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Optimizing mixed cool thermal storage systems across a connected community

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

Abstract

A high level of electric demand flexibility must be integrated into our building infrastructure to enable greater renewable energy penetration in the grid. In the U.S., 9 % of electricity generated is used to cool buildings in a periodic manner, making this end-use an ideal target for active management through cool thermal energy storage (CTES) technologies. Historic uses for CTES are designed around central chilled water plants, but these systems cool less than 25 % of U.S. commercial floorspace. Emerging technologies are under development to serve the many smaller distributed cooling systems, such as rooftop units (RTUs), and have the potential to add CTES to an additional 66 % of cooled commercial floorspace. However, these unitary thermal storage systems (UTSS) lack the modeling and analysis tools to evaluate them in the future interactive grid context. This study develops the modeling and optimization tools necessary to simultaneously examine central and distributed ice storage systems within the multi-building, connected community context. An integrated simulation-optimization workflow is created to allow for rapid customized analysis. Results demonstrate the energy and flexibility tradeoffs of various implementations.

Suggested Citation

  • Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2023. "Optimizing mixed cool thermal storage systems across a connected community," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027275
    DOI: 10.1016/j.energy.2023.129333
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    1. Zekai Şen & Eyüp Şişman & Ismail Dabanli, 2020. "Wet and dry spell feature charts for practical uses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 1975-1986, December.
    2. 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.
    3. Ruan, Yingjun & Liu, Qingrong & Li, Zhengwei & Wu, Jiazheng, 2016. "Optimization and analysis of Building Combined Cooling, Heating and Power (BCHP) plants with chilled ice thermal storage system," Applied Energy, Elsevier, vol. 179(C), pages 738-754.
    4. 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).
    5. Balzanelli GM & Distratis P & Amatulli F & Catucci O & Cefalo A & D’Angela G & Lazzaro R & Palazzo D & Aityan KS & Dipalma G & Inchingolo F & Nguyen KCD & Pham HV & Tomassone D & Tran Cong T & Gargi, 2020. "Clinical Features in Predicting COVID-19," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 29(5), pages 22921-22926, August.
    6. 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.
    7. Perevoznikov E. N., 2020. "Spectral Features of Systems With Chaotic Dynamics," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 6(6), pages 58-65, 06-2020.
    8. Tommy K. Y. Cheung & Simon K. C. Cheung, 2020. "Spatial dependence model with feature difference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 615-627, July.
    9. Cole, Wesley J. & Rhodes, Joshua D. & Gorman, William & Perez, Krystian X. & Webber, Michael E. & Edgar, Thomas F., 2014. "Community-scale residential air conditioning control for effective grid management," Applied Energy, Elsevier, vol. 130(C), pages 428-436.
    10. Chan, Apple L.S. & Chow, Tin-Tai & Fong, Square K.F. & Lin, John Z., 2006. "Performance evaluation of district cooling plant with ice storage," Energy, Elsevier, vol. 31(14), pages 2750-2762.
    11. Mazzoni, Stefano & Sze, Jia Yin & Nastasi, Benedetto & Ooi, Sean & Desideri, Umberto & Romagnoli, Alessandro, 2021. "A techno-economic assessment on the adoption of latent heat thermal energy storage systems for district cooling optimal dispatch & operations," Applied Energy, Elsevier, vol. 289(C).
    12. Barbour, Edward & Parra, David & Awwad, Zeyad & González, Marta C., 2018. "Community energy storage: A smart choice for the smart grid?," Applied Energy, Elsevier, vol. 212(C), pages 489-497.
    13. 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).
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