A multi-objective optimal design method for thermal energy storage systems with PCM: A case study for outdoor swimming pool heating application. (June 2020)

• Record Type: Journal Article
• Authors: Li, Yantong; Ding, Zhixiong; Shakerin, Mohammad; Zhang, Nan
• Is Part Of: Journal of energy storage. Volume 29(2020)
• Publication Date: 2020-06
• Subjects: Multi-objective optimization -- Thermal energy storage -- Phase change material -- Outdoor swimming pool -- Heating system; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.101371
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Traditional design methods for thermal energy storage systems (TES) with phase change material (PCM) are mostly based on worst-case scenario, which causes too large size of main components. Current optimal design methods for these systems mainly focus on single optimization objective, which only satisfies the unilateral requirement. A multi-objective optimal design method for these systems is urgently needed, and therefore this paper remedies this knowledge gap. The response surface methodology is adopted to develop the surrogated models of the optimization objectives to improve the computational efficiency. Then, the non-dominated sorting genetic algorithm II is used to perform the double-objective and triple-objective optimization for acquiring the Pareto optimal solutions. Finally, the final decision-making methods that includes LINMAP and TOPSIS are adopted to identify the final optimal solutions. A case study of optimizing the design for an outdoor swimming pool (OSP) heating system with PCM storage tank, is conducted to illustrate the proposed approach. Eight final optimal solutions were identified, and the sp of the system in these solutions was 1.05, 1.24, 1.04, 1.22, 1.06, 1.06, 1.07, and 0.88 years, respectively. Results indicate that the proposed approach is effective to conduct the multi-objective optimization for the OSP heating systems and guide the design optimization for the TES systems with PCM.