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Holistic modeling framework of demand response considering multi-timescale uncertainties for capacity value estimation

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  • Zeng, Bo
  • Zhao, Dongbo
  • Singh, Chanan
  • Wang, Jianhui
  • Chen, Chen

Abstract

Demand response (DR) is regarded as an effective tool to mitigate the operational uncertainties and enhance the reliability of power supply in smart grids. However, with the time-varying attribute, to what extent DR could be committed is a major concern for utilities. This paper proposes a new approach to assess the capacity value (CV) of DR with a methodological framework developed for the uncertainty modeling of DR. The novelty of this framework is its inclusion of both physical and human-related analyses in DR programs, which allows the characterization of DR variability to be accurate for the CV estimation. To achieve this, the demand-side activities in DR are disaggregated into several modules as load usage, contract selection and actual performance. Based on their intrinsic properties, different parametric models are proposed to represent the impact of each technical/social factor on the availability of DR. The parameters of these models are determined using learning-based algorithms to adapt to various behavior patterns of consumers. The outputs of the framework will serve as the quantifiers of DR capability and are integrated into reliability-based CV evaluation. The results of case studies verify the effectiveness of the proposed methodology.

Suggested Citation

  • Zeng, Bo & Zhao, Dongbo & Singh, Chanan & Wang, Jianhui & Chen, Chen, 2019. "Holistic modeling framework of demand response considering multi-timescale uncertainties for capacity value estimation," Applied Energy, Elsevier, vol. 247(C), pages 692-702.
    • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:692-702
    DOI: 10.1016/j.apenergy.2019.03.121
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    References listed on IDEAS

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    1. Zeng, Bo & Wu, Geng & Wang, Jianhui & Zhang, Jianhua & Zeng, Ming, 2017. "Impact of behavior-driven demand response on supply adequacy in smart distribution systems," Applied Energy, Elsevier, vol. 202(C), pages 125-137.
    2. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    3. Zeng, Bo & Wei, Xuan & Zhao, Dongbo & Singh, Chanan & Zhang, Jianhua, 2018. "Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties," Applied Energy, Elsevier, vol. 229(C), pages 186-200.
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    6. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
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    8. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
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    Cited by:

    1. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).

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