NEUP CINR-19-16879: Proactive Hybrid Nuclear with Load Forecasting

This project develops new capabilities of design and dispatch optimization of nuclear hybrid energy systems (NHES) in RAVEN. Blended (physics-based and data-driven) machine learning will be applied to forecast demand and production of thermal and electrical loads. Two experimental case studies are proposed to test the software developments with a lab-scale thermal energy storage and with a large district energy system. As a final step, the software developments will be generalized to other NHES.

Additional Info

Field	Value
Awarded Institution	Brigham Young University
PI	John Hedengren