A two-dimensional model of the charging process on a heat storage unit in a shell-and-tube type latent heat subsystem of a solar power plant with direct steam generation was constructed in this study. The effects of the outer diameter to inner diameter ratio, aspect ratio, phase change material (PCM) thermal conductivity, and heat transfer fluid (HTF) mass flow rate were investigated. Results show that increasing the PCM thermal conductivity, HTF mass flow rate, and aspect ratio of the heat storage unit can shorten heat storage time, but the ratio of the outer diameter to the inner diameter of the heat storage unit has an optimal value of 6 in this problem. Using response surface methodology analysis, the influence of the aspect ratio, outer-to-innerdiameter ratio, PCM thermal conductivity, and HTF mass flow rate on the storage time of the phase change heat storage unit is in descending order.After a genetic algorithm optimization, the storage rate of the heat storage unit increased by 35%. The results of this study can guide the heat storage unit to achieve a better practical application performance.

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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.