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Energy-Water Resilience

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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.

Advancing Energy-Water Resilience in California: A Framework for Integrated Water and Power Management Opportunities

This white paper identifies opportunities to advance energy-water resilience in California, using the Central Valley Project (CVP) and State Water Project (SWP) system. It addresses the critical intersection of both water for energy and energy for water management, emphasizing how...
Mork, E.

Mitigating Wildfire-Driven Hydrological Impacts on Energy Generation

Following a wildfire, increased sedimentation and altered flow regimes can disrupt water quantity and quality for years, impacting water users (e.g., municipal, industrial, agricultural) and undermining energy reliability, efficiency, and infrastructure longevity. Upstream restora...
Catalano, A., Ferencz, S., Michaels, R., and Hester, E.

Energy Needs for Water Supply Expansion in Urban Areas

Rising urban water demand requires new water supply provisioning in cities. Understanding and quantifying the energy-intensity of new water supply options can help planners identify pathways for water supply expansion with manageable energy requirements. There is opportunity for ...
Deines, J., Catalano, A., Sinnott, V., Yoon, J., Sun, N., and Duan, Z.

Aligning Bytes with Basins to Promote Data Center Growth in the U.S.

This white paper examines the water-energy tradeoffs of U.S. data-center expansion, highlighting how cooling choices, power sourcing, and siting decisions shape local water security, grid resilience, and watershed sustainability. The study focuses on integrating direct (on-site po...
Siddik, M., Turner, S., and Chinthavali, S.

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.

Wave Energy for Resilient Water Supply in Coastal Communities

This white paper examines the use of wave energy for desalination in coastal communities, focusing on enhancing resilience by providing a sustainable source of drinking water during disruptions (intersection of energy and water). Coastal areas, particularly remote ones, face signi...
Grasberger, J., and Klise, K.

Employing AI-Enabled Prognostics for Energy-Water Resilience

This white paper focuses on strengthening the resilience and efficiency of hydropower systems at the energy-water nexus through AI-enabled prognostics and hardware-in-the-loop (HIL) simulation. It emphasizes integrating digital intelligence with physical infrastructure to optimize...
Zhao, S., Qiu, F., and Agalgaonkar, Y.

Dynamic Population Mapping to Advance Energy-Water Resilience

Dynamic population mapping, combined with household-level energy and water demand profiling, enables precise, actionable forecasting and resilience planning for U.S. energy-water systems under variable population and climate conditions. Energy and water systems face growing stres...
Zimmer, A., Tuccillo, J., Jeong, B., and Urban, M.

Energy-Efficient Water Management Using Geo-AI for Evapotranspiration and Crop Risk Analysis

This white paper outlines a strategy for enhancing energy-efficient water management in agriculture through the application of Geospatial Artificial Intelligence (GeoAI). The focus is on developing a Dynamic Irrigation Window Scheduling (DIWS) system that optimizes irrigation base...
Cafferty, K., and Pacheco, C.

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