Reinforcement learning increases wind farm power production by enabling closed-loop collaborative control
File(s)
Published version
Author(s)
Mole, Andrew
Weissenbacher, Max
Rigas, Georgios
Laizet, Sylvain
Type
Journal Article
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 do not resolve critical dynamic turbulent fluctuations in the flow. In this work, we present a reinforcement learning (RL) controller trained using high-fidelity large-eddy simulation (LES), enabling real-time response to atmospheric turbulence through collaborative,
dynamic control strategies. In a three wind turbine test case, our RL 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.
dynamic control strategies. In a three wind turbine test case, our RL 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.
Date Issued
2026-07-10
Date Acceptance
2026-02-11
Citation
Communications Engineering, 2026, 5
ISSN
2731-3395
Publisher
Nature Portfolio
Journal / Book Title
Communications Engineering
Volume
5
Copyright Statement
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s44172-026-00667-8
Publication Status
Published
Article Number
ARTN 129
Date Publish Online
2025-05-05