Climate-Proofing Critical Energy Infrastructure: Smart Grids, Artificial Intelligence, and Machine Learning for Power System Resilience against Extreme Weather Events
Publication: Journal of Infrastructure Systems
Volume 30, Issue 1
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.
Practical Applications
Climate change exacerbates challenges in our energy systems, from aging infrastructure and a constantly shifting regulatory environment to cybersecurity risks and diversifying energy portfolios. Addressing these issues requires strategic investment in modern infrastructure, particularly smart grids enhanced by advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies are vital for enhancing power system resilience against climate impacts. Automated systems such as automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) provide real-time data crucial for managing extreme weather events. AI and ML contribute to predictive maintenance, preventing failures and blackouts. They also forecast grid loads during severe weather, facilitating proactive power distribution management to prevent blackouts. This comprehensive improvement in situational awareness promotes economic growth in the energy sector and supports sustainable, climate-resilient transformation. AI and ML not only improve energy distribution and efficiency but also promote conservation efforts and ensure reliable energy amidst a changing climate. Collaboration among utility managers, regulators, and governments is key, focusing on data access, verification, and adaptability. Strategies should be tailored to each utility’s unique challenges. Moreover, establishing technical standards is critical for enhancing power grid resilience against climate-induced extreme weather events.
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Data Availability Statement
No data, models, or code was generated or used during the study.
Acknowledgments
The author is grateful for the valuable input from anonymous reviewers, which significantly improved the manuscript during blind peer review. This research received partial support from the National Science Foundation (NSF) and the US Department of Energy (DOE) under NSF CA No. EEC-1041895. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect those of NSF or DOE.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 30 • Issue 1 • March 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Published online: Jan 3, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 3, 2024
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence and machine learning
- Building systems
- Climate change
- Climates
- Computer programming
- Computing in civil engineering
- Electric power
- Electrical systems
- Energy engineering
- Energy infrastructure
- Engineering fundamentals
- Environmental engineering
- Grid systems
- Infrastructure
- Infrastructure resilience
- Lifeline systems
- Systems engineering
- Systems management
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