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Constrained multi-objective optimization of thermocline packed-bed thermal-energy storage

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  • Marti, Jan
  • Geissbühler, Lukas
  • Becattini, Viola
  • Haselbacher, Andreas
  • Steinfeld, Aldo

Abstract

A constrained multi-objective optimization approach is applied to optimize the exergy efficiency and material costs of thermocline packed-bed thermal-energy storage systems using air as the heat-transfer fluid. The axisymmetric packed-bed’s height, top and bottom radii, insulation-layer thicknesses, and particle diameter were chosen as design variables. The competing objectives of maximizing the exergy efficiency and minimizing the material costs were treated by a Pareto front. The Pareto front allows identifying the most efficient design for a given cost or the cheapest design for a given efficiency and is an important tool to find the best overall design of storage systems for a specific application. Constraints were imposed to obtain storage systems with specified capacities and limits on the air outflow temperatures during charging and discharging. The results showed that a storage shaped as a truncated cone with the smallest cross-section at the top has a higher exergy efficiency than storages shaped as cylinders or truncated cones with the largest cross-section at the top. The higher efficiency is attributed to the axial temperature distribution in the packed bed and the associated conduction heat losses across the insulated walls. The optimization of an industrial-scale storage allowed identifying a design with an exergy efficiency that was only 4.8% below that of the most efficient design, but a cost that was 81.3% lower than the cost of the most efficient design. Compared to brute-force design approaches, the optimization procedure can reduce the computational time by 91–99%.

Suggested Citation

  • Marti, Jan & Geissbühler, Lukas & Becattini, Viola & Haselbacher, Andreas & Steinfeld, Aldo, 2018. "Constrained multi-objective optimization of thermocline packed-bed thermal-energy storage," Applied Energy, Elsevier, vol. 216(C), pages 694-708.
    • Handle: RePEc:eee:appene:v:216:y:2018:i:c:p:694-708
    DOI: 10.1016/j.apenergy.2017.12.072
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    Cited by:

    1. Becattini, V. & Haselbacher, A., 2019. "Toward a new method for the design of combined sensible/latent thermal-energy storage using non-dimensional analysis," Applied Energy, Elsevier, vol. 247(C), pages 322-334.
    2. Gao, Long & Gegentana, & Liu, Zhongze & Sun, Baizhong & Che, Deyong & Li, Shaohua, 2020. "Multi-objective optimization of thermal performance of packed bed latent heat thermal storage system based on response surface method," Renewable Energy, Elsevier, vol. 153(C), pages 669-680.
    3. Wang, Yang & Li, Heping & Ortega-Fernández, Iñigo & Huang, Xuefeng & Jiang, Bo & Bielsa, Daniel & Palomo, Elena, 2021. "The time-varying radiation applied in the temperature-sensitive reaction system stabilized with heat storage technology," Applied Energy, Elsevier, vol. 283(C).
    4. Trevisan, Silvia & Wang, Wujun & Guedez, Rafael & Laumert, Björn, 2022. "Experimental evaluation of an innovative radial-flow high-temperature packed bed thermal energy storage," Applied Energy, Elsevier, vol. 311(C).
    5. Roos, Philipp & Haselbacher, Andreas, 2021. "Thermocline control through multi-tank thermal-energy storage systems," Applied Energy, Elsevier, vol. 281(C).
    6. Singh, Shobhana & Sørensen, Kim & Condra, Thomas & Batz, Søren Søndergaard & Kristensen, Kristian, 2019. "Investigation on transient performance of a large-scale packed-bed thermal energy storage," Applied Energy, Elsevier, vol. 239(C), pages 1114-1129.
    7. Calderón-Vásquez, Ignacio & Cortés, Eduardo & García, Jesús & Segovia, Valentina & Caroca, Alejandro & Sarmiento, Cristóbal & Barraza, Rodrigo & Cardemil, José M., 2021. "Review on modeling approaches for packed-bed thermal storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).

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