As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses. ... Read more

With the rapid increase in wind farm developments, it is essential to evaluate the impacts of newly constructed wind farms on adjacent existing and planned wind farms. Various numerical models have been used to study the interactions between adjacent farms, spanning from fast-running engineering models to numerical weather prediction models. These models are essential to anticipate and mitigate wake effects in future wind energy deployments. Since the atmospheric conditions are variable over the year, it is important to characterize the variation of the wake interactions over the year, important for wind farm operation and planning. Due to the higher computational cost of numerical weather prediction models compared to fast-running engineering models, they are limited in their capacity to evaluate a wide range of design scenarios or very long simulation periods. In this study, we investigate the annual variation of wake effects coming from a new wind farm cluster on adjacent, existing wind farms in the North Sea using a simulation of a representative year. We compare results from a numerical weather prediction model (the Weather and Research Forcasting model) and for multiple fast-running engineering wake models (Gauss-BPA, Jensen (kw = 0.02), Jensen (kw = 0.04), Cumulative curl, TurbOPark (A = 0.04), TurbOPark (A = 0.06)), providing insights into variations in wake loss predictions among the models. Indeed, both the numerical weather prediction model and the engineering models make different assumptions to predict wake interactions between wind farms. Throughout this study a distinction is made between external wake losses, caused by the newly built wind farm cluster only, and internal wake losses, which are generated by the individual wind farms of the existing cluster. Temporal variations in stability are the main driver of hourly and seasonal variations in external wake losses, while internal losses are also determined by seasonal variations in wind speed. While yearly averaged external wind farm losses from the numerical weather prediction model are limited to 4 %, the internal wake losses reach as high as 3 % for the closest adjacent, existing wind farm to the new wind farm cluster. Additionally, all engineering models considered predict lower wake losses compared to the numerical weather prediction model, but predictions exhibit a very large range of magnitudes, ranging from 98 % to 33 % difference for external wakes and 59 % to 14 % for internal wakes compared to the numerical weather prediction model. Not only do the results differ quantitatively but also qualitatively between model strategies, i.e. yearly spatial distributions, especially for the external wake predictions of the former fast-running engineering models (Gauss-BPA, Jensen (kw = 0.02), Jensen (kw = 0.04)) and the numerical weather prediction model. These engineering models do not capture the same qualitative trend as the numerical weather prediction model while the newly designed engineering models (Cumulative curl, TurbOPark (A = 0.04), TurbOPark (A = 0.06)) do. For the internal wake losses, qualitatively, both engineering models and the numerical weather prediction model show higher internal wake losses for turbines located in the center of the wind farm, with highest losses for densely spaced turbines, and lower losses at the edge of the wind farm, however all models show different magnitudes of losses. ... Read more