Multi-objective optimization of cascaded packed bed thermal energy storage unit based on response surface and factor analysis methods

Chengxu Chen, Xiaoze Du, Lizhong Yang and Alessandro Romagnoli

Applied Energy, 2025, vol. 386, issue C, No S0306261925003289

Abstract: The cascaded multi-layer packed bed thermal energy storage (TES) unit with varying fill ratios is proposed to enhance its thermal performance. A concentric dispersion model for simulating thermal fluid heat transfer is developed and experimentally validated. Based on this, four designs are explored to examine the effect of the filling ratio of phase change materials with different melting points on the thermal performance of the packed bed TES system, including that of balanced-layer, top-heavy-layer, middle-heavy-layer and bottom-heavy-layer. The multi-factor and multi-objective optimization is conducted by response surface and factor analysis methods. Differs from the previous studies that only designed several configurations with different phase change material filling ratios, the present sudy focuses on the interaction between the filling ratio and the thermal performances, as well as the optimal filling ratio of each layer to achieve the best thermal performance. The results show that the bottom-heavy-layer has the shortest charging time of 950 min and the highest energy utilization of 61.72 %, while the top-heavy-layer has the highest charging exergy efficiency of 84.7 % and the largest TES capacity of 96.88 MWh. As for the multi-objective optimization, the optimized value of comprehensive evaluation indicator F is 1.7112, and the corresponding charging time, energy utilization, TES capacity, and charging exergy efficiency is 778 min, 0.62, 99.76 MWh, and 0.83, respectively. This research establishes a foundation for the advanced optimization of phase change material filling ratios and comprehensive system-level evaluation.

Keywords: Thermal energy storage; Cascaded packed bed; Response surface method; Phase change material; Multi-objective optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125598

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