IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v262y2023ipas0360544222023519.html
   My bibliography  Save this article

Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation

Author

Listed:
  • Zou, Wenke
  • Sun, Yongjun
  • Gao, Dian-ce
  • Zhang, Xu

Abstract

The integration of thermal energy storage in chilled water systems is an effective way to improve energy efficiency and is essential for achieving carbon emission reduction. However, the commonly used large-scale thermal energy storage needs significantly larger space, which hinders the wide application of thermal storage in large number of existing buildings. Unlike previous studies, this study integrated a small-scale stratified chilled water storage tank into chilled water plants and proposed a global optimal control strategy to enhance the overall system energy performance. The proposed strategy determines the optimal settings of stratified chilled water storage tank charging/discharging flow rate, chilled water supply temperature, and the number of chillers in order to minimize the daily energy consumption of the chilled water plants under varying load conditions. The stratified chilled water storage tank was modelled as a “virtual chiller” to quantify the energy consumption related to the charging/discharging. Multiple charging/discharging cycles were controlled for optimal chiller loading. The proposed control strategy was evaluated in a simulated complex central chilled water plant. The results show that the proposed optimal control strategy can save the daily energy consumption of the central chilled water plant by 4.35–7.67%, 2.10–3.90%, and 2.30–5.15% in three typical weather conditions.

Suggested Citation

  • Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).
  • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023519
    DOI: 10.1016/j.energy.2022.125469
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222023519
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.125469?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. Sun, Yongjun & Huang, Gongsheng & Xu, Xinhua & Lai, Alvin Chi-Keung, 2018. "Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls," Applied Energy, Elsevier, vol. 212(C), pages 565-576.
    2. Shan, Kui & Wang, Jiayuan & Hu, Maomao & Gao, Dian-ce, 2019. "A model-based control strategy to recover cooling energy from thermal mass in commercial buildings," Energy, Elsevier, vol. 172(C), pages 958-967.
    3. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    4. Gao, Dian-ce & Wang, Shengwei & Shan, Kui, 2016. "In-situ implementation and evaluation of an online robust pump speed control strategy for avoiding low delta-T syndrome in complex chilled water systems of high-rise buildings," Applied Energy, Elsevier, vol. 171(C), pages 541-554.
    5. Yan, Tian & Sun, Zhongwei & Gao, Jiajia & Xu, Xinhua & Yu, Jinghua & Gang, Wenjie, 2020. "Simulation study of a pipe-encapsulated PCM wall system with self-activated heat removal by nocturnal sky radiation," Renewable Energy, Elsevier, vol. 146(C), pages 1451-1464.
    6. Shan, Kui & Fan, Cheng & Wang, Jiayuan, 2019. "Model predictive control for thermal energy storage assisted large central cooling systems," Energy, Elsevier, vol. 179(C), pages 916-927.
    7. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    8. 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).
    9. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    10. 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.
    11. Ashok, S. & Banerjee, R., 2003. "Optimal cool storage capacity for load management," Energy, Elsevier, vol. 28(2), pages 115-126.
    12. Soler, Mònica Subirats & Sabaté, Carles Civit & Santiago, Víctor Benito & Jabbari, Faryar, 2016. "Optimizing performance of a bank of chillers with thermal energy storage," Applied Energy, Elsevier, vol. 172(C), pages 275-285.
    13. Luo, Na & Hong, Tianzhen & Li, Hui & Jia, Ruoxi & Weng, Wenguo, 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 459-475.
    14. Anderson, Austin & Rezaie, Behnaz & Rosen, Marc A., 2021. "An innovative approach to enhance sustainability of a district cooling system by adjusting cold thermal storage and chiller operation," Energy, Elsevier, vol. 214(C).
    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. 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).
    2. Fanghan Su & Zhiyuan Wang & Yue Yuan & Chengcheng Song & Kejun Zeng & Yixing Chen & Rongpeng Zhang, 2023. "Enhanced Operation of Ice Storage System for Peak Load Management in Shopping Malls across Diverse Climate Zones," Sustainability, MDPI, vol. 15(20), pages 1-23, October.

