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Enabling Energy-Water Resilience in U.S. Data Centers: Water-Aware Grid Planning and Operations with Infrastructure-Compatible Cooling

Rapid, concentrated expansion of AI data centers is reshaping electricity demand profiles, local infrastructure requirements, and regional resource constraints. These facilities are often sized from tens to hundreds of megawatts with multi-gigawatt campus clusters. They are relati...

Zhou, Z., Wu, M., Qiu, F., Muehleisen, R., Zhao, D., Yan, E., and Worek, W.

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 ...

Jackson, N., and Rao, N.

Water Energy Planning Strategies to meet Emerging Load from Data Centers

This white paper discusses the urgent need for innovative management of energy and water resources in the context of rapidly growing data centers, particularly as U.S. electricity demand is projected to increase by 35 to 40% by 2040 due to the rise of artificial intelligence (AI)....

Bhowmik, P., Cafferty, K., Klise, K., and Jackson, N.

Integrated Planning for Resilient Energy-, Infrastructure-, and Hydro-Scapes

The focus of this paper is nationally-comprehensive, hyper-granular assessment of future energy expansion opportunities and challenges, considering multi-dimensional risks and consequent implications of large infrastructure additions on surface water and groundwater, spanning from...

McCollum, D., Rathore, S., Liu, Y., and Parish, E.

Hydrotwin: an AI-based physics and optimization framework for improved water resource resilience in systems experiencing adverse events

This white paper focuses on water for energy. As we move ahead into an uncertain future for water resource demand and supply, a massive challenge exists to optimize water systems to be resilient to a range of press/pulse extreme event states and conditions. Optimal decision-makin...

Bennett, K., Schwenk, J., and Garcia, M.

Agentic Optimization for Resilience in Hydropower Reservoir Systems

The growing electricity demand in the U.S. requires a resilient energy strategy. Hydropower is a major component of that strategy but is subject to environmental and human stressors that impact its dependability. AI-driven reinforcement learning, called "Agentic" learning, presen...

Schwenk, J., Garcia-Cardona, C., Bennett, K., Singh, S., and Brelsford, C.