Abstract
A single sensible thermal storage system has the disadvantage of poor system efficiency, and a sensible-latent graded thermal storage system can effectively solve this problem. Moreover, the graded thermal storage system has the virtue of being adjustable, which can be adapted to many power generation systems. Therefore, this paper first analyzes the influence factors of the graded thermal storage system’s exergy and thermal efficiency. Subsequently, each factor’s significance was analyzed using the response surface method, and the prediction model for system exergy efficiency and cost was established using the support vector machine method. Finally, the second-generation nondominated sorting genetic algorithm (NSGA-II) was used to globally optimize the graded thermal storage system’s exergy efficiency and cost by Matlab software. As a result, the exergy efficiency was increased by 11.01%, and the cost was reduced by RMB 5.85 million. In general, the effect of multi-objective optimization is obvious.