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

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  • Xie, Yiwei
  • Hu, Pingfang
  • Zhu, Na
  • Lei, Fei
  • Xing, Lu
  • Xu, Linghong

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.

Suggested Citation

  • Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong, 2020. "Collaborative optimization of ground source heat pump-radiant ceiling air conditioning system based on response surface method and NSGA-II," Renewable Energy, Elsevier, vol. 147(P1), pages 249-264.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:249-264
    DOI: 10.1016/j.renene.2019.08.109
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    Cited by:

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    2. Lijian Shi & Jun Zhu & Fangping Tang & Chuan Wang, 2020. "Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model," Energies, MDPI, vol. 13(4), pages 1-19, February.
    3. Feng, Zongbao & Wu, Xianguo & Chen, Hongyu & Qin, Yawei & Zhang, Limao & Skibniewski, Miroslaw J., 2022. "An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China," Energy, Elsevier, vol. 248(C).
    4. Nikitin, Andrey & Farahnak, Mehdi & Deymi-Dashtebayaz, Mahdi & Muraveinikov, Sergei & Nikitina, Veronika & Nazeri, Reza, 2022. "Effect of ice thickness and snow cover depth on performance optimization of ground source heat pump based on the energy, exergy, economic and environmental analysis," Renewable Energy, Elsevier, vol. 185(C), pages 1301-1317.
    5. Li, Yantong & Ding, Zhixiong & Du, Yaxing, 2020. "Techno-economic optimization of open-air swimming pool heating system with PCM storage tank for winter applications," Renewable Energy, Elsevier, vol. 150(C), pages 878-890.
    6. Xie, Yiwei & Hu, Pingfang & Peng, Donggen & Zhu, Na & Lei, Fei, 2023. "Development of a group control strategy based on multi-step load forecasting and its application in hybrid ground source heat pump," Energy, Elsevier, vol. 273(C).
    7. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
    8. Li, Chunying & Tang, Haida, 2020. "Evaluation on year-round performance of double-circulation water-flow window," Renewable Energy, Elsevier, vol. 150(C), pages 176-190.

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