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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.
Adaptive 3D Printed Micro-Hydropower for Resilient Grids: Digital-to-Deployment Framework
We propose to enhance the resilience of the U.S. electricity grid by enabling the rapid, adaptive, and on-demand manufacturing of site-specific micro-to-small hydropower systems for low-head dams. This approach directly addresses the nation's untapped 85,000+ non-powered dams (NPD...
Kim, P., Chawla, K., Hassen, A., Roschli, A., Post, B., Saha, S., and Mueller, R.
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.
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.