Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 79 references that are in the map. First posted on arXiv 25 Jun 2025.
Builds on
15 of 79 references.
References · 15
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3 more
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Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
- 2026Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning
- 2025How to craft a deep reinforcement learning policy for wind farm flow control
- 2026Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms
- 2024Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
- 2024Reinforcement learning to maximise wind turbine energy generation
- 2026Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions
- 2026Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning