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A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties

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  • Zhuang, Chaoqun
  • Wang, Shengwei
  • Shan, Kui

Abstract

Proper and reliable control of central chilling systems with multiple chillers is crucial to save energy and enhance energy efficiency. The conventional total-cooling-load-based chiller sequencing control strategies determine switching (on/off) thresholds according to building instantaneous cooling load and chiller maximum cooling capacity. However, due to the existence of measurement uncertainties and ever-changing operating conditions, optimal switching points often deviate significantly from predefined thresholds. To deal with these challenges and uncertainties, a risk-based robust optimal chiller sequencing control strategy is proposed to improve the robustness and energy efficiency of chillers in operation. As the core of the control strategy, an online stochastic decision-making scheme, which is developed to optimize chiller staging based on quantified risks. The risk of failure to achieve expected operation performance by switching on/off a chiller is evaluated through analyzing the probabilistic fused cooling load and the probabilistic chiller maximum cooling capacity, based on Bayesian calibration of cooling load and capacity models. The best switching points can therefore be identified in a stochastic approach. The results of case studies show that the proposed strategy can improve the reliability and robustness of chiller sequence operation. Compared with the conventional strategy, the switching frequency was decreased by more than 54%, and the energy use of central cooling systems can be reduced by 2.8% without sacrificing thermal comfort.

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  • 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).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314318
    DOI: 10.1016/j.apenergy.2020.115983
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    References listed on IDEAS

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    Cited by:

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    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Huang, Kehua & Zeng, Wenhao & Zhang, Wenlong & Ye, Haiseng, 2022. "Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact," Energy, Elsevier, vol. 255(C).
    3. 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).
    4. 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).
    5. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
    6. Kumar, Devesh & Pindoriya, Naran M., 2024. "A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant," Energy, Elsevier, vol. 290(C).
    7. Campos, Gustavo & Liu, Yu & Schmidt, Devon & Yonkoski, Joseph & Colvin, Daniel & Trombly, David M. & El-Farra, Nael H. & Palazoglu, Ahmet, 2021. "Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant," Applied Energy, Elsevier, vol. 300(C).

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