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Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings

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

Abstract

Conventional design of chiller plant is typically based on the peak cooling loads of buildings, while the cooling load reaches its peak level for only a small proportion of time in a year. This results in that even a perfectly designed chiller plant could be very significantly oversized in actual operation and it thus causes significant energy wastes. In this paper, an uncertainty-based optimal design based on probabilistic approach is proposed to optimize the chiller plant design. It ensures that the chiller plant operate at a high efficiency and the minimum annual total cost (including annual operational cost and annual capital cost) could be achieved under various possible cooling load conditions, considering the uncertain variables in cooling load calculation (i.e., weather conditions). On the premise of determining the minimum sufficient number of Monte Carlo simulation, this method maximizes the operating COP (coefficient of performance) and minimizing the annual total cost. A case study on the chiller plant of a building in Hong Kong is conducted to demonstrate the design process and validate the uncertainty-based optimal design method.

Suggested Citation

  • Cheng, Qi & Wang, Shengwei & Yan, Chengchu & Xiao, Fu, 2017. "Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings," Applied Energy, Elsevier, vol. 185(P2), pages 1613-1624.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1613-1624
    DOI: 10.1016/j.apenergy.2015.10.097
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    6. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
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    8. Deng, Jiewen & Wei, Qingpeng & Qian, Yangyang & Zhang, Hui, 2018. "Does magnetic bearing variable-speed centrifugal chiller perform truly energy efficient in buildings: Field-test and simulation results," Applied Energy, Elsevier, vol. 229(C), pages 998-1009.
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    10. 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.
    11. Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    12. 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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