Guest Editors
Dr. Junwei Ma
Email: jwma@tamu.edu
Affiliation: Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, United States
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Research Interests: energy infrastructure resilience and adaptation, applied AI, smart cities, disaster risk reduction, climate adaptation and sustainable development, social equity and environmental justice
Dr. Bo Li
Email: libo@tamu.edu
Affiliation: Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, United States
Homepage:
Research Interests: urban energy systems, climate adaptation, AI for urban science, energy infrastructure resilience and reliability
Dr. Xiangpeng Li
Email: xplli@tamu.edu
Affiliation: Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, United States
Homepage:
Research Interests: infrastructure resilience, AI for urban resilience, power outages and restoration, power outage deprivation cost, equity and environmental justice
Summary
Climate change, extreme weather, and compound hazards are increasingly exposing vulnerabilities in energy systems, leading to widespread power outages, cascading failures, and prolonged recovery. Traditional reliability metrics and rule-based operational strategies are often insufficient to capture the spatiotemporal complexity, interdependencies, and social consequences of energy disruptions during disasters. Recent advances in artificial intelligence (AI), data-driven modeling, and high-resolution sensing offer transformative opportunities to enhance the resilience, adaptability, and decision-making capacity of energy systems under extreme conditions.
The aim of this Special Issue is to advance engineering knowledge on AI-enabled methods, models, and systems that improve the preparedness, real-time operation, and post-disaster recovery of energy infrastructures. The scope spans power grids, integrated and hybrid energy systems, microgrids, and energy storage, with emphasis on disaster contexts such as hurricanes, floods, heatwaves, wildfires, and cold extremes. Contributions are encouraged that develop novel AI-driven analytics, control strategies, and resilience metrics that support robust operation, rapid restoration, and equitable energy outcomes.
Topics of interest include, but are not limited to:
· AI and machine learning for power outage prediction, vulnerability assessment, and resilience quantification
· Intelligent energy system operation and control during extreme events
· AI-driven restoration planning, recovery optimization, and resilience curves
· Data-driven assessment of energy equity, social impacts, and policy implications during disasters
· Integration of real-time sensing, digital twins, and decision-support systems for resilient energy engineering
Keywords
artificial intelligence, energy system resilience, power outages, extreme events and disasters, smart grids and energy networks, adaptive energy systems, disaster response and recovery, energy infrastructure vulnerability
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