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Data analytics and optimization of an ice-based energy storage system for commercial buildings

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  • Luo, Na
  • Hong, Tianzhen
  • Li, Hui
  • Jia, Ruoxi
  • Weng, Wenguo

Abstract

Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system’s performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system’s operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:459-475
    DOI: 10.1016/j.apenergy.2017.07.048
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    9. 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.
    10. 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.
    11. Dhumane, Rohit & Ling, Jiazhen & Aute, Vikrant & Radermacher, Reinhard, 2017. "Portable personal conditioning systems: Transient modeling and system analysis," Applied Energy, Elsevier, vol. 208(C), pages 390-401.
    12. Yuness Badiei & Josue Campos do Prado, 2023. "Analyzing the Impact of Electricity Rates on the Feasibility of Solar PV and Energy Storage Systems in Commercial Buildings: Financial vs. Resilience Perspective," Energies, MDPI, vol. 16(5), pages 1-15, March.
    13. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    14. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    15. 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.
    16. Sun, Fangtian & Li, Junlong & Fu, Lin & Li, Yonghong & Wang, Ruixiang & Zhang, Shigang, 2020. "New configurations of district heating and cooling system based on absorption and compression chillers driven by waste heat of flue gas from coke ovens," Energy, Elsevier, vol. 193(C).
    17. Pan, Chunjian & Vermaak, Natasha & Romero, Carlos & Neti, Sudhakar & Hoenig, Sean & Chen, Chien-Hua & Bonner, Richard, 2018. "Cost estimation and sensitivity analysis of a latent thermal energy storage system for supplementary cooling of air cooled condensers," Applied Energy, Elsevier, vol. 224(C), pages 52-68.
    18. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Data fusion in predicting internal heat gains for office buildings through a deep learning approach," Applied Energy, Elsevier, vol. 240(C), pages 386-398.
    19. Xu, Xiao & Hu, Weihao & Liu, Wen & Du, Yuefang & Huang, Rui & Huang, Qi & Chen, Zhe, 2021. "Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy," Applied Energy, Elsevier, vol. 297(C).
    20. Morimoto, Takashi & Asaoka, Tatsunori & Kumano, Hiroyuki, 2023. "Heat storage characteristics of multi-component sugar alcohol slurries," Energy, Elsevier, vol. 272(C).
    21. Xiao Yang & Qiyang Wang & Yang Liu & Dongmei Yang & Yixu Wang & Haiyan Qin & Zedong Liu & Hua Chen, 2022. "Flow Characteristics and Heat-Transfer Enhancement of Air Agitation in Ice Storage Air Conditioning Systems," Energies, MDPI, vol. 15(16), pages 1-16, August.
    22. 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.
    23. 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.

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