Distribution System Resilience Under Asynchronous Information Using Deep Reinforcement Learning

• Published in: IEEE transactions on power systems Vol. 36; no. 5; pp. 4235 - 4245
• Main Authors: Bedoya, Juan Carlos, Wang, Yubo, Liu, Chen-Ching
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Asynchronous information; Decision making; Deep learning; deep reinforcement learning; distribution system restoration; Mathematical models; Monte Carlo methods; Monte Carlo tree search; Optimization; Power system restoration; Reinforcement learning; Resilience; resilient distribution systems; Restoration
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2021.3056543

Resilience of a distribution system can be enhanced by efficient restoration of critical load following a major outage. Existing models include optimization approaches that consider available information without incorporating the inherent asynchrony of data arrival during execution of the restoration plan. Failure to consider the asynchronous nature of information arrival can lead to underutilization of critical resources. Moreover, analytical models become computationally inefficient for large scale systems. On the other hand, artificial intelligence (AI)-based tools have demonstrated efficient results for power system applications. In this paper, it is proposed a Reinforcement Learning (RL) model that learns how to efficiently restore a distribution system after a major outage. The proposed approach is based on a Monte Carlo Tree Search to expedite the training process. The proposed model strategy provides a robust decision-making tool for asynchronous and partial information scenarios. The results, validated with the IEEE 13-bus test feeder and IEEE 8500-node distribution test feeder, demonstrate the effectiveness and scalability of the proposed method.