    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. Fanghan Su & Zhiyuan Wang & Yue Yuan & Chengcheng Song & Kejun Zeng & Yixing Chen & Rongpeng Zhang, 2023. "Enhanced Operation of Ice Storage System for Peak Load Management in Shopping Malls across Diverse Climate Zones," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    2. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    3. Gao, Cheng & Wang, Dan & Sun, Yuying & Wang, Wei & Zhang, Xiuyu, 2023. "Optimal load dispatch of multi-source looped district cooling systems based on energy and hydraulic performances," Energy, Elsevier, vol. 274(C).
    4. Zhang, Wei & Hong, Wenpeng & Jin, Xu, 2022. "Research on performance and control strategy of multi-cold source district cooling system," Energy, Elsevier, vol. 239(PB).
    5. Jia, Lizhi & Liu, Junjie & Chong, Adrian & Dai, Xilei, 2022. "Deep learning and physics-based modeling for the optimization of ice-based thermal energy systems in cooling plants," Applied Energy, Elsevier, vol. 322(C).
    6. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Wunvisa Tipasri & Amnart Suksri & Karthikeyan Velmurugan & Tanakorn Wongwuttanasatian, 2022. "Energy Management for an Air Conditioning System Using a Storage Device to Reduce the On-Peak Power Consumption," Energies, MDPI, vol. 15(23), pages 1-19, November.
    8. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    9. Dufour, Thomas & Hoang, Hong Minh & Oignet, Jérémy & Osswald, Véronique & Clain, Pascal & Fournaison, Laurence & Delahaye, Anthony, 2017. "Impact of pressure on the dynamic behavior of CO2 hydrate slurry in a stirred tank reactor applied to cold thermal energy storage," Applied Energy, Elsevier, vol. 204(C), pages 641-652.
    10. Neri, Manfredi & Guelpa, Elisa & Verda, Vittorio, 2022. "Design and connection optimization of a district cooling network: Mixed integer programming and heuristic approach," Applied Energy, Elsevier, vol. 306(PA).
    11. 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).
    12. Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2021. "Design and dispatch optimization of packaged ice storage systems within a connected community," Applied Energy, Elsevier, vol. 298(C).
    13. 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).
    14. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    15. Ron-Hendrik Peesel & Florian Schlosser & Henning Meschede & Heiko Dunkelberg & Timothy G. Walmsley, 2019. "Optimization of Cooling Utility System with Continuous Self-Learning Performance Models," Energies, MDPI, vol. 12(10), pages 1-17, May.
    16. Athanasios Anagnostis & Serafeim Moustakidis & Elpiniki Papageorgiou & Dionysis Bochtis, 2022. "A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling," Energies, MDPI, vol. 15(6), pages 1-24, March.
    17. Shan, Kui & Wang, Shengwei & Zhuang, Chaoqun, 2021. "Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests," Applied Energy, Elsevier, vol. 300(C).
    18. Yang, Weijia & Huang, Yuping & Zhao, Daiqing, 2023. "A coupled hydraulic–thermal dynamic model for the steam network in a heat–electricity integrated energy system," Energy, Elsevier, vol. 263(PC).
    19. Zhu, Kai & Li, Xueqiang & Campana, Pietro Elia & Li, Hailong & Yan, Jinyue, 2018. "Techno-economic feasibility of integrating energy storage systems in refrigerated warehouses," Applied Energy, Elsevier, vol. 216(C), pages 348-357.
    20. Fan, Cheng & Huang, Gongsheng & Sun, Yongjun, 2018. "A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level," Energy, Elsevier, vol. 164(C), pages 536-549.

    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:energy:v:262:y:2023:i:pa:s0360544222023519. 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.journals.elsevier.com/energy .

    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.