Collaborative optimization of ground source heat pump-radiant ceiling air conditioning system based on response surface method and NSGA-II. (March 2020)

• Record Type: Journal Article
• Authors: Xie, Yiwei; Hu, Pingfang; Zhu, Na; Lei, Fei; Xing, Lu; Xu, Linghong
• Is Part Of: Renewable energy. Volume 147(2020)Part 1
• Page Start: 249
• Page End: 264
• Publication Date: 2020-03
• Subjects: Ground source heat pump -- Radiant ceiling -- RSM -- NSGA-II -- Collaborative optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.08.109
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A parametric collaborative optimization method for ground source heat pump-radiant ceiling (GSHP-RC) system is proposed to find the optimum setpoint combinations by maximizing system performance and reducing operating costs while ensuring indoor thermal comfort. The method integrates response surface method (RSM) and fast non-dominated sorting genetic algorithm (NSGA-II) to search the nonlinearity relationship between the controllable factors and the response factors and execute multi-objective optimization (MOO) progressively. A GSHP-RC system in an office building is investigated based on TRNSYS. Three controllable factors, the water supply temperature of radiant ceiling, the indoor set temperature and the water supply temperature of the heat pump, were used to analyze the influence mechanism on the system. The system performance, thermal comfort and economy are evaluated by seasonal performance factor (SPF), predicted mean vote (PMV) and the operating cost (OC). Two optimal cases were determined with the SPF of 3.741 and 3.734, the OC of CNY 23525 and 24613, and the PMV of 0.225 and -0.223 respectively. The optimization can realize 17.3% and 13.0% of the operating cost saving for the optimal cases respectively with the significant improvement in SPF under the premise of indoor thermal comfort compared with the reference case with conventional parameters. Highlights: A parametric collaborative optimization method based on RSM and NSGA-II is proposed. The method can obviously improve the performance of GSHP-RC system. The approach integrates system performance, indoor thermal comfort and system economy. A case study of GSHP-RC system is optimized and comprehensively evaluated.