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Design and dispatch optimization of packaged ice storage systems within a connected community

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  • Heine, Karl
  • Tabares-Velasco, Paulo Cesar
  • Deru, Michael

Abstract

The traditional implementation of cool thermal energy storage (CTES) must be reimagined within the context of a dynamic grid and smart buildings operating as connected communities. As most buildings do not operate central chillers or connect to district cooling loops, this necessitates a broader use of packaged CTES. Our objective is to begin answering the question of how such packaged CTES should be implemented within a connected community. We do so by presenting a simulation–optimization workflow employing building energy modeling software and a mixed-integer linear program to design and dispatch a packaged CTES technology to achieve minimum total annual cost. We demonstrate this methodology on a seven-building case study using current utility rates and find that total annual cooling energy costs can be reduced by 17.8% compared to baseline, after accounting for the cost of storage. We perform three parametric sensitivity studies to evaluate modeling assumptions and obtain the prioritization of storage procurement as a function of annualized life-cycle cost of storage. We find that a community optimization approach provides significantly different results than individual building optimizations and provides greater savings compared to baseline.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921005870
    DOI: 10.1016/j.apenergy.2021.117147
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    Cited by:

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    2. 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).
    3. Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2023. "Optimizing mixed cool thermal storage systems across a connected community," Energy, Elsevier, vol. 285(C).
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    7. Liu, Zichu & Quan, Zhenhua & Zhao, Yaohua & Zhang, Wanlin & Yang, Mingguang & Shi, Junzhang, 2023. "Thermal performance analysis of ice thermal storage device based on micro heat pipe arrays: Role of bubble-driven flow," Renewable Energy, Elsevier, vol. 217(C).
    8. Liu, Zichu & Quan, Zhenhua & Zhang, Nan & Wang, Yubo & Yang, Mingguang & Zhao, Yaohua, 2023. "Energy and exergy analysis of a novel direct-expansion ice thermal storage system based on three-fluid heat exchanger module," Applied Energy, Elsevier, vol. 330(PB).
    9. Wang, Zhaojun & Zhang, Zhonghui & Zhang, Zhonglian & Lei, Dayong & Li, Moxuan & Zhang, Liuyu, 2023. "Two-layer optimization of integrated energy system with considering ambient temperature effect and variable operation scheme," Energy, Elsevier, vol. 278(C).
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