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A Decision-Framework for Retrofitting or Decommissioning Hydropower Plants in the USA
This white paper presents a comprehensive decision framework for evaluating the retrofitting or decommissioning of aging hydropower plants in the United States, focusing on the critical intersection of water and energy. The U.S. hydropower sector, comprising approximately 2,000 pl...
Mishra, S., Kumar, S., Prairie, J., Giovando, J., Rajib, A., and Castillo, C.
Training, Testing, and Demonstration Capability for In-Conduit Hydropower in Water Systems
This white paper discusses the development of in-conduit hydro turbine testing and training capabilities to enhance energy-water resilience by promoting in-conduit hydropower systems. These systems are installed in irrigation or municipal water distribution pipes, and the project ...
Reese, S., and DeNeale, S.
Unlocking Small-scale Hydropower using Water Infrastructure for National Energy Security
Reliable local energy generation and long-duration energy storage are critical to balancing peak demands and providing distributed emergency backup near both urban and rural populations. However, small-scale (< 10 MW) hydropower faces barriers, including siting, permitting, water ...
Stokes-Draut, J., Taylor, M., and Rao, P.
Co-Development Opportunities Between Hydropower and Municipal Water and Wastewater Infrastructures
This white paper outlines opportunities to modernize U.S. water-energy infrastructure through co-development of hydropower with municipal water and wastewater systems. It highlights the untapped potential of integrating hydropower generation into new and existing water supply proj...
Chu, A., and DeNeale, S.
A Framework for Improving Locational Marginal Price Estimates for Technoeconomic Analysis of Hydropower Investments and Operational Decisions
The focus is on improving the analytics supporting hydropower investment decisions through the more accurate characterization of the locational marginal prices (LMPs) that will define future energy payments. Enhanced analytics around LMP prediction could also be used to improve op...
Balducci, P., DeSomber, K., and Zhou, Z.
Advancing Industrial Sector Water Management for Energy Recovery in Water Supply, In-Process, and Discharge Systems
This paper addresses both water-for-energy and energy-for-water by integrating hydropower generation to improve industrial water system resilience. Conduit hydropower is produced by capturing flowing water under pressure in closed-pipe systems and converting the energy into usable...
Bhandari, I., Billings, B., DeNeale, S., Chu, A., and Meira, K.
Hybrid Bioenergy and Hydropower Systems: Leveraging Heterogeneous Biomass and Wastes from Watersheds
Sandia National Laboratories and Idaho National Laboratory (INL) explore the water-for-energy nexus in this white paper, focusing on the impact of heterogeneous watershed waste materials on hydropower facilities. They identify key challenges--such as pile burning, wildfires, and d...
Quiroz-Arita, C., and Paudel, R.
Optimizing Hydropower Operations through Basin-Scale Coordination for Energy-Water Resilience
This white paper aligns with the Water for Energy focal area and advances energy-water resilience through basin-scale coordination of hydropower operations. It outlines a transition from top-down management approaches to bottom-up, stakeholder-driven approach to improve hydropower...
Davis, L., Corsair, H., Ossa, D., and Nachman, 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.
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.