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Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems

Author

Listed:
  • Jing Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yiqi Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zhi Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Chunyan Rong

    (Institute of State Grid Hebei Electric Power Company Economic and Technological Research, Shijiazhuang 050000, China)

  • Tao Wang

    (Institute of State Grid Hebei Electric Power Company Economic and Technological Research, Shijiazhuang 050000, China)

  • Zhang Zhang

    (Institute of State Grid Hebei Electric Power Company Economic and Technological Research, Shijiazhuang 050000, China)

  • Suyang Zhou

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control scheme and has high computational efficiency and competitive potential for online application.

Suggested Citation

  • Jing Zhang & Yiqi Li & Zhi Wu & Chunyan Rong & Tao Wang & Zhang Zhang & Suyang Zhou, 2021. "Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems," Energies, MDPI, vol. 14(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3540-:d:574791
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    References listed on IDEAS

    as
    1. Mojgan Hojabri & Ulrich Dersch & Antonios Papaemmanouil & Peter Bosshart, 2019. "A Comprehensive Survey on Phasor Measurement Unit Applications in Distribution Systems," Energies, MDPI, vol. 12(23), pages 1-23, November.
    2. Kirstin Beyer & Robert Beckmann & Stefan Geißendörfer & Karsten von Maydell & Carsten Agert, 2021. "Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(7), pages 1-11, April.
    3. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
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    Cited by:

    1. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
    2. Di Liu & Junwei Cao & Mingshuang Liu, 2022. "Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties," Energies, MDPI, vol. 15(9), pages 1-20, April.

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