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Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings

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  • Cui, Borui
  • Gao, Dian-ce
  • Xiao, Fu
  • Wang, Shengwei

Abstract

This paper provides a method to evaluate the cost-saving potential of active cool thermal energy storage (CTES) integrated with HVAC system for demand management in commercial building. Active storage is capable of shifting peak demand for peak load management (PLM) as well as providing longer duration and larger capacity for demand response (DR). In this research, a model-based optimal design method using genetic algorithm is developed to optimize the capacity of active CTES for maximizing the life-cycle cost saving including capital cost associated with storage capacity as well as incentives from both fast DR and PLM. In the method, the active CTES operates under a fast DR control strategy during DR events and under the storage-priority operation mode to shift peak demand during normal days. The optimal storage capacities, maximum annual net cost saving and corresponding power reduction set-points during DR events are obtained by using the proposed optimal design method. This research provides guidance in comprehensive evaluation of the cost-saving potential of active CTES integrated with HVAC system for building demand management including both fast DR and PLM.

Suggested Citation

  • Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    • Handle: RePEc:eee:appene:v:201:y:2017:i:c:p:382-396
    DOI: 10.1016/j.apenergy.2016.12.035
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    3. Chai, Jiale & Huang, Pei & Sun, Yongjun, 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions," Energy, Elsevier, vol. 185(C), pages 176-189.
    4. 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.
    5. Huang, Jing & Boland, John & Liu, Weidong & Xu, Chang & Zang, Haixiang, 2018. "A decision-making tool for determination of storage capacity in grid-connected PV systems," Renewable Energy, Elsevier, vol. 128(PA), pages 299-304.
    6. Lizana, Jesús & Chacartegui, Ricardo & Barrios-Padura, Angela & Valverde, José Manuel, 2017. "Advances in thermal energy storage materials and their applications towards zero energy buildings: A critical review," Applied Energy, Elsevier, vol. 203(C), pages 219-239.
    7. Xiaoyu Xu & Chun Chang & Xinxin Guo & Mingzhi Zhao, 2023. "Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils," Energies, MDPI, vol. 16(14), pages 1-18, July.
    8. Ren, Haoshan & Sun, Yongjun & Albdoor, Ahmed K. & Tyagi, V.V. & Pandey, A.K. & Ma, Zhenjun, 2021. "Improving energy flexibility of a net-zero energy house using a solar-assisted air conditioning system with thermal energy storage and demand-side management," Applied Energy, Elsevier, vol. 285(C).
    9. Liu, Mingzhe & Heiselberg, Per, 2019. "Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics," Applied Energy, Elsevier, vol. 233, pages 764-775.
    10. Lake, Andrew & Rezaie, Behanz, 2018. "Energy and exergy efficiencies assessment for a stratified cold thermal energy storage," Applied Energy, Elsevier, vol. 220(C), pages 605-615.
    11. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    12. 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.
    13. 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.
    14. Mehrjerdi, Hasan & Bornapour, Mosayeb & Hemmati, Reza & Ghiasi, Seyyed Mohammad Sadegh, 2019. "Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes," Energy, Elsevier, vol. 168(C), pages 919-930.
    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. Ferrara, Maria & Rolfo, Andrea & Prunotto, Federico & Fabrizio, Enrico, 2019. "EDeSSOpt – Energy Demand and Supply Simultaneous Optimization for cost-optimized design: Application to a multi-family building," Applied Energy, Elsevier, vol. 236(C), pages 1231-1248.
    17. Liang, Zheming & Bian, Desong & Zhang, Xiaohu & Shi, Di & Diao, Ruisheng & Wang, Zhiwei, 2019. "Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market," Applied Energy, Elsevier, vol. 236(C), pages 916-926.
    18. Ebrahimi, Mahyar, 2020. "Storing electricity as thermal energy at community level for demand side management," Energy, Elsevier, vol. 193(C).

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