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AI-Enhanced Hydropower Systems: Smart Dams for a Resilient Future

This paper focuses on water-energy, using AI for smart, holistic hydropower operations to enhance water for energy resilience. The existing challenges include (a) increasing demand for water and electricity, requiring a shift to flexible, real-time hydropower operations due to ch...

Varadharajan, C., Ajami, N., Brodie, E., Ciulla, F., Falco, N., Feldman, D., Newcomer, M., Dwivedi, D., Li, Y., Nakata, R., Nakata, N., Nico, P., Williams, K., Mahoney, M., Ramakrishnan, L., and Cholia, 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.

Quantifying Energy Use across U.S. Water Sectors: A Scalable AI/ML-Driven Framework for Data Integration and Resilience Planning

This white paper explores opportunities to quantify and characterize energy use across major U.S. water sectors, including irrigation, water supply, and water treatment, with a focus on energy for water dimension. A scalable AI/ML-driven framework will be developed to harmonize fr...

Ghimire, G., Bhanja, S., and Martinez, R.

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.

A Hybrid AI-Optimization Framework for Resilient Hydropower Operations to Support Grid Stability, Extreme Weather Management, and Large-Scale Industrial Loads

This white paper supports a two-stage decision-support framework that links fast, probabilistic inflow forecasts from modern machine learning and generative AI with multistage stochastic optimization for hydropower scheduling. By propagating uncertainty from prediction into operat...

Ploussard, Q., and Feinstein, J.

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.

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.

An Energy-Water Nexus Foundation Model Ecosystem to Advance System Resiliency and Affordability

This white paper presents how an AI-driven foundation model ecosystem for hydropower and the electric grid, integrated with a large language model (LLM) agent can address the "Water for Energy" challenge and modernize coordinated planning and operations of the U.S. energy-water ne...

Kwon, J., Kim, K., Levin, T., and Botterud, A.

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.

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.

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