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<p>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 do not resolve critical dynamic turbulent fluctuations in the flow. In this work, we present a reinforcement learning controller trained using high-fidelity turbulence resolving simulations, enabling real-time response to atmospheric turbulence through collaborative, dynamic control strategies. In a three wind turbine test case, our reinforcement learning controller achieves a 4.30% (95% CI = [4.10%,&#xa0;4.49%]) increase in wind farm power output compared to baseline operation, nearly doubling the 2.19% (95% CI = [1.98%,&#xa0;2.39%]) gain from static optimal yaw control and a substantial increase over the gain from global wind direction based dynamic control obtained through Bayesian optimization of 2.67% (95% CI = [2.47%,&#xa0;2.87%]). These results establish that reinforcement learning is able to utilize the increased information available from turbulence resolved simulations to learn improved, dynamic flow-responsive control for wind farm power maximization, with direct implications for accelerating renewable energy deployment to net-zero targets.</p>

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Reinforcement learning increases wind farm power production by enabling closed-loop collaborative control

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Andrew Mole,
  • Max Weissenbacher,
  • Georgios Rigas,
  • Sylvain Laizet

摘要

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 do not resolve critical dynamic turbulent fluctuations in the flow. In this work, we present a reinforcement learning controller trained using high-fidelity turbulence resolving simulations, enabling real-time response to atmospheric turbulence through collaborative, dynamic control strategies. In a three wind turbine test case, our reinforcement learning controller achieves a 4.30% (95% CI = [4.10%, 4.49%]) increase in wind farm power output compared to baseline operation, nearly doubling the 2.19% (95% CI = [1.98%, 2.39%]) gain from static optimal yaw control and a substantial increase over the gain from global wind direction based dynamic control obtained through Bayesian optimization of 2.67% (95% CI = [2.47%, 2.87%]). These results establish that reinforcement learning is able to utilize the increased information available from turbulence resolved simulations to learn improved, dynamic flow-responsive control for wind farm power maximization, with direct implications for accelerating renewable energy deployment to net-zero targets.