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Impact of behavior-driven demand response on supply adequacy in smart distribution systems

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  • Zeng, Bo
  • Wu, Geng
  • Wang, Jianhui
  • Zhang, Jianhua
  • Zeng, Ming

Abstract

As an integral feature of a future smart grid, demand response (DR) provides utility companies an emerging alternative to boost the reliability performance (e.g., supply adequacy) of power systems. Unlike for physical devices, the availability of DR is not only dependent on the operations of electric appliances on the demand side but can be affected by customers’ behaviors. Thus, how or to what extent DR actions can affect power system reliability becomes an important issue for utilities. In this paper, a new approach for assessing the contribution of incentive-based DR to the supply adequacy of smart distribution systems (SDS) is presented. Compared with existing methods, our method explicitly captures the varying availability of customer DR capabilities. We introduce a behavior-reinforcement procedure to model the correlation of users’ participation willingness with their historical DR profitability. Then, a strategic DR dispatch strategy can be developed to optimize users’ DR availability bids in the market. In addition, the interdependency between a communication system and DR operation has also been considered. A hybrid algorithm based on sequential Monte-Carlo simulation and an optimal load dispatch method is employed to evaluate the system reliability in this context. The proposed approach is illustrated using both a small-scale test case and a real regional distribution grid in China. The results demonstrate the effectiveness of the presented method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:125-137
    DOI: 10.1016/j.apenergy.2017.05.098
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    References listed on IDEAS

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    1. Rious, Vincent & Perez, Yannick & Roques, Fabien, 2015. "Which electricity market design to encourage the development of demand response?," Economic Analysis and Policy, Elsevier, vol. 48(C), pages 128-138.
    2. Li, Yanfu & Zio, Enrico, 2012. "Uncertainty analysis of the adequacy assessment model of a distributed generation system," Renewable Energy, Elsevier, vol. 41(C), pages 235-244.
    3. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    4. Kwag, Hyung-Geun & Kim, Jin-O, 2014. "Reliability modeling of demand response considering uncertainty of customer behavior," Applied Energy, Elsevier, vol. 122(C), pages 24-33.
    5. Paliwal, Priyanka & Patidar, N.P. & Nema, R.K., 2014. "Planning of grid integrated distributed generators: A review of technology, objectives and techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 557-570.
    6. Li, Gengfeng & Bie, Zhaohong & Xie, Haipeng & Lin, Yanling, 2016. "Customer satisfaction based reliability evaluation of active distribution networks," Applied Energy, Elsevier, vol. 162(C), pages 1571-1578.
    7. Nistor, Silviu & Wu, Jianzhong & Sooriyabandara, Mahesh & Ekanayake, Janaka, 2015. "Capability of smart appliances to provide reserve services," Applied Energy, Elsevier, vol. 138(C), pages 590-597.
    8. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    9. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    Full references (including those not matched with items on IDEAS)

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