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Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

Author

Listed:
  • Dong Hua

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Fei Peng

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Suisheng Liu

    (Guangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, China)

  • Qinglin Lin

    (Guangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, China)

  • Jiahui Fan

    (Guangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, China)

  • Qian Li

    (Consultation and Evaluation Center, Energy Development Research Institute, CSG, Guangzhou 510000, China)

Abstract

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

Suggested Citation

  • Dong Hua & Fei Peng & Suisheng Liu & Qinglin Lin & Jiahui Fan & Qian Li, 2025. "Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning," Energies, MDPI, vol. 18(2), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:333-:d:1566377
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