A survey on resilient microgrid system from cybersecurity perspective

Abstract

Due to increases in communication speed, computation, the liberalization of the electrical service business, and the environmental impact of traditional power generation technologies, Distributed Energy Resources (DERs) power systems such as microgrids are gaining in popularity. It is therefore imperative to develop resilient microgrid systems capable of withstanding cyber physical threats. The capacity to integrate Machine Learning (ML) and Deep Learning (DL) to analyze energy data has created opportunities for businesses and academia to explore the possibilities of enhancing the cybersecurity of microgrid systems. This study surveys and discusses recent developments, challenges, and opportunities in cybersecurity for microgrid systems, from both attack and defense perspectives. In this paper, we address the current state and future directions in cybersecurity in industrial communication networks, and endpoint security in distributed control systems. This paper discusses attack types including Man-In-The-Middle (MITM), False Data Injection (FDI), and Distributed Denial of Service (DDoS) attacks, alongside defensive mechanisms including AI-based detection and multi-layered security frameworks. Furthermore, this survey offers comprehensive insights into benchmark datasets and open-source tools frequently utilized in experimental research and practical applications. It includes an in-depth comparison, discussion, and opportunities for future research to guide the research community’s focus and advancing progress in the field.