Advancements in grid resilience: Recent innovations in AI-driven solutions - Sana Hafez; Mohammad Alkhedher; Mohamed Ramadan; Abdalla Gad; Marah Alhalabi; Maha Yaghi; Mohamed Jama; Mohammed Ghazal (Results in Engineering)
In response to increasing complexities in modern power grids posed by the integration of smart grids, smart meters, microgrids, Renewable Energy Resources (RERs), and Distributed Energy Resources (DERs), there has been a growing emphasis on leveraging Artificial Intelligence (AI) to enhance the resilience of power grids. In recent years, there has been several advancements in employing various AI methodologies such as Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) for dealing with the challenges caused by climate change, cybersecurity threats, and evolving energy demands. The applications of AI can span across different aspects of grid resilience, including fault detection and diagnosis, predictive maintenance, optimal resource allocation, demand-side management, and cyber resilience. This review provides an overview of the recent advancements in implementing AI to enhance grid resilience. It discusses the challenges and future directions for research and development in this rapidly evolving field.