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

TL;DR

This paper introduces a hierarchical RL-MPC framework for wind farm wake steering, combining reinforcement learning with model predictive control to improve power output and safety.

Contribution

It presents a novel hybrid control architecture where an RL agent estimates states for MPC, outperforming baseline and pure RL methods.

Findings

Achieves 23% power gain over baseline control.

Surpasses idealized MPC with perfect state knowledge.

Maintains safety during training while providing stable control.

Abstract

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.