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Multi-Agent Deep Reinforcement Learning-Based Distributed Voltage Control of Flexible Distribution Networks with Soft Open Points

Author

Listed:
  • Liang Zhang

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Fan Yang

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Dawei Yan

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Guangchao Qian

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Juan Li

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Xueya Shi

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Jing Xu

    (State Grid Tianjin Economic Research Institute, Tianjin 300171, China)

  • Mingjiang Wei

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Haoran Ji

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Hao Yu

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

Abstract

The increasing number of distributed generators (DGs) leads to the frequent occurrence of voltage violations in distribution networks. The soft open point (SOP) can adjust the transmission power between feeders, leading to the evolution of traditional distribution networks into flexible distribution networks (FDN). The problem of voltage violations can be effectively tackled with the flexible control of SOPs. However, the centralized control method for SOP may make it difficult to achieve real-time control due to the limitations of communication. In this paper, a distributed voltage control method is proposed for FDN with SOPs based on the multi-agent deep reinforcement learning (MADRL) method. Firstly, a distributed voltage control framework is proposed, in which the updating algorithm of the intelligent agent of MADRL is expounded considering experience sharing. Then, a Markov decision process for multi-area SOP coordinated voltage control is proposed, where the control areas are divided based on electrical distance. Finally, an IEEE 33-node test system and a practical system in Taiwan are used to verify the effectiveness of the proposed method. It shows that the proposed multi-area SOP coordinated control method can achieve real-time control while ensuring a better control effect.

Suggested Citation

  • Liang Zhang & Fan Yang & Dawei Yan & Guangchao Qian & Juan Li & Xueya Shi & Jing Xu & Mingjiang Wei & Haoran Ji & Hao Yu, 2024. "Multi-Agent Deep Reinforcement Learning-Based Distributed Voltage Control of Flexible Distribution Networks with Soft Open Points," Energies, MDPI, vol. 17(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5244-:d:1503720
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    References listed on IDEAS

    as
    1. Li, Peng & Ji, Haoran & Yu, Hao & Zhao, Jinli & Wang, Chengshan & Song, Guanyu & Wu, Jianzhong, 2019. "Combined decentralized and local voltage control strategy of soft open points in active distribution networks," Applied Energy, Elsevier, vol. 241(C), pages 613-624.
    2. Ricardo de Oliveira & Leonardo Willer de Oliveira & Edimar José de Oliveira, 2023. "Optimization Approach for Planning Soft Open Points in a MV-Distribution System to Maximize the Hosting Capacity," Energies, MDPI, vol. 16(3), pages 1-22, January.
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