Numerical modelling of offshore wind-farm cluster wakes
Pablo Ouro, Mina Ghobrial, Karim Ali, Tim Stallard
Abstract
Large offshore wind farms are being installed in proximity to other wind farms to maximise seabed use so that ambitious targets for electricity system decarbonisation can be realised in line with the UN SDG 7: Affordable and Clean Energy. When in operation, a wind farm generates a low-velocity wake region downstream, reducing the available wind resource and potentially impacting the performance of wind farms located downstream. Accurate prediction of wake effects from multi-GW wind farms and clusters of closely spaced wind farms is essential to enable reliable forecasts of energy yield, thus informing wind-farm siting and operation. For these scales there is insufficient data available from existing operational wind-farms alone to fully evaluate the applicability of numerical models for capturing the relevant flow physics. Farm-to-farm interactions are expected to have their greatest impact during stable thermal stratification, characterised by low turbulence intensity and small length scales that promote long wakes. Gravity waves may induce secondary effects impacting wind-farm wake recovery but knowledge about these remains limited. Coriolis forcing plays an important factor influencing the far-wake region with wake deflection being particularly important to capture since this determines which downstream farms are impacted. The review identifies five main numerical frameworks widely used by industry and academia for wind-farm flows and evaluates the applicability and limitations of each approach. Subsequently several recommendations are made for improving confidence in predictions of such wakes, and hence forecasts of the energy-yield from multi-GW wind-farm clusters. • Prediction of wakes from multi-GW scale wind farm clusters increasingly important. • Five classes of numerical method identified for studying wakes of large wind farms. • Large wind farms and wakes affected by thermal stratification and Coriolis forcing. • Microscale LES captures thermal stratification, Coriolis forcing and gravity waves. • Mesoscale models capture spatio-temporal dynamics of wind-farm flows.