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A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids

Author

Listed:
  • Tianhao Wang

    (Electric Power Research Institute, State Grid Tianjin Electric Power Company, No. 8, Haitai Huake 4th Road, Huayuan Industrial Zone, Binhai High Tech Zone, Tianjin 300384, China)

  • Shiqian Ma

    (Electric Power Research Institute, State Grid Tianjin Electric Power Company, No. 8, Haitai Huake 4th Road, Huayuan Industrial Zone, Binhai High Tech Zone, Tianjin 300384, China)

  • Zhuo Tang

    (School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China)

  • Tianchun Xiang

    (State Grid Tianjin Electric Power Company, No. 39 Wujing, Guangfu Street, Hebei District, Tianjin 300010, China)

  • Chaoxu Mu

    (School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China)

  • Yao Jin

    (State Grid Tianjin Electric Power Company, No. 39 Wujing, Guangfu Street, Hebei District, Tianjin 300010, China)

Abstract

This paper proposes a novel cooperative voltage control strategy for an isolated microgrid based on the multi-agent advantage actor-critic (MA2C) algorithm. The proposed method facilitates the collaborative operation of a distributed energy system (DES) by adopting an attention mechanism to adaptively boost information processing effectiveness through the assignment of importance scores. Additionally, the algorithm we propose, executed through a centralized training and decentralized execution framework, implements secondary control and effectively restores voltage deviation. The introduction of an attention mechanism alleviates the burden of information transmission. Finally, we illustrate the effectiveness of the proposed method through a DES consisting of six energy nodes.

Suggested Citation

  • Tianhao Wang & Shiqian Ma & Zhuo Tang & Tianchun Xiang & Chaoxu Mu & Yao Jin, 2023. "A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids," Energies, MDPI, vol. 16(15), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5653-:d:1203953
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    References listed on IDEAS

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    1. Nikita Tomin & Nikolai Voropai & Victor Kurbatsky & Christian Rehtanz, 2021. "Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
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