To effectively design a future energy system where wind power plays a major role, it is essential to represent wind generation using accurate time series data in the modelling process. For pan-European applications, the data must represent the significant geographical variation in wind resources across Europe. This is important for modelling current wind installations and for analysing the economically efficient deployment of new installations throughout Europe. And for analysing and comparing onshore vs. offshore wind installations in scenarios towards 2050 and beyond, the data must include accurate wind generation time series for both, including new technologies such as floating. For a wide-spread use of the data, also outside of academic research, it is essential to have the data validated.

This paper discusses findings from several projects (EU, Nordic and Danish), where the aim has been to provide representative high-resolution wind generation time series for pan-European power and energy system studies. Recent updates to the wind modelling focus on detailed wake modelling, stochastic simulation of unavailability and considering the impacts of climate change. A key result where the presented modelling is applied is the Pan European Climate Database (PECD) version 4.2 dataset for The European Network of Transmission System Operators (ENTSO-E). The data are made openly available in the C3S Climate Data Store. The generated data include also other technologies, such as solar photovoltaic, but here we focus on wind power.

The results highlight the need for adjusting reanalysis and climate projection wind speeds with a high resolution wind dataset, such as the Global Wind Atlas (GWA). We find that GWA version 2 (GWA2) improves validation metrics (correlation to measured data, capacity factor, etc.) across Europe compared to using ERA5 reanalysis wind speeds. GWA3 appears to be less effective than GWA2. A relatively detailed wake model, considering plant size, turbine spacing, appropriate turbulence intensity, etc., provides more accurate results than simpler setups. Stochastic simulation of unavailability provides more accurate representation of wind generation distribution especially at high generation. We discuss the impacts of the improvements on power and energy system modelling.

The same level of modelling detail as used with reanalysis data (covering 40+ years), is applied also when working with climate projections towards 2100. Overall, the impact of climate change on pan-European wind generation is expected to be low-to-moderate based on the results.

While the generated base pan-European datasets are hourly, the developed wind model allows simulation at up-to 5 min resolution. This has been demonstrated and validated at regional level. Sub-hourly simulation capabilities are expected to be increasingly in demand as markets move to higher resolution (15 min or even 5 min). With growing focus on intra-day and balancing markets—largely influenced by forecast inaccuracies—there is a rising interest in simulating wind forecasts, not just available power, for future scenario analyses. And as more-and-more offshore wind power plants are built close to each other, there is increasing need to model large-scale wakes in detail. We discuss plans for considering these aspects in our ongoing and future work.