IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v238y2019icp1444-1451.html
   My bibliography  Save this article

Online chiller loading strategy based on the near-optimal performance map for energy conservation

Author

Listed:
  • Wang, Yijun
  • Jin, Xinqiao
  • Shi, Wantao
  • Wang, Jiangqing

Abstract

This paper develops an online control strategy for the multiple chillers with constant flow of cooling water and chilled water (primary side). The strategy is based on the performance map which indicates the chillers’ near-optimal control to save their energy consumption. The map is formed with diversified data sources and updated periodically to ensure its good quality. Two simulation studies are conducted to validate the effectiveness of the proposed strategy in saving the chillers’ energy consumption. In Study 1, the proposed strategy is simulated during 8 weeks in August and September, with its map updating and not updating respectively. The result shows that periodically updating the map can make the proposed strategy more effective in energy saving. And the computation time per execution of the proposed strategy is feasible for online application. In Study 2, the proposed strategy is simulated from May to November. The result indicates that the proposed strategy is insensible to the initial history data, and can reduce the chillers’ energy consumption comparing with the sequence strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:1444-1451
    DOI: 10.1016/j.apenergy.2019.01.170
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919301655
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.170?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. Lubis, Arnas & Jeong, Jongsoo & Giannetti, Niccolo & Yamaguchi, Seiichi & Saito, Kiyoshi & Yabase, Hajime & Alhamid, Muhammad I. & Nasruddin,, 2018. "Operation performance enhancement of single-double-effect absorption chiller," Applied Energy, Elsevier, vol. 219(C), pages 299-311.
    3. Candanedo, J.A. & Dehkordi, V.R. & Stylianou, M., 2013. "Model-based predictive control of an ice storage device in a building cooling system," Applied Energy, Elsevier, vol. 111(C), pages 1032-1045.
    4. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    5. Du, Zhimin & Chen, Ling & Jin, Xinqiao, 2017. "Data-driven based reliability evaluation for measurements of sensors in a vapor compression system," Energy, Elsevier, vol. 122(C), pages 237-248.
    6. Sapienza, Alessio & Santamaria, Salvatore & Frazzica, Andrea & Freni, Angelo, 2011. "Influence of the management strategy and operating conditions on the performance of an adsorption chiller," Energy, Elsevier, vol. 36(9), pages 5532-5538.
    7. 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.
    8. 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.
    9. Chang, Yung-Chung & Chen, Wu-Hsing, 2009. "Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy," Energy, Elsevier, vol. 34(4), pages 448-456.
    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. Coelho, Leandro dos Santos & Klein, Carlos Eduardo & Sabat, Samrat L. & Mariani, Viviana Cocco, 2014. "Optimal chiller loading for energy conservation using a new differential cuckoo search approach," Energy, Elsevier, vol. 75(C), pages 237-243.
    12. Yu, F.W. & Chan, K.T., 2010. "Simulation and electricity savings estimation of air-cooled centrifugal chiller system with mist pre-cooling," Applied Energy, Elsevier, vol. 87(4), pages 1198-1206, April.
    13. Yu, F.W. & Chan, K.T., 2008. "Optimization of water-cooled chiller system with load-based speed control," Applied Energy, Elsevier, vol. 85(10), pages 931-950, October.
    14. Chang, Yung-Chung, 2006. "An innovative approach for demand side management—optimal chiller loading by simulated annealing," Energy, Elsevier, vol. 31(12), pages 1883-1896.
    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. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    2. Lian, Kuang-Yow & Hong, Yong-Jie & Chang, Che-Wei & Su, Yu-Wei, 2022. "A novel data-driven optimal chiller loading regulator based on backward modeling approach," Applied Energy, Elsevier, vol. 327(C).
    3. 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).
    4. Catrini, P. & Panno, D. & Cardona, F. & Piacentino, A., 2020. "Characterization of cooling loads in the wine industry and novel seasonal indicator for reliable assessment of energy saving through retrofit of chillers," Applied Energy, Elsevier, vol. 266(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. 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. Xue, Qi & Jin, Xinqiao & Jia, Zhiyang & Lyu, Yuan & Du, Zhimin, 2024. "Optimal control strategy of multiple chiller system based on background knowledge graph," Applied Energy, Elsevier, vol. 375(C).
    3. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    4. 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.
    5. 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).
    6. Federica Acerbi & Mirco Rampazzo & Giuseppe De Nicolao, 2020. "An Exact Algorithm for the Optimal Chiller Loading Problem and Its Application to the Optimal Chiller Sequencing Problem," Energies, MDPI, vol. 13(23), pages 1-29, December.
    7. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    8. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    9. 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.
    10. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    11. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Shugang & Gong, Xuemei, 2018. "A model-based optimal control strategy for ground source heat pump systems with integrated solar photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 228(C), pages 1399-1412.
    12. Chen, Qun & Xu, Yun-Chao & Hao, Jun-Hong, 2014. "An optimization method for gas refrigeration cycle based on the combination of both thermodynamics and entransy theory," Applied Energy, Elsevier, vol. 113(C), pages 982-989.
    13. Olszewski, Pawel, 2022. "Experimental analysis of ON/OFF and variable speed drive controlled industrial chiller towards energy efficient operation," Applied Energy, Elsevier, vol. 309(C).
    14. Min-Yong Qi & Jun-Qing Li & Yu-Yan Han & Jin-Xin Dong, 2020. "Optimal Chiller Loading for Energy Conservation Using an Improved Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 13(15), pages 1-18, July.
    15. Ma, Keyan & Liu, Mingsheng & Zhang, Jili, 2021. "Online optimization method of cooling water system based on the heat transfer model for cooling tower," Energy, Elsevier, vol. 231(C).
    16. Chang-Ming Lin & Chun-Yin Wu & Ko-Ying Tseng & Chih-Chiang Ku & Sheng-Fuu Lin, 2019. "Applying Two-Stage Differential Evolution for Energy Saving in Optimal Chiller Loading," Energies, MDPI, vol. 12(4), pages 1-12, February.
    17. 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).
    18. Guofu Luo & Tianxing Sun & Haoqi Wang & Hao Li & Jiaqi Wang & Zhuang Miao & Honglei Si & Fuliang Che & Gen Liu, 2023. "An Energy-Saving Regulation Framework of Central Air Conditioning Based on Cloud–Edge–Device Architecture," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    19. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    20. Liu, Mingzhe & Ooka, Ryozo & Choi, Wonjun & Ikeda, Shintaro, 2019. "Experimental and numerical investigation of energy saving potential of centralized and decentralized pumping systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.

    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:238:y:2019:i:c:p:1444-1451. 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.