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Robust preventive and corrective security-constrained OPF for worst contingencies with the adoption of VPP: A safe reinforcement learning approach

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  • Wei, Xiang
  • Chan, Ka Wing
  • Wang, Guibin
  • Hu, Ze
  • Zhu, Ziqing
  • Zhang, Xian

Abstract

The rising frequency of extreme weather events calls for urgent measures to improve the resilience and reliability of power systems. This paper, therefore, presents a robust preventive-corrective security-constrained optimal power flow (PCSCOPF) model designed to strengthen power system reliability during N-k outages. The model integrates fast-response virtual power plants (VPPs), dynamically adjusting their injections to mitigate post-contingency overloads and maintain branch flows within emergency limits. Additionally, a novel approach combining deep reinforcement learning (DRL) with Lagrangian relaxation is introduced to efficiently solve the PCSCOPF decision-making problem. By framing the problem as a constrained Markov decision process (CMDP), the proposed Lagrangian-based soft actor-critic (L-SAC) algorithm optimizes control actions while ensuring constraint satisfaction during the exploration process. Extensive investigations have been conducted on the IEEE 30-bus and 118-bus systems to evaluate their computational efficiency and reliability.

Suggested Citation

  • Wei, Xiang & Chan, Ka Wing & Wang, Guibin & Hu, Ze & Zhu, Ziqing & Zhang, Xian, 2025. "Robust preventive and corrective security-constrained OPF for worst contingencies with the adoption of VPP: A safe reinforcement learning approach," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924023547
    DOI: 10.1016/j.apenergy.2024.124970
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