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Simplified dynamic modeling of single-tank thermal energy storage systems

Author

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  • Raccanello, J.
  • Rech, S.
  • Lazzaretto, A.

Abstract

The paper analyzes the behavior of the most common single-tank configurations of thermal storage capacities that involve transfer of mass (open systems) or/and heat (closed/hybrid systems), in presence or not of solid or phase-change filler materials. This is done using simplified dynamic models of different complexity: zero-dimensional, quasi-one-dimensional and one-dimensional. The aim is to show whether, and within which limits, simplified models can substitute more complex and time consuming two or three-dimensional ones. The comparison with experimental data showed that the one-dimensional model is able to predict performance, thermal stratification and any other relevant aspect of the storage behavior with sufficient accuracy. Conversely, the other two approaches do not show this ability because they neglect or underestimate the heat transferred along the longitudinal direction of the storage capacity. One-dimensional simulations show that higher charging efficiency can be achieved using open systems (+2.4% points compared to closed systems) and reducing the diameter of the filler material particles, if present. In closed systems the length of the cold heat exchanger can be reduced to half of the tank height with minimum decrease (about −0.6% after 1 h) of the temperature at which the thermal energy can be taken from the storage.

Suggested Citation

  • Raccanello, J. & Rech, S. & Lazzaretto, A., 2019. "Simplified dynamic modeling of single-tank thermal energy storage systems," Energy, Elsevier, vol. 182(C), pages 1154-1172.
  • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:1154-1172
    DOI: 10.1016/j.energy.2019.06.088
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    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Camille Pajot & Nils Artiges & Benoit Delinchant & Simon Rouchier & Frédéric Wurtz & Yves Maréchal, 2019. "An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data," Energies, MDPI, vol. 12(19), pages 1-22, September.
    3. Maouris, Georgios & Sarabia Escriva, Emilio Jose & Acha, Salvador & Shah, Nilay & Markides, Christos N., 2020. "CO2 refrigeration system heat recovery and thermal storage modelling for space heating provision in supermarkets: An integrated approach," Applied Energy, Elsevier, vol. 264(C).
    4. Parida, Dipti Ranjan & Advaith, S. & Dani, Nikhil & Basu, Saptarshi, 2022. "Assessing the impact of a novel hemispherical diffuser on a single-tank sensible thermal energy storage system," Renewable Energy, Elsevier, vol. 183(C), pages 202-218.
    5. Marika Pilou & George Kosmadakis & George Meramveliotakis, 2023. "Modeling of an Integrated Renewable-Energy-Based System for Heating, Cooling, and Electricity for Buildings," Energies, MDPI, vol. 16(12), pages 1-29, June.
    6. Klemen Sredenšek & Sebastijan Seme & Bojan Štumberger & Miralem Hadžiselimović & Amor Chowdhury & Zdravko Praunseis, 2021. "Experimental Validation of a Dynamic Photovoltaic/Thermal Collector Model in Combination with a Thermal Energy Storage Tank," Energies, MDPI, vol. 14(23), pages 1-21, December.
    7. Joseph Rendall & Fernando Karg Bulnes & Kyle Gluesenkamp & Ahmad Abu-Heiba & William Worek & Kashif Nawaz, 2021. "A Flow Rate Dependent 1D Model for Thermally Stratified Hot-Water Energy Storage," Energies, MDPI, vol. 14(9), pages 1-17, May.

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