Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control

TL;DR

This paper introduces a reinforcement learning controller integrated with high-fidelity simulations to optimize wind farm power output through dynamic, collaborative control, significantly outperforming static methods.

Contribution

It presents the first RL-based wind farm control method using high-fidelity LES, enabling real-time turbulence response and improved energy production.

Findings

RL controller increases power output by 4.30%

Nearly doubles the gain of static optimal yaw control

Demonstrates effectiveness of dynamic flow-responsive control

Abstract

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.