Clustering wind turbines as a wind farm to share the infrastructure is an effective strategy to reduce the cost of energy. However, this results in aerodynamic wake interaction among wind turbines. Yawing the upstream wind turbines can mitigate the losses in wind farm power output. Yaw-misalignment also affects the loads, as partial wake overlap can increase fatigue of downstream turbines. This paper studies multi-objective optimization of wind farm wake using yaw-misalignment to increase power production and reduce loads due to partial wake overlap. This is achieved using a computational framework consisting of an aerodynamic model for wind farm wake, a blade-element-momentum model to compute the power and the loads, and a gradient-based optimizer. The results show that yaw-misalignment is capable of increasing the power production of the wind farm, while reducing the loading due to partial wake overlap. A multi-objective optimization is able to further decrease the loads at the expense of a small amount of power production.@en

To make wind energy cost competitive with traditional resources, wind turbines are commonly placed in groups. Aerodynamic interaction between the turbines causes sub-optimal energy production. A control strategy to mitigate these losses is by redirecting the wake by yaw misalignment. This paper aims to assess the influence of load variations of the rotor due to partial wake overlap and presents a combined optimization of the power and loads using wake redirection. For this purpose, we design a computational framework which computes the wind farm power production and the wind turbine rotor loads based on the yaw settings. The simulation results show that partial wake overlap can significantly increase asymmetric loading of the rotor disk and that yaw misalignment is beneficial in situations where the wake can be sufficiently directed away from the downstream turbine.@en