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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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    21. Morimoto, Takashi & Asaoka, Tatsunori & Kumano, Hiroyuki, 2023. "Heat storage characteristics of multi-component sugar alcohol slurries," Energy, Elsevier, vol. 272(C).
    22. 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.
    23. 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.
    24. 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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