Optimization of PCM layer height of cascaded two-layered packed-bed thermal energy storage tank with capsules of varying diameters based on genetic algorithm

The packed-bed thermal energy storage (PBTES) technology exhibits significant potential for utilization in various energy sectors, including concentrating solar power, city heating systems and power peaking. This paper uses a genetic algorithm (GA) to optimize the phase change material (PCM) layer height arrangement of cascaded two-layered PBTES with capsules of varying diameters to enhance thermal performance during charging. The optimization process considers the oscillatory distribution of radial porosity, and the objective function utilized for assessing the thermal performance of the system is thermal energy storage (TES) rate density. The TES rate density for over 2000 different layer height arrangements was calculated. The findings indicate that the PCM layer height significantly influences the thermal performance of the tank in each layer. The charging performance is optimal when the layer height ratio of high and low melting point PCM is 12.05 % and 87.95 %, respectively, which is the optimal model. In comparison to the original model with equal bed height, the optimal model demonstrates a reduction in charging time of 28 %, increases the TES rate density by 17.68 %, and the greatest improvement in charging efficiency reached 31.28 %. And reduces the unit TES cost by 13 %. This study shows that GA is an effective tool for optimizing the structural arrangement of PBTES.