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Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability

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  • Gang, Wenjie
  • Wang, Shengwei
  • Xiao, Fu
  • Gao, Dian-ce

Abstract

Appropriate design provides the cooling system to achieve good performance with low energy consumption and cost. Conventional design method in heating, ventilation and air-conditioning (HVAC) field usually selects the cooling system based on certain cooling load and experiences. The performance of the selected cooling system may deviate from the expectations due to cooling load uncertainty. This paper proposes a novel design method to obtain the robust optimal cooling systems for buildings by quantifying the uncertainty in cooling load calculation and equipment reliability in operation comprehensively. By quantifying the cooling load uncertainty with Monte Carlo method and chiller reliability using Markov method, the robust optimal cooling system is obtained with minimum annual total cost. By applying the new method in the design of the cooling system for a building, its function and performance as well as potential benefits are demonstrated and evaluated. Results show that the proposed method can obtain the optimal cooling systems with low cost and high robustness and provides a promising means for designers to make their best design decisions based on quantitative assessment according to their priority.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:159:y:2015:i:c:p:265-275
    DOI: 10.1016/j.apenergy.2015.08.070
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    Cited by:

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    10. Li, Hangxin & Wang, Shengwei, 2020. "Coordinated robust optimal design of building envelope and energy systems for zero/low energy buildings considering uncertainties," Applied Energy, Elsevier, vol. 265(C).
    11. 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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    13. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang & Lan, Bo, 2019. "A robust optimization model for designing the building cooling source under cooling load uncertainty," Applied Energy, Elsevier, vol. 241(C), pages 390-403.
    14. 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.
    15. Li, Hangxin & Wang, Shengwei & Tang, Rui, 2019. "Robust optimal design of zero/low energy buildings considering uncertainties and the impacts of objective functions," Applied Energy, Elsevier, vol. 254(C).
    16. 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.
    17. Georgios P. Trachanas & Aikaterini Forouli & Nikolaos Gkonis & Haris Doukas, 2020. "Hedging uncertainty in energy efficiency strategies: a minimax regret analysis," Operational Research, Springer, vol. 20(4), pages 2229-2244, December.
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    19. Frangopoulos, Christos A., 2018. "Recent developments and trends in optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 1011-1020.
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