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Amelioration of the cooling load based chiller sequencing control

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  • Huang, Sen
  • Zuo, Wangda
  • Sohn, Michael D.

Abstract

Cooling Load based Control (CLC) for the chiller sequencing is a commonly used control strategy for multiple-chiller plants. To improve the energy efficiency of these chiller plants, researchers proposed various CLC optimization approaches, which can be divided into two groups: studies to optimize the load distribution and studies to identify the optimal number of operating chillers. However, both groups have their own deficiencies and do not consider the impact of each other. This paper aims to improve the CLC by proposing three new approaches. The first optimizes the load distribution by adjusting the critical points for the chiller staging, which is easier to be implemented than existing approaches. In addition, by considering the impact of the load distribution on the cooling tower energy consumption and the pump energy consumption, this approach can achieve a better energy saving. The second optimizes the number of operating chillers by modulating the critical points and the condenser water set point in order to achieve the minimal energy consumption of the entire chiller plant that may not be guaranteed by existing approaches. The third combines the first two approaches to provide a holistic solution. The proposed three approaches were evaluated via a case study. The results show that the total energy consumption saving for the studied chiller plant is 0.5%, 5.3% and 5.6% by the three approaches, respectively. An energy saving of 4.9–11.8% can be achieved for the chillers at the cost of more energy consumption by the cooling towers (increases of 5.8–43.8%). The pumps’ energy saving varies from −8.6% to 2.0%, depending on the approach.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:168:y:2016:i:c:p:204-215
    DOI: 10.1016/j.apenergy.2016.01.035
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    References listed on IDEAS

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    11. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    12. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    13. Catrini, P. & Panno, D. & Cardona, F. & Piacentino, A., 2020. "Characterization of cooling loads in the wine industry and novel seasonal indicator for reliable assessment of energy saving through retrofit of chillers," Applied Energy, Elsevier, vol. 266(C).
    14. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    15. Mu, Baojie & Li, Yaoyu & House, John M. & Salsbury, Timothy I., 2017. "Real-time optimization of a chilled water plant with parallel chillers based on extremum seeking control," Applied Energy, Elsevier, vol. 208(C), pages 766-781.
    16. Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).
    17. Hinkelman, Kathryn & Wang, Jing & Zuo, Wangda & Gautier, Antoine & Wetter, Michael & Fan, Chengliang & Long, Nicholas, 2022. "Modelica-based modeling and simulation of district cooling systems: A case study," Applied Energy, Elsevier, vol. 311(C).
    18. Federica Acerbi & Mirco Rampazzo & Giuseppe De Nicolao, 2020. "An Exact Algorithm for the Optimal Chiller Loading Problem and Its Application to the Optimal Chiller Sequencing Problem," Energies, MDPI, vol. 13(23), pages 1-29, December.
    19. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio, 2018. "Integration of distributed generation technologies on sustainable buildings," Applied Energy, Elsevier, vol. 224(C), pages 582-601.

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