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A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant

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  • Kumar, Devesh
  • Pindoriya, Naran M.

Abstract

This article focuses on addressing the chiller sequencing problem of chiller plants by establishing a comprehensive energy management framework. The contribution of this work is threefold. Firstly, a distributive modeling architecture is presented that establishes five concurrent models as input to the framework. A synergy among these models is exploited to formulate a chance-constrained stochastic chiller sequencing problem. Secondly, a quantified life expectancy model of the chiller plant is introduced and a case is made for why it is influential in delivering industrially applicable solutions. The model attempts to strike a balance between economic optimality and improved reliability. Thirdly, a chiller data pre-processing protocol incubating two heuristic algorithms is proposed to address measurement uncertainties of the chiller plant state variables. Furthermore, this work develops a robust ensemble model to accurately forecast the cooling load and embeds a chain of reformulations to improve the global solution's optimality. The developed framework is realized with the plant at the Indian Institute of Technology Gandhinagar. The results confirm that the proposed framework leads to a significant amount of power savings. In comparison to conventional scheduling, the chiller plant power consumption can be reduced by up to 6.2 %, thereby illustrating its efficacy.

Suggested Citation

  • Kumar, Devesh & Pindoriya, Naran M., 2024. "A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant," Energy, Elsevier, vol. 290(C).
  • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223033996
    DOI: 10.1016/j.energy.2023.130005
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    References listed on IDEAS

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    1. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    2. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    3. Cheung, Howard & Wang, Shengwei, 2019. "Reliability and availability assessment and enhancement of water-cooled multi-chiller cooling systems for data centers," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    5. Deng, Na & Cai, Rongchang & Gao, Yuan & Zhou, Zhihua & He, Guansong & Liu, Dongyi & Zhang, Awen, 2017. "A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin," Energy, Elsevier, vol. 141(C), pages 1750-1763.
    6. 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).
    7. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, April.
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