Posters

Abstract

The current expansion in both the number and scale of offshore wind farms is expected to continue over the next decades to support the decarbonization targets. This growth is characterized by spatial concentration in regions with favorable energy resources and metocean conditions and established infrastructure, leading to the emergence of large offshore wind farm clusters. In this context, the availability of reliable tools to evaluate interactions among wind farms, as wind speed reductions due to intra-cluster wake effects can have a significant impact on cost and opportunity assessments. While analytical models offer simplified results and computational fluid dynamics ensure accuracy with significant computational cost, both approaches fail to capture the complexity of real climate conditions. Mesoscale circulation models with parametrization of the interaction between the atmosphere and offshore wind farms represent a cost-effective and valuable alternative. In this study, we propose a hybrid downscaling approach to efficiently assess wind speed deficits under real atmospheric conditions. We focus on two marine areas located in the Irish Sea and in the offshore Thames river mouth, which are both characterized by a high concentration of offshore wind farms and the availability of extensive technical and meteorological data. The outputs of the hybrid model are validated by comparison with the available in-situ wind speed measurements. Validation results show nearly-zero bias across multiple spatial and temporal scales. We then develop a methodology to quantify the impact of offshore wind farms on the wind speed in their surrounding area by combining the results of a reduced number of downscaling simulations with the information provided by the statistical core of the hybrid model. The final output of this process is a set of maps that can be obtained with reduced computational effort and employed as a support tool for the planning of future offshore wind farms.