Hybrid Nuclear Energy Systems

This project develops new capabilities of design and dispatch optimization of nuclear hybrid energy systems (NHES) in the "Risk Analysis Virtual Environment (RAVEN)" modelling software. 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.

Support for this research comes from the DOE NEUP Project 19-16879: Proactive Hybrid Nuclear with Load Forecasting. This project is a collaboration between BYU Process Research and Intelligent Systems Modeling (PRISM) Group, the Energy Systems Research Group at the University of Utah, and Idaho National Labs (INL).


NHES with H2 Storage

Ho, A., Mohammadi, K., Hedengren J.D., Memmott, M., Powell, K.M., Dynamic simulation of a novel nuclear hybrid energy system with large-scale hydrogen storage in an underground salt cavern, International Journal of Hydrogen Energy, 29 July 2021. Article
Abstract: In this study, a nuclear hybrid energy system (NHES) with large-scale hydrogen storage integrated with a gas turbine cycle is proposed as a flexible system for load following. The proposed system consists of a nuclear reactor, a steam Rankine cycle, a hydrogen electrolyzer, a storage system for hydrogen in an underground salt cavern, and a Brayton cycle that uses hydrogen as fuel to generate additional electricity to meet peak demand. A dynamic mathematical model is developed for each subsystem of the NHES. To evaluate the potential benefits of the system, a one-year study is conducted, using scaled grid demand data from ISO New England. The dynamic simulation results show that the system is capable of meeting the demand of the grid without additional electricity from outside sources for 93% of the year, while decreasing the number of ramping cycles of the nuclear reactor by 92.7%. There is also potential for economic benefits as the system only had to ramp up and down 7.4% of the year, which increased the nuclear capacity factor from 86.3% to 98.3%. The simulation results show that the proposed hybrid system improves the flexibility of nuclear power plants, provides more electricity, and reduces greenhouse gas emissions.

Grid Energy Benchmarks

Gates, N.S., Hill, D.C., Billings, B.W., Powell, K.M., Hedengren, J.D., Benchmarks for Grid Energy Management with Python Gekko, 60th Conference on Decision and Control (CDC), Austin, TX, USA, December 13-15, 2021. Preprint | Benchmarks
Abstract: Recent grid energy problems in California and Texas highlight the need for optimized grid design and operation. The complexity of the electric grid presents difficult control problems that require powerful solvers and efficient formulations for tractable solutions. The Gekko Optimization Suite is a machine learning and optimization package in Python for optimal control with differential algebraic equations and is capable of solving complex grid design and control problems. A series of non-dimensional benchmark cases are proposed for grid energy production. These include (I) load following, (II) cogeneration, (III) tri-generation, and energy storage with (IV) constant production, (V) load following, and (VI) cogeneration. Individual case studies include ramp rate constraints, power production, and energy storage operation as design variables. The tutorials demonstrate methods to solve control problems with sequential and simultaneous solutions of the objective and dynamic constraints. While these tutorials are specific to grid energy system optimization, the tutorials also demonstrate how to efficiently solve large-scale nonlinear dynamic systems with a trade-off analysis between sequential and simultaneous methods.