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Frequency control for islanded AC microgrid based on deep reinforcement learning

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  • Xianggang Liu
  • Zhi-Wei Liu
  • Ming Chi
  • Guixi Wei

Abstract

The incorporation of intermittent and stochastic renewable energy into a microgrid creates frequent fluctuations, which provides new challenges in frequency control. This paper deals with the frequency control problem in the islanded AC microgrid (IACMG) via a model-free deep reinforcement learning (DRL) method, which includes offline learning and online control. Twin-delayed deep deterministic policy gradient is involved to improve the performance of the agent to minimise the frequency deviation. The advantage of the proposed method is self-adaptive to the uncertain IACMG model including renewable energy sources. Finally, the effectiveness and robustness of the proposed controller is demonstrated by four simulation scenarios.

Suggested Citation

  • Xianggang Liu & Zhi-Wei Liu & Ming Chi & Guixi Wei, 2024. "Frequency control for islanded AC microgrid based on deep reinforcement learning," Cyber-Physical Systems, Taylor & Francis Journals, vol. 10(1), pages 43-59, January.
    • Handle: RePEc:taf:tcybxx:v:10:y:2024:i:1:p:43-59
    DOI: 10.1080/23335777.2022.2130434
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