Wind farm multi-objective wake redirection for optimizing power production and loads

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.