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Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability

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  • Cheng, Qi
  • Wang, Shengwei
  • Yan, Chengchu

Abstract

Conventional design of cooling water systems mainly focused on the individual components of cooling water system, not the system as a whole. In this paper, a robust optimal design based on sequential Monte Carlo simulation is proposed to optimize the design of cooling water system. Monte Carlo simulation is used to obtain the cooling load distribution of required accuracy, power consumption and unmet cooling load. Convergence assessment is conducted to terminate the sampling process of Monte Carlo simulation. Under different penalty ratios and repair rates, this proposed design minimizes the annual total cost of cooling water system. A case study of a building in Hong Kong is conducted to demonstrate the design process and test the robust optimal design method. The results show that the minimum total cost could be achieved under various possible cooling load conditions considering the uncertainties of design inputs and reliability of system components.

Suggested Citation

  • Cheng, Qi & Wang, Shengwei & Yan, Chengchu, 2017. "Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability," Energy, Elsevier, vol. 118(C), pages 489-501.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:489-501
    DOI: 10.1016/j.energy.2016.10.051
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    References listed on IDEAS

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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).
    2. Ma, Jiaze & Wang, Yufei & Feng, Xiao, 2017. "Energy recovery in cooling water system by hydro turbines," Energy, Elsevier, vol. 139(C), pages 329-340.
    3. Yamile Díaz Torres & Paride Gullo & Hernán Hernández Herrera & Migdalia Torres del Toro & Mario A. Álvarez Guerra & Jorge Iván Silva Ortega & Arne Speerforck, 2022. "Statistical Analysis of Design Variables in a Chiller Plant and Their Influence on Energy Consumption and Life Cycle Cost," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    4. Postnikov, Ivan & Stennikov, Valery & Mednikova, Ekaterina & Penkovskii, Andrey, 2018. "Methodology for optimization of component reliability of heat supply systems," Applied Energy, Elsevier, vol. 227(C), pages 365-374.

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