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Life-cycle cost benefit analysis and optimal design of small scale active storage system for building demand limiting

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  • Cui, Borui
  • Wang, Shengwei
  • Sun, Yongjun

Abstract

This paper presents a quantitative analysis on the life-cycle cost saving potentials of active cold storage systems concerning the operational cost, initial investment and the space cost. A simulation-based optimal design method is developed and used to optimize the storage capacity and analyze the impact of the capacity on the life-cycle cost saving potentials. Using the actual load profiles of three real buildings of different sizes, the life-cycle cost saving potentials under demand limiting control are investigated under Hong Kong electricity price structure. The optimal storage capacities, monthly/annual operational cost savings and corresponding peak demand set-points are obtained from using the marginal decision rule. Results show that small scale storages can offer substantial annual net cost saving. In addition, it can help the grid to achieve higher grid power reliability by enhancing the demand response ability of buildings.

Suggested Citation

  • Cui, Borui & Wang, Shengwei & Sun, Yongjun, 2014. "Life-cycle cost benefit analysis and optimal design of small scale active storage system for building demand limiting," Energy, Elsevier, vol. 73(C), pages 787-800.
    • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:787-800
    DOI: 10.1016/j.energy.2014.06.084
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    Citations

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    Cited by:

    1. Xu, Lei & Wang, Shengwei & Xiao, Fu, 2019. "An adaptive optimal monthly peak building demand limiting strategy considering load uncertainty," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Cui, Borui & Gao, Dian-ce & Wang, Shengwei & Xue, Xue, 2015. "Effectiveness and life-cycle cost-benefit analysis of active cold storages for building demand management for smart grid applications," Applied Energy, Elsevier, vol. 147(C), pages 523-535.
    3. Ren, Haoshan & Gao, Dian-ce & Ma, Zhenjun & Zhang, Sheng & Sun, Yongjun, 2024. "Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level," Applied Energy, Elsevier, vol. 356(C).
    4. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    5. Tang, Rui & Wang, Shengwei, 2019. "Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 873-882.
    6. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    7. 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.
    8. Gert van Wyk & Vinessa Naidoo & E. Innocents Edoun, 2021. "Guiding Principles for Establishing Energy Consumption Reduction and Increase Production Performance in Manufacturing," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 502-515.
    9. Kovacic, Iva & Zoller, Veronika, 2015. "Building life cycle optimization tools for early design phases," Energy, Elsevier, vol. 92(P3), pages 409-419.

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