Short-term voltage stability emergency control strategy pre-formulation for massive operating scenarios via adversarial reinforcement learning

Congbo Bi, Di Liu, Lipeng Zhu, Shiyang Li, Xiaochen Wu and Chao Lu

Applied Energy, 2025, vol. 389, issue C, No S0306261925004817

Abstract: The high penetration of renewable energy shifts the randomness and uncertainty of power systems, challenging traditional interpolation-based emergency control strategy pre-formulation. Deep reinforcement learning (DRL)-based approaches provide a promising alternative to tackle this issue. However, the applicability of prevalent DRL-based methods is limited by the safety concerns in low-frequency high-risk conditions and by the computational costs for tackling various fault scenarios. To address these issues, we develop a safe reinforcement learning (SRL)-based emergency control framework against short-term voltage instability. First, considering the need for scanning numerous fault scenarios in large-scale power systems, we employ u-shapelet-based time series clustering to group faults with similar response characteristics, which simplifies the construction of emergency control strategies for various fault scenarios while guaranteeing performance. After clustering, a neural network-based security margin estimator for safety quantification is incorporated with a risky action corrector via the estimated margin’s gradient projection for safety guarantee to form an SRL-enabled decision-making agent, achieving efficient and safe strategy pre-formulation. Further, adversarial sample generation is performed to gather extreme scenarios for the SRL-based agent, improving robustness and applicability. Comprehensive tests on the IEEE 39-bus system and the Guangdong Provincial Power Grid demonstrate the effectiveness of the proposed framework.

Keywords: Emergency control; Safe reinforcement learning; Adversarial generative network; Clustering; Voltage stability (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125751

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