IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v280y2020ics0306261920314318.html
   My bibliography  Save this article

A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties

Author

Listed:
  • Zhuang, Chaoqun
  • Wang, Shengwei
  • Shan, Kui

Abstract

Proper and reliable control of central chilling systems with multiple chillers is crucial to save energy and enhance energy efficiency. The conventional total-cooling-load-based chiller sequencing control strategies determine switching (on/off) thresholds according to building instantaneous cooling load and chiller maximum cooling capacity. However, due to the existence of measurement uncertainties and ever-changing operating conditions, optimal switching points often deviate significantly from predefined thresholds. To deal with these challenges and uncertainties, a risk-based robust optimal chiller sequencing control strategy is proposed to improve the robustness and energy efficiency of chillers in operation. As the core of the control strategy, an online stochastic decision-making scheme, which is developed to optimize chiller staging based on quantified risks. The risk of failure to achieve expected operation performance by switching on/off a chiller is evaluated through analyzing the probabilistic fused cooling load and the probabilistic chiller maximum cooling capacity, based on Bayesian calibration of cooling load and capacity models. The best switching points can therefore be identified in a stochastic approach. The results of case studies show that the proposed strategy can improve the reliability and robustness of chiller sequence operation. Compared with the conventional strategy, the switching frequency was decreased by more than 54%, and the energy use of central cooling systems can be reduced by 2.8% without sacrificing thermal comfort.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314318
    DOI: 10.1016/j.apenergy.2020.115983
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920314318
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115983?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huang, Sen & Zuo, Wangda & Sohn, Michael D., 2016. "Amelioration of the cooling load based chiller sequencing control," Applied Energy, Elsevier, vol. 168(C), pages 204-215.
    2. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Comparison of alternative decision-making criteria in a two-stage stochastic program for the design of distributed energy systems under uncertainty," Energy, Elsevier, vol. 156(C), pages 709-724.
    3. 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.
    4. Min, Yunran & Chen, Yi & Yang, Hongxing, 2019. "A statistical modeling approach on the performance prediction of indirect evaporative cooling energy recovery systems," Applied Energy, Elsevier, vol. 255(C).
    5. 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.
    6. Wang, Shengwei & Cui, Jingtan, 2005. "Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method," Applied Energy, Elsevier, vol. 82(3), pages 197-213, November.
    7. Yu, F.W. & Chan, K.T., 2005. "Experimental determination of the energy efficiency of an air-cooled chiller under part load conditions," Energy, Elsevier, vol. 30(10), pages 1747-1758.
    8. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    9. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Probabilistic optimal design of cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Yu, F.W. & Chan, K.T., 2012. "Improved energy management of chiller systems by multivariate and data envelopment analyses," Applied Energy, Elsevier, vol. 92(C), pages 168-174.
    11. Zhuang, Chaoqun & Wang, Shengwei, 2020. "Risk-based online robust optimal control of air-conditioning systems for buildings requiring strict humidity control considering measurement uncertainties," Applied Energy, Elsevier, vol. 261(C).
    12. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional value-at-risk framework," Energy, Elsevier, vol. 113(C), pages 762-775.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lukas Theisinger & Michael Frank & Matthias Weigold, 2024. "Systematic Development of Application-Oriented Operating Strategies for the Example of an Industrial Heating Supply System," Energies, MDPI, vol. 17(9), pages 1-13, April.
    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Huang, Kehua & Zeng, Wenhao & Zhang, Wenlong & Ye, Haiseng, 2022. "Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact," Energy, Elsevier, vol. 255(C).
    3. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    4. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
    5. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Cui, Zhitao & You, Zhiqiang & Ma, Xiaowen, 2023. "Robust enhancement of chiller sequencing control for tolerating sensor measurement uncertainties through controlling small-scale thermal energy storage," Energy, Elsevier, vol. 280(C).
    6. Kumar, Devesh & Pindoriya, Naran M., 2024. "A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant," Energy, Elsevier, vol. 290(C).
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    2. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
    3. 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.
    4. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    5. Abou-Ziyan, Hosny Z. & Alajmi, Ali F., 2014. "Effect of load-sharing operation strategy on the aggregate performance of existed multiple-chiller systems," Applied Energy, Elsevier, vol. 135(C), pages 329-338.
    6. Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Jinhua Hu & Meng Wang, 2017. "Modeling and Optimization of a CoolingTower-Assisted Heat Pump System," Energies, MDPI, vol. 10(5), pages 1-18, May.
    7. Chen, Qun & Wang, Yi-Fei & Xu, Yun-Chao, 2015. "A thermal resistance-based method for the optimal design of central variable water/air volume chiller systems," Applied Energy, Elsevier, vol. 139(C), pages 119-130.
    8. Qinli Deng & Liangxin Xu & Tingfang Zhao & Xuexin Hong & Xiaofang Shan & Zhigang Ren, 2022. "Cooperative Optimization of A Refrigeration System with A Water-Cooled Chiller and Air-Cooled Heat Pump by Coupling BPNN and PSO," Energies, MDPI, vol. 15(19), pages 1-19, September.
    9. Huang, Sen & Zuo, Wangda & Sohn, Michael D., 2016. "Amelioration of the cooling load based chiller sequencing control," Applied Energy, Elsevier, vol. 168(C), pages 204-215.
    10. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    11. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    12. Min, Yunran & Chen, Yi & Shi, Wenchao & Yang, Hongxing, 2021. "Applicability of indirect evaporative cooler for energy recovery in hot and humid areas: Comparison with heat recovery wheel," Applied Energy, Elsevier, vol. 287(C).
    13. Ma, Zhenjun & Wang, Shengwei, 2009. "Building energy research in Hong Kong: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1870-1883, October.
    14. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    15. Mu, Baojie & Li, Yaoyu & House, John M. & Salsbury, Timothy I., 2017. "Real-time optimization of a chilled water plant with parallel chillers based on extremum seeking control," Applied Energy, Elsevier, vol. 208(C), pages 766-781.
    16. Guo, Fangzhou & Li, Ao & Yue, Bao & Xiao, Ziwei & Xiao, Fu & Yan, Rui & Li, Anbang & Lv, Yan & Su, Bing, 2024. "Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network," Applied Energy, Elsevier, vol. 354(PA).
    17. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    18. Ho, W.T. & Yu, F.W., 2021. "Improved model and optimization for the energy performance of chiller system with diverse component staging," Energy, Elsevier, vol. 217(C).
    19. Zhuang, Chaoqun & Wang, Shengwei, 2020. "Risk-based online robust optimal control of air-conditioning systems for buildings requiring strict humidity control considering measurement uncertainties," Applied Energy, Elsevier, vol. 261(C).
    20. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Cui, Zhitao & You, Zhiqiang & Ma, Xiaowen, 2023. "Robust enhancement of chiller sequencing control for tolerating sensor measurement uncertainties through controlling small-scale thermal energy storage," Energy, Elsevier, vol. 280(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314318. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.