Posters

Abstract

To make wind projects liable, accurate wind resource assessments are required. Wind atlases produced by long-term high resolution mesoscale model simulations such as NEWA can be applied for that. Since the production of such wind atlases are computational expensive, different scenarios such as the accounting for existing and possible future wind farms cannot be easily tested. To solve this problem statistical-dynamical downscaling methods have been proposed. These methods aim to represent the long-term 30-year wind statistic by only simulating a shorter representative period. Thereby computational costs are reduced, and simulating different scenarios with wind farm wakes becomes feasible. In this work we will give an overview over different downscaling techniques and introduce two methods in more detail. In the first method, a calendar year is identified that is most representative of the long-term climate in a certain region. It is a mulit-variable, multi-location method as the statistics of wind speed, wind direction and stability are evaluated at multiple locations in the domain of interest. The identified year is then downscaled from reanalysis data using a mesoscale model. This method reduces the computational resources to 1/30 and has been applied among others to study the future wind resources of the German Bight using ERA5 reanalysis and the mesoscale model WRF (Agora Energiewende et al. 2020). Selecting representative days that are distributed over different years can reduce the required computational resources even further. In this second method, days are randomly sampled out of a 30-year period. The Perkins Skill Score is applied to evaluate the agreement between the combined PDF of the representative days an the 30-year PDF. The method can be applied for different variables individually as well as for the combination of variables those variables. This is especially relevant offshore, where both the wind- and the wave climate are of interest. To ensure that the climate is representative for the domain of interest, multiple locations are used. This method has been applied to the German Bight using long-term wind and wave measurements and it was shown that overall 180 days are sufficient to represent the long-term wind and wave climate in the German Bight (Fischereit et al 2022). The representative days are simulated with a wind-wake-wave coupled mesoscale modeling system to identify the influence of waves and wakes on wind resources in the German Bight. In the presentation besides introducing the methods and showing the sample results, the methods will be compared, and advantages and disadvantages be discussed. The methods will be generalized beyond the German Bight with the aim to provide cost-effective methods for wind resource assessment across the globe that can take into account farm wake effects. References Agora Energiewende, Agora Verkehrswende, Technical University of Denmark and Max-Planck-Institute for Biogeochemistry (2020): Making the Most of Offshore Wind: Re-Evaluating the Potential of Offshore Wind in the German North Sea. Available at https://static.agora-energiewende.de/fileadmin/Projekte/2019/Offshore_Potentials/176_A-EW_A-VW_Offshore-Potentials_Publication_WEB.pdf J. Fischereit, X.G. Larsén, and A.N. Hahmann (2022): Climatic impacts of wind-wave-wake interactions in offshore wind farms. Frontiers in Energy Research - Wind Energy, (revised version submitted)