AI-Driven Sustainability and Resiliency in Grid-of-Microgrids: Pre- and Post-event Strategies
Abstract
Over the last couple of decades, a transition has evolved from the traditional centralized power grid to a distributed grid of microgrids (GoMGs) dominated by power electronics-based generation. The primary objective of this evolution is to achieve a sustainable, resilient grid while ensuring clean, reliable, and self-adaptive energy access across various operating conditions. However, this new energy paradigm, with high penetration of renewable energy, poses amplified challenges in controlling and securing GoMGs to maintain resiliency, reliability, sustainability, and operational stability. To address these issues and ensure a smarter, cybersecure, data-driven, and sustainable MG, many researchers at the intersection of power electronics, power systems, and artificial intelligence (AI) are exploring ways to develop and implement efficient and reliable AI-based techniques. This article sheds light on the multidimensional perspectives of sustainability and resiliency in GoMGs, focusing on security, stability, accessibility, and scalability in relation to the current state of technological maturity. Building on this vision, a futuristic roadmap is presented to enhance sustainability and resiliency using cutting-edge AI applications, enabling pre-event strategies, such as prediction, optimization, and adaptation, as well as post-event mitigation and restoration techniques.