부산대학교 도서관

Green energy production is typically decentralized, and the ecosystem of production, transmission, and distribution differs significantly from centralized systems. Therefore, ensuring the resilience of green energy infrastructure demands a distinct approach, particularly regarding the security and safety aspects of these CIs. Green Energy CIs have less inherent protection, along with ancillary protection facilities compared to conventional power plants. This underscores the need to leverage AI to enhance the safety and security of green energy infrastructures, providing efficient and cost-effective solutions. This study aims to provide a comprehensive overview of AI implementations for enhancing the security and safety of green energy. Although this study is a work in progress, the present article will specifically delve into the resilience aspects of green energy infrastructures. Given the focus on AI implementation and data-driven solutions, we approach energy systems from a cyber-physical and societal perspective, emphasizing their broader impact on society. The ongoing study has unveiled significant improvements in resilience through the application of AI methods and data-driven models, such as machine learning, deep learning, neural networks, multiagent systems, big data, and data mining. Furthermore, we explore the challenges associated with integrating AI into green energy systems and investigate its various applications. This exploration aims to identify key features that will guide the development of novel approaches to enhancing the resilience of green energy systems through AI-based solutions for security and safety. Finally, results show a significant gap in the safety applications of AI. It received the least attention in the articles While the term ”safety” is frequently mentioned, even when the article’s primary focus is not on safety applications.