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AC/DC hybrid distribution network reconfiguration with microgrid formation using multi-agent soft actor-critic

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  • Wu, Tao
  • Wang, Jianhui
  • Lu, Xiaonan
  • Du, Yuhua

Abstract

Recent extreme events trigger tremendous concerns on distribution system resilience. Meanwhile, high penetration of inverter-interfaced distributed generators (DGs) and diversified source and load mix facilitate the development and implementation of hybrid AC and DC distribution networks (HDNs). This paper proposes a deep reinforcement learning-based (DRL) approach for distribution network reconfiguration with microgrid formation in face of extreme events. The proposed optimization model facilitates critical service restoration by forming isolated sections nested inside the HDNs when severe power outages occur (e.g., disconnection from the main grid). The operational characteristics of isolated HDNs (e.g., droop-controlled nodes in AC and DC sections, lack of slack buses in autonomous operation, etc.) are considered. To reduce the computational burden, a multi-agent soft actor-critic (MA-SAC) approach is developed to solve the proposed reconfiguration problem, where multiple agents coordinately control circuit breakers to sectionalize the HDNs and can cater for different system states and scales. Simulation tests are conducted in two test systems to verify the validity of the proposed approach.

Suggested Citation

  • Wu, Tao & Wang, Jianhui & Lu, Xiaonan & Du, Yuhua, 2022. "AC/DC hybrid distribution network reconfiguration with microgrid formation using multi-agent soft actor-critic," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921014604
    DOI: 10.1016/j.apenergy.2021.118189
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Citations

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    Cited by:

    1. Ning Xin & Laijun Chen & Linrui Ma & Yang Si, 2022. "A Rolling Horizon Optimization Framework for Resilient Restoration of Active Distribution Systems," Energies, MDPI, vol. 15(9), pages 1-14, April.
    2. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
    3. Zhang, Lu & Yu, Shunjiang & Zhang, Bo & Li, Gen & Cai, Yongxiang & Tang, Wei, 2023. "Outage management of hybrid AC/DC distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch," Applied Energy, Elsevier, vol. 335(C).
    4. Xie, Haipeng & Tang, Lingfeng & Zhu, Hao & Cheng, Xiaofeng & Bie, Zhaohong, 2023. "Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    6. Zhang, Guozhou & Hu, Weihao & Cao, Di & Zhou, Dao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Coordinated active and reactive power dynamic dispatch strategy for wind farms to minimize levelized production cost considering system uncertainty: A soft actor-critic approach," Renewable Energy, Elsevier, vol. 218(C).
    7. Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.

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