EuroWindWakes: Multiscale Modelling of European Wind Energy Wake Effects

The European Union has set ambitious targets for harnessing the offshore wind energy potential of the North Sea. A key challenge in achieving these goals is properly accounting for the large-scale effects of wind farms on wind resources and the associated uncertainties when modeling energy yields in dense wind farm clusters. Underestimating these effects could lead to lower-than-anticipated power generation on a national or even European level, which would affect the energy transition in general as well as corresponding national plans. Additionally, this could lead to overly optimistic bidding in tenders, threatening the financially viability offshore wind projects. The EuroWindWakes project, funded under the CETPartnership, addresses these challenges and aims to reduce the modelling uncertainties by developing, enhancing, validating and benchmarking different multiscale modelling techniques. The project considers various scales for different models, ranging from synoptical scales in weather forecasting models over atmosphere-wave coupled mesocale models to high and low fidelity microscale models (LES and engineering-wake models) that are being coupled to mesoscale models. Furthermore, a lower-fidelity but fast canopy model is being developed to take into account the effect of wind farms on the flow. The international consortium of the projects consists of universities, research institutes, national weather services, consultants, and wind farm operators from the North Sea neighboring countries Netherlands, Germany and Denmark. The project runs from November 2024 to October 2027. This poster introduces the project and presents first results. These include an approach for generating an open-source database and toolkit for mesoscale modelling of European offshore wind farms. The approach combines turbine locations extracted from OpenStreetMap, Copernicus Sentinel data and Emodnet data with thrust and power curves calculated via the turbine generator in pyWake. This approach is validated against closed source data, e.g. from manufacturers. This research was funded by CETPartnership, the Clean Energy Transition Partnership under the 2023 joint call for research proposals, co funded by the European Commission (GA 101 069750 ) and with the funding organizations as detailed on https://cetpartnership.eu/funding-agencies-and-call-modules.