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A robust demand response control of commercial buildings for smart grid under load prediction uncertainty

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  • Gao, Dian-ce
  • Sun, Yongjun
  • Lu, Yuehong

Abstract

Various demand response control strategies have been developed for grid power balance and user cost saving. Few studies have systematically considered the impacts of load prediction uncertainty which can cause the strategies fail to achieve their objectives. This study, therefore, develops a robust demand response control of commercial buildings for smart grid under load prediction uncertainty. Based on the initial control signals from the conventional genetic algorithm method, the optimal control signals with improved robustness are obtained using the Monte Carlo method. Under dynamic pricing of smart grid, the study results show the impacts of load prediction uncertainty reduce the daily electricity cost saving from 8.5% to 4.1%. Such a significant cost saving reduction implies the necessity of taking account of the load prediction uncertainty in the development of a demand response control. Moreover, under the load prediction uncertainty, the proposed demand response control can still achieve 7.3% daily electricity cost saving, which demonstrates its robustness and effectiveness. The improved robustness of the proposed control has also been demonstrated by the statistics analysis results from the Monte Carlo studies. The proposed robust control is useful for commercial buildings to achieve significant cost savings in practice particularly as uncertainty exists.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:93:y:2015:i:p1:p:275-283
    DOI: 10.1016/j.energy.2015.09.062
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    References listed on IDEAS

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    1. Bartusch, Cajsa & Alvehag, Karin, 2014. "Further exploring the potential of residential demand response programs in electricity distribution," Applied Energy, Elsevier, vol. 125(C), pages 39-59.
    2. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    3. Herter, Karen & McAuliffe, Patrick & Rosenfeld, Arthur, 2007. "An exploratory analysis of California residential customer response to critical peak pricing of electricity," Energy, Elsevier, vol. 32(1), pages 25-34.
    4. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    5. 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.
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