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

2025-06-25

AI Summary

Traditional wind farms employ individual turbine control, neglecting wake coupling effects and atmospheric turbulence dynamics, thereby limiting aggregate power output. This paper proposes a reinforcement learning (RL)-based cooperative closed-loop control framework, achieving—for the first time—the real-time integration of RL with high-fidelity large-eddy simulation (LES) to enable dynamic wake-steering decisions. The method incorporates Bayesian optimization to accelerate policy training, overcoming limitations of static, low-fidelity models and open-loop optimization approaches. Experimental results demonstrate a 4.30% increase in total farm power relative to baseline operation—nearly double the 2.19% gain achieved by static yaw optimization. This work establishes the first high-fidelity, dynamic closed-loop paradigm for intelligent cooperative wind farm control.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Enabling closed-loop collaborative wind farm control
• Increasing wind farm power via dynamic wake steering
• Replacing static simulators with turbulence-responsive RL

Innovation

• Reinforcement learning enables closed-loop wind farm control
• High-fidelity LES integrates with RL for dynamic strategies
• Dynamic control increases power output by 4.30%