Reinforcement Learning for Water-Energy Infrastructure Resilience and Evolution

This white paper outlines a foundational AI-based framework for improving resilience across the energy-water nexus, with a focus on electric grid and water system interdependencies under hydrologic and weather extremes. The focal area spans both "water for energy" and "energy for water," emphasizing multi-scale coordination and adaptive planning. The challenge addressed is the lack of integrated, dynamic decision-support tools to manage cascading risks between electricity and water systems, especially under compound extreme events. The proposed near-term opportunity centers on a two-part architecture: (1) a foundation model that unifies multimodal data to simulate system behavior and generate scenarios, and (2) reinforcement learning agents trained to develop adaptive operational strategies and inform long-term infrastructure planning. The approach incorporates explainable AI and physics-informed constraints to ensure transparency and trust. A regional case study serves as a testbed to validate the framework's ability to manage system tradeoffs and mitigate disruptions. Success will be measured by reductions in service disruptions, improved performance of RL-driven strategies, scenario diversity, infrastructure risk mapping, and the usability and integration of AI tools into planning and operations.