Annual wake impacts in and between wind farm clusters – Part 2: Comparison of WRF and fast-running engineering wake models

Wind energy is regarded as an important component for global decarbonization strategies, and the rapid expansion of offshore wind farms has led to increasingly large and closely spaced wind farm clusters. As a result, assessing wake impacts between neighboring wind farms has become increasingly important. Multiple numerical modeling approaches are available for such assessments and differ in their physical assumptions, representations of atmospheric processes, and computational cost. Numerical weather prediction models and fast-running engineering wake models are both used for long-term wake impact studies, yet they are based on different assumptions regarding atmospheric variability and wake recovery. These differences introduce uncertainty in predicted power production, and it remains unclear how consistently the different modeling approaches estimate long-term wake impacts and how sensitive these estimates are to model formulation. Here we compare wake impacts predicted by commonly used engineering wake models with those simulated by a numerical weather prediction model (WRF), simulated in Part 1 (Porchetta et al., 2026) of this paper series, for a meteorologically representative year (2016), focusing on the planned Princess Elisabeth offshore wind farm cluster and the Belgian–Dutch wind farm cluster in the southern North Sea. We show that engineering wake models generally predict higher wind farm power production and smaller wake energy losses than WRF. Separating these losses into internal and external components reveals that external wake energy losses have a larger relative spread between modeling approaches than internal wake energy losses, particularly during summer. By analyzing wake energy losses as a function of atmospheric stability, we demonstrate that discrepancies between modeling approaches increase under stable stratification, which occurs more frequently during summer. Under these conditions, reduced turbulent mixing leads to slower wake recovery and increased sensitivity to the wake recovery formulation. Engineering wake models that explicitly account for turbulence-dependent wake recovery show closer agreement with WRF, particularly for external wake losses. These results quantify the uncertainty associated with long-term intra-farm and farm–farm wake estimates and identify atmospheric stability as a key driver of model spread. This paper forms the second part of a two-part study investigating wake impacts of offshore wind farms. In Part 1, wake effects induced by the planned Princess Elisabeth wind farm cluster are analyzed using WRF to characterize annual mean and sub-annual variability under atmospheric conditions. The present paper (Part 2) builds on this analysis by comparing the WRF estimates with commonly used fast-running engineering wake models, enabling a systematic assessment of differences between modeling approaches and the uncertainty associated with long-term intra-farm and farm–farm wake estimates.