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Control strategy for dynamic operation of multiple chillers under random load constraints

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  • Liu, Xuefeng
  • Huang, Bin
  • Zheng, Yulan

Abstract

Under the influence of building load stochasticity and thermal inertia of central air conditioning system, the lag of control strategy makes it difficult for the cooling source system to meet the cooling demand of users in real time, which affects the operational energy efficiency ratio of the cooling source system. In this regard, this paper proposes a chiller dynamic optimization method based on Markov chain model predictive control algorithm, which studies the optimization of parallel operation characteristics of multiple chillers on the basis of correcting the occurrence of small probability events and avoiding frequent chiller starts and stops, reveals the prediction law of hourly load distribution of the future air conditioning system, explores the balance point between the prediction time of building load and the calculation time of chiller control strategy, provides a theoretical basis and reference for determining the time limit of the optimal calculation and the output of the optimal control strategy. The study provides a theoretical basis and reference for determining the time limit of the optimal calculation and the output of the optimal control strategy. It is found that the optimal control strategy not only has good optimization effect, but also the confidence is significantly increased by 56.7%.

Suggested Citation

  • Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003262
    DOI: 10.1016/j.energy.2023.126932
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    1. Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
    2. Ma, Zhenjun & Wang, Shengwei, 2011. "Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm," Applied Energy, Elsevier, vol. 88(1), pages 198-211, January.
    3. Asad, Hussain Syed & Yuen, Richard Kwok Kit & Huang, Gongsheng, 2017. "Multiplexed real-time optimization of HVAC systems with enhanced control stability," Applied Energy, Elsevier, vol. 187(C), pages 640-651.
    4. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    5. 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.
    6. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
    7. 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.
    8. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
    9. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    10. 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.
    11. Powell, Kody M. & Cole, Wesley J. & Ekarika, Udememfon F. & Edgar, Thomas F., 2013. "Optimal chiller loading in a district cooling system with thermal energy storage," Energy, Elsevier, vol. 50(C), pages 445-453.
    12. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
    13. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    14. Lv, Liugen & Huang, Chen & Li, Li & Chen, Jianchang, 2015. "Experimental study on calculation method of the radiant time factors," Renewable Energy, Elsevier, vol. 73(C), pages 28-35.
    15. Lee, Tzong-Shing & Lu, Wan-Chen, 2010. "An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers," Applied Energy, Elsevier, vol. 87(11), pages 3486-3493, November.
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