Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations

With increasing wind installations, there is a need to analyse generation variability in detail. This paper applies the reanalysis approach for modelling variability; however, with two important additions. Firstly, high-resolution microscale data combined mesoscale time series model local wind. Secondly, as are often missing technical parameters large-scale power plant datasets, machine learning used estimate values. It shown that such detailed leads more accurate simulations than baseline when compared historical from multiple European countries. In addition, applicability of methodology analysing future scenarios changing installations demonstrated.