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The contribution of utility-scale wind energy to primary frequency control significantly boosts overall grid reliability. The primary frequency control capability of wind turbines is subject to constraints dictated by fluctuating wind speeds and the set load-shedding coefficient. The optimal contribution of wind turbines to primary frequency control is dictated by the collective performance of the entire power system, demanding the development of adaptive control strategies. In this paper, a deep reinforcement learning-based dynamic strategy of wind turbine generators for primary frequency control is proposed. Taking advantage of the deep deterministic policy gradient (DDPG) algorithm to deal with the state and action of multidimensional continuous system, the dynamic control strategy considering the variable wind speed, load shedding coefficient and power grid state is realized. The effectiveness of the proposed approach was confirmed through simulation studies on the IEEE 39-bus system.

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Deep Reinforcement Learning-Based Dynamic Strategy of Wind Turbine Generators for Primary Frequency Control

  • 会议论文

  • 发表日期：

    2026年

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  • Jia Ning,
  • Zhixian Wang,
  • Wenke Zhou,
  • Ying Wang

摘要

The contribution of utility-scale wind energy to primary frequency control significantly boosts overall grid reliability. The primary frequency control capability of wind turbines is subject to constraints dictated by fluctuating wind speeds and the set load-shedding coefficient. The optimal contribution of wind turbines to primary frequency control is dictated by the collective performance of the entire power system, demanding the development of adaptive control strategies. In this paper, a deep reinforcement learning-based dynamic strategy of wind turbine generators for primary frequency control is proposed. Taking advantage of the deep deterministic policy gradient (DDPG) algorithm to deal with the state and action of multidimensional continuous system, the dynamic control strategy considering the variable wind speed, load shedding coefficient and power grid state is realized. The effectiveness of the proposed approach was confirmed through simulation studies on the IEEE 39-bus system.