Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
This work addresses wind farm optimization for renewable energy deployment, representing a transformative approach rather than an incremental improvement.
The paper tackled the problem of wind farm power production by developing a reinforcement learning controller integrated with high-fidelity simulation for dynamic closed-loop control, achieving a 4.30% increase in power output compared to baseline.
Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially increase the combined wind farm energy production. Although dynamic closed-loop control has proven effective in flow control applications, wind farm optimization has relied primarily on static, low-fidelity simulators that ignore critical turbulent flow dynamics. In this work, we present the first reinforcement learning (RL) controller integrated directly with high-fidelity large-eddy simulation (LES), enabling real-time response to atmospheric turbulence through collaborative, dynamic control strategies. Our RL controller achieves a 4.30% increase in wind farm power output compared to baseline operation, nearly doubling the 2.19% gain from static optimal yaw control obtained through Bayesian optimization. These results establish dynamic flow-responsive control as a transformative approach to wind farm optimization, with direct implications for accelerating renewable energy deployment to net-zero targets.