Multi-factor analysis and optimization design of a cascaded packed-bed thermal storage system coupled with adiabatic compressed air energy storage

In an adiabatic compressed air energy storage (A-CAES), one of the key components is the heat storage system, in which the packed bed filled with encapsulated phase-change capsules has been widely investigated because of its excellent thermal performance. In this paper, a packed-bed thermal energy storage model with three layers of phase change materials (PCM) is proposed in the context of an A-CAES. Four factors primarily affecting the thermal performance of the packed bed thermal storage system, namely the mass flow rate of heat transfer fluid (HTF), the inlet temperature of HTF, the cascade situation of PCM, and the arrangement of particle sizes, are critically analyzed by conducing the orthogonal experiments. On this basis, the machine learning method is applied to predict and optimize the thermal performance of the packed bed aiming to find the optimal results. The comparison of results between the optimized and original models expressively shows a reduction of 8.46% in PCM mass, an increase of 92.18%/116.82% in the overall heat storage/release quantity, and an increase of 19.23% in the overall efficiency. This work outlooks a guideline for the potential application of packed bed in A-CAES.