Energy grid management system with anomaly detection and Q-learning decision modules. (April 2023)

• Record Type: Journal Article
• Authors: Syu, Jia-Hao; Srivastava, Gautam; Fojcik, Marcin; Cupek, Rafał; Lin, Jerry Chun-Wei
• Is Part Of: Computers & electrical engineering. Volume 107(2023)
• Publication Date: 2023-04
• Subjects: Smart grid -- Energy management -- Q-learning -- Self-attention -- Operating reserve -- Subsidy; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2023.108639
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management.