Hybrid Nuclear Energy Systems

Projects.HybridNuclear History

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July 29, 2021, at 01:18 PM by 136.36.4.38 -
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July 29, 2021, at 01:12 PM by 136.36.4.38 -
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-> '''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.
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-> '''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.
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July 29, 2021, at 01:10 PM by 136.36.4.38 -
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-> 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. [[https://authors.elsevier.com/c/1dUQX1HxM4vVGY|Article]]
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* 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. [[https://authors.elsevier.com/c/1dUQX1HxM4vVGY|Article]]
July 29, 2021, at 01:06 PM by 136.36.4.38 -
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# 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. [[https://authors.elsevier.com/c/1dUQX1HxM4vVGY|Article]]
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-> 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. [[https://authors.elsevier.com/c/1dUQX1HxM4vVGY|Article]]

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-> '''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.
July 29, 2021, at 01:05 PM by 136.36.4.38 -
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Coming soon...
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# 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. [[https://authors.elsevier.com/c/1dUQX1HxM4vVGY|Article]]
May 18, 2020, at 10:34 PM by 136.36.211.159 -
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Support for this research comes from the DOE NEUP Project 19-16879: Proactive Hybrid Nuclear with Load Forecasting.
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Support for this research comes from the DOE NEUP Project 19-16879: Proactive Hybrid Nuclear with Load Forecasting. This project is a collaboration between [[https://apm.byu.edu/prism|BYU Process Research and Intelligent Systems Modeling (PRISM) Group]], the [[https://kodypowell.che.utah.edu/|Energy Systems Research Group]] at the University of Utah, and Idaho National Labs (INL).
May 18, 2020, at 10:31 PM by 136.36.211.159 -
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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.
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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.
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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.
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!!!! Publications

Coming soon...
May 18, 2020, at 10:30 PM by 136.36.211.159 -
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(:title Hybrid Nuclear Energy Systems:)

(:keywords research, dispatch, optimization, planning, energy, nuclear, small modular, reactor:)

(:description Hybrid Nuclear Energy System Design and Control at Brigham Young University:)

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.