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Robust enhancement of chiller sequencing control for tolerating sensor measurement uncertainties through controlling small-scale thermal energy storage

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  • Zou, Wenke
  • Sun, Yongjun
  • Gao, Dian-ce
  • Cui, Zhitao
  • You, Zhiqiang
  • Ma, Xiaowen

Abstract

Reliable chiller sequencing control strategy is crucial to enhancing control robustness and energy efficiency of the multiple-chiller systems. However, the commonly used conventional chiller sequencing control strategy, which determines the chiller stage by comparing the measurement instantaneous cooling load with the chiller's cooling capacity, often deviate significantly from its desired results due to the presence of measurement uncertainties. Different from the existing solutions focusing on improving the accuracy of measured cooling load using complicated models fed with large amounts of measurement information, this study presents a novel solution from a new perspective, tolerating rather than dealing with measurement uncertainties directly through introducing and controlling a small-scale thermal energy storage integrated with the chiller plant. Two schemes, namely robustness-enhancement scheme and chiller operating efficiency enhancement scheme, were developed and collaboratively utilized to achieve two objectives simultaneously: enhancing the robustness of the sequencing control and improving the operating efficiency of chillers even under measurement uncertainties. The results of case studies demonstrate that, compared with the conventional strategy, the proposed strategy can reduce the switching frequency by up to 77.42% as well as save the total energy use of chilled water systems by up to 4.44% without sacrificing indoor thermal comfort.

Suggested Citation

  • Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Cui, Zhitao & You, Zhiqiang & Ma, Xiaowen, 2023. "Robust enhancement of chiller sequencing control for tolerating sensor measurement uncertainties through controlling small-scale thermal energy storage," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223015463
    DOI: 10.1016/j.energy.2023.128152
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    References listed on IDEAS

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    1. 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.
    2. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).
    3. 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).
    4. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    5. Gao, Dian-ce & Wang, Shengwei & Shan, Kui, 2016. "In-situ implementation and evaluation of an online robust pump speed control strategy for avoiding low delta-T syndrome in complex chilled water systems of high-rise buildings," Applied Energy, Elsevier, vol. 171(C), pages 541-554.
    6. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    7. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional value-at-risk framework," Energy, Elsevier, vol. 113(C), pages 762-775.
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    Cited by:

    1. 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).

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