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operating modes: standby, charging, and discharging. The concrete and pipe geometries, fluid medium, pipe medium, and concrete medium are all capable of replacement by a user in the Modelica model. Reconfiguration is straightforward. The Python model is more specific to water and concrete materials matched in the Modelica model. The low-fidelity model results agree well with the high-fidelity model and only requires appropriate input and output formatting to be usable as a called function. The latent-heat thermal energy storage unit is based on a lab-scale experiment and has been shown to have good results relative to the lab experiment. A low-fidelity model has been developed that shows good complete melting and solidification results when compared to the high-fidelity model within Modelica. The paraffin wax experiment shows good potential for extension to other solid-liquid phase change materials. Continued work on this model will extend replaceable materials into the pipe and phase change material portions of the model. The third storage unit developed in FY20 was the packed bed thermocline unit. This unit was designed using a modified version of the Schumann equations capable of dealing with stagnant flow conditions. Transient runs have shown the capability of the thermocline model to operate under low or no-flow conditions, as well as to charge and discharge the system. Like the concrete models, the thermocline was developed with an inheritable material property class structure for both the fluid and filler, meaning it is customizable to the end users’ designation of fluid, and can be quickly reconfigured to meet the geometric needs. For the system-wide grid optimization problems that become apparent with the IES program, it was important to adopt low-level surrogate models for some of the thermal energy storage technologies. This was accomplished for the concrete and latent heat storage systems through the creation of Python files and work is planned to surrogate the thermocline model using machine-learning algorithms for understanding the physics of the unit. These lower order or binary files (Python or surrogate using machine learning) are computationally much cheaper to run and, thus, operate well when incorporated within the stochastic optimization problems run for the IES program. Moreover, economic data was collected for use within the FORCE and IES stochastic optimization framework. This information is incorporated within the FORCE platform. Through commencement of this work, a systems-level model of concrete, latent heat, and thermocline thermal energy storage systems with associated control systems have been created. Now that these models are available, they can be utilized within different integrated energy park configurations to understand optimal system operation, control, and dispatching. Moreover, given the generic nature of the models, industrial partner technologies (e.g., Storworks Power, EnergyNest) can be quickly added to the repository using the existing models as a basis. 6. ACKNOWLEDGEMENTS This work was supported by the Integrated Energy Systems program at Idaho National Laboratory under DOE Operations contract number DE-AC07-05ID14517. 7. REFERENCES [1] C. Rabiti, A. S. Epiney, P. Talbot, J. S. Kim, S. Bragg-Sitton, A. Alfonsi, A. Yigitoglu, S. Greenwood, S. M. Cetiner, F. Ganda, G. Maronati. September 2017. “Status Report on Modeling and Simulation Capabilities for Nuclear-Renewable Hybrid Energy Systems.” INL/EXT-17-43441, Idaho National Laboratory. [2] Jong S. Kim, Michael McKellar, Shannon Bragg-Sitton, Richard Boardman. October 2016. “Status Report on the Component Models Developed in the Modelica Framework: High-Temperature Steam Electrolysis & Gas Turbine Power Plant.” INL/EXT-16-40305, Revision 0, Idaho National Laboratory. [3] Jong S. Kim, Konor L. Frick. May 2018. “Status Report on the Component Models Developed in the Modelica Framework: Reverse Osmosis Desalination Plant & Thermal Energy Storage.” 29PDF Image | Thermal Energy Storage Model Development
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