model accounts for both the mean and variance of the wind distribution as a function of height and surface roughness. By assuming that both mesoscale and microscale wind speed distributions are Weibull distributed, the mesoscale time series are transformed. We test several approaches for this transformation using mesoscale time series from the New European Wind Atlas (3 km resolution) and downscale them to 50 m resolution. For this downscaling we use high-resolution landcover (CORINE) and elevation data (Copernicus DEM).

Finally, we validate this approach using a database of meteorological masts and wind lidars across Europe. These measurements demonstrate that microscale variability is significant, and accounting for it can substantially improve wind resource estimations derived from mesoscale models. Future improvements to this workflow are also discussed.