用强化学习实现风机群实时协同控制，提升风电场发电量4.3%。
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
- 通过强化学习与高精度模拟结合，实现风机群动态协同控制。
- 相比静态最优偏航控制，发电量提升4.30%，接近翻倍。
- 适合关注风电优化与智能控制的能源研究者和工程师。
传统风场控制采用单机独立运行以最大化单机出力，但全厂协同尾流转向可显著提升整体发电量。尽管动态闭环控制在流体控制中已验证有效，但风场优化仍依赖忽略湍流动态的静态低精度模拟。本文首次将强化学习（RL）控制器直接集成于高保真大涡模拟（LES），实现对大气湍流的实时响应，通过协同动态控制策略提升发电效率。相比基线运行，该方法使风场总发电量提升4.30%，近乎翻倍于静态最优偏航控制（经贝叶斯优化获得）的2.19%增益。结果表明，动态流响应控制是风场优化的变革性方法，对实现碳中和目标具有直接意义。
原文摘要 · Abstract (English)
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.
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