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Developing a simplified model for evaluating chiller-system configurations

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  • Lee, W.L.
  • Lee, S.H.

Abstract

Optimizing system configuration is always an interest of building designers. By statistical analysis of the design data of 50 commercial buildings in Hong Kong and the performance data of 186 chiller models, it is ascertained that the chiller capacity and the fraction of full-load capacity have little influence on the system's energy performance. Following on this, this paper presents an evaluation of the energy performance of a multiple-chiller system consisting of 2-10 equally sized chillers. Such an analysis was based on performance data from three major manufacturers. It is found that the energy efficiency of multiple-chiller system improves with a higher number of chillers, and the maximum saving is estimated to be 9.5%. Based on the results of the study, a simplified model relating energy use with number of chillers has been established. The model can help designers more quickly determine how the energy efficiency can be weighted against other factors, such as the additional plant room space and the financial implications.

Suggested Citation

  • Lee, W.L. & Lee, S.H., 2007. "Developing a simplified model for evaluating chiller-system configurations," Applied Energy, Elsevier, vol. 84(3), pages 290-306, March.
    • Handle: RePEc:eee:appene:v:84:y:2007:i:3:p:290-306
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    References listed on IDEAS

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    1. Chan, K. T. & Yu, F. W., 2002. "Applying condensing-temperature control in air-cooled reciprocating water chillers for energy efficiency," Applied Energy, Elsevier, vol. 72(3-4), pages 565-581, July.
    2. Lee, W. L. & Yik, F. W. H. & Jones, P. & Burnett, J., 2001. "Energy saving by realistic design data for commercial buildings in Hong Kong," Applied Energy, Elsevier, vol. 70(1), pages 59-75, September.
    3. Chan, K.T. & Yu, F.W., 2006. "Thermodynamic-behaviour model for air-cooled screw chillers with a variable set-point condensing temperature," Applied Energy, Elsevier, vol. 83(3), pages 265-279, March.
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    Cited by:

    1. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    2. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2023. "A robust capacity configuration selection method of multiple-chiller system concerned with the uncertainty of annual hourly load profile," Energy, Elsevier, vol. 282(C).
    3. Cheng, Qi & Wang, Shengwei & Yan, Chengchu & Xiao, Fu, 2017. "Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings," Applied Energy, Elsevier, vol. 185(P2), pages 1613-1624.
    4. Huang, Sen & Zuo, Wangda & Sohn, Michael D., 2016. "Amelioration of the cooling load based chiller sequencing control," Applied Energy, Elsevier, vol. 168(C), pages 204-215.
    5. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.

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