Climate-Proofing Critical Energy Infrastructure: Smart Grids, Artificial Intelligence, and Machine Learning for Power System Resilience against Extreme Weather Events
Joseph Nyangon
Abstract
Electric power systems face heightened risks from climate change, on top of existing challenges like aging infrastructure, regulatory shifts, and cybersecurity threats. This paper explores how advanced technologies, including smart grids, artificial intelligence (AI), and machine learning, (ML), enhance the resilience of power systems against climate-driven extreme weather events. Drawing insights from resilience theory, the paper presents a state-of-the-art review of the literature on power system resilience, highlighting the escalating vulnerabilities of energy systems to weather-related disruptions. Although utilities currently use technologies like automated meter reading and advanced metering infrastructure to collect vital grid performance data, the lack of strategic collaboration often impedes effective data governance and sharing, thus undermining efficient responses to climate threats. The paper underscores the significance of distributed energy resources, long-duration energy storage, microgrids, and demand-side management. It further illustrates how AI and ML optimize smart grids to support these strategies. Proactive integration of smart grids with advanced technologies could significantly reduce climate-related costs compared to non-adaptive methods. Such proactive grid resilience strategies not only climate-proof energy infrastructure against climatic changes but also herald a modern, placed-based industrial transformation.