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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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    1. Gang, Wenjie & Augenbroe, Godfried & Wang, Shengwei & Fan, Cheng & Xiao, Fu, 2016. "An uncertainty-based design optimization method for district cooling systems," Energy, Elsevier, vol. 102(C), pages 516-527.
    2. Yıldız, Yusuf & Arsan, Zeynep Durmuş, 2011. "Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates," Energy, Elsevier, vol. 36(7), pages 4287-4296.
    3. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability," Applied Energy, Elsevier, vol. 159(C), pages 265-275.
    4. Ashouri, Araz & Petrini, Flavio & Bornatico, Raffaele & Benz, Michael J., 2014. "Sensitivity analysis for robust design of building energy systems," Energy, Elsevier, vol. 76(C), pages 264-275.
    5. Picón-Núnez, Martín & Polley, Graham T. & Canizalez-Dávalos, Lázaro & Medina-Flores, José Martín, 2011. "Short cut performance method for the design of flexible cooling systems," Energy, Elsevier, vol. 36(8), pages 4646-4653.
    6. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    7. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
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    Cited by:

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    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. Li, Xuehan & Wang, Wei & Ye, Lingling & Ren, Guorui & Fang, Fang & Liu, Jizhen & Chen, Zhe & Zhou, Qiang, 2024. "Improving frequency regulation ability for a wind-thermal power system by multi-objective optimized sliding mode control design," Energy, Elsevier, vol. 300(C).
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    5. 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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