Modeling electrical particle thermal energy storage systems for long-duration, grid-electricity storage applications

Jeffrey Gifford, Xingchao Wang, Zhiwen Ma and Robert Braun

Applied Energy, 2024, vol. 371, issue C, No S0306261924009048

Abstract: Particle-based thermal energy storage technologies could facilitate increased penetration of variable renewable energy resources by decreasing the costs of providing dispatchable, carbon-free generation resources. However, the technology is not yet commercially available, and its performance has not been thoroughly characterized. This work creates a system modeling platform for particle-based thermal energy storage systems that can characterize performance and is transferable to emerging forms and applications of particle-based thermal energy storage. A library of key component models developed for particle-based thermal energy storage is described and benchmarked against high-fidelity models or with experimental results. A notional 135 MWe power plant employing particle thermal energy storage for grid-scale, electricity storage applications is conceived and simulated. The results show these systems can achieve greater than 50% annual round-trip efficiencies in a variety of durations, locations, dispatch schedules, and key design parameters. A 1.5% decrease in round-trip efficiency was observed when increasing storage duration from 10 to 100 h. Dispatch schedules can impact round-trip efficiency by greater than 0.5%. In regards to component design and performance, the insulation thickness of the silos and the heat exchanger approach temperature impacted performance the most. Particle-based thermal energy storage has the potential to assist in the decarbonization of not only electricity but industry and building sectors as well. This work enables the analysis of the performance of systems in these sectors and serves as a detailed technical model for design optimization in the future.

Keywords: Particle thermal energy storage; Long-duration energy storage; Modelica; System modeling; Electrical energy storage (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123521

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