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Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems

Author

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  • Xiaofeng Guo

    (ESIEE Paris, University of Paris Est, F-93162 Noisy le Grand, France
    LIED-PIERI, UMR 8236, CNRS, University of Paris Diderot (Paris 7), F-75013 Paris, France)

  • Alain Pascal Goumba

    (ESIEE Paris, University of Paris Est, F-93162 Noisy le Grand, France
    EFFICACITY, 14-20 boulevard Newton, F-77447 Marne la Vallée CEDEX 2, France)

  • Cheng Wang

    (Jiangsu Provincial Key Laboratory of Oil & Gas Storage and Transportation Technology, Changzhou University, Changzhou, Jiangsu 213016, China)

Abstract

Large-scale solar heating for the building sector requires an adequate Thermal Energy Storage (TES) strategy. TES plays the role of load shifting between the energy demand and the solar irradiance and thus makes the annual production optimal. In this study, we report a simplified algorithm uniquely based on energy flux, to evaluate the role of active TES on the annual performance of a large-scale solar heating for residential thermal energy supply. The program considers different types of TES, i.e., direct and indirect, as well as their specifications in terms of capacity, storage density, charging/discharging limits, etc. Our result confirms the auto-regulation ability of indirect (latent using Phase Change Material (PCM), or Borehole thermal storage (BTES) in soil) TES which makes the annual performance comparable to that of direct (sensible with hot water) TES. The charging and discharging restrictions of the latent TES, until now considered as a weak point, could retard the achievement of fully-charged situation and prolong the charging process. With its compact volume, the indirect TES turns to be promising for large-scale solar thermal application.

Suggested Citation

  • Xiaofeng Guo & Alain Pascal Goumba & Cheng Wang, 2019. "Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems," Energies, MDPI, vol. 12(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1948-:d:233115
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    References listed on IDEAS

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    6. Zhao, Yin & Gong, Mingju & Sun, Jiawang & Han, Cuitian & Jing, Lei & Li, Bo & Zhao, Zhixuan, 2023. "A new hybrid optimization prediction strategy based on SH-Informer for district heating system," Energy, Elsevier, vol. 282(C).
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