Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms

Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework that combines reinforcement learning with model predictive control, where an RL agent learns compensatory state estimates for an MPC controller, rather than directly controlling turbines. Evaluated on a three-turbine case, the approach achieves a 23% power gain over the baseline control and surpasses the idealized MPC with perfect state knowledge. Compared to direct RL control, the hybrid architecture maintains superior safety characteristics during training while achieving comparable performance with more stable control actions.