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Reliability modeling of demand response considering uncertainty of customer behavior

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  • Kwag, Hyung-Geun
  • Kim, Jin-O

Abstract

Demand response (DR) has been considered as a generation alternative to improve the reliability indices of the system and load point. However, when the demand resources scheduled in the DR market fail to result in demand reductions, it can potentially bring new problems associated with maintaining a reliable supply. In this paper, a reliability model of the demand resource is constructed considering customers’ behaviors in the same form as conventional generation units, where the availability and unavailability are associated with the simple two-state model. The reliability model is generalized by a multi-state model. In the integrated power market with DR, market players provide the demand reduction and generation, which are represented by an equivalent multi-state demand response and generation, respectively. The reliability indices of the system and load point are evaluated using the optimal power flow by minimizing the summation of load curtailments with various constraints.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:122:y:2014:i:c:p:24-33
    DOI: 10.1016/j.apenergy.2014.01.068
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    References listed on IDEAS

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    1. Joung, Manho & Kim, Jinho, 2013. "Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability," Applied Energy, Elsevier, vol. 101(C), pages 441-448.
    2. Kwag, Hyung-Geun & Kim, Jin-O, 2012. "Optimal combined scheduling of generation and demand response with demand resource constraints," Applied Energy, Elsevier, vol. 96(C), pages 161-170.
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    6. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2015. "Performance evaluation of power demand scheduling scenarios in a smart grid environment," Applied Energy, Elsevier, vol. 142(C), pages 164-178.
    7. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
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    14. Cheng, Lin & Wan, Yuxiang & Tian, Liting & Zhang, Fang, 2019. "Evaluating energy supply service reliability for commercial air conditioning loads from the distribution network aspect," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. 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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    17. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
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    20. Ponce de Leon Barido, Diego & Suffian, Stephen & Kammen, Daniel M. & Callaway, Duncan, 2018. "Opportunities for behavioral energy efficiency and flexible demand in data-limited low-carbon resource constrained environments," Applied Energy, Elsevier, vol. 228(C), pages 512-523.
    21. Postnikov, Ivan & Stennikov, Valery & Mednikova, Ekaterina & Penkovskii, Andrey, 2018. "Methodology for optimization of component reliability of heat supply systems," Applied Energy, Elsevier, vol. 227(C), pages 365-374.
    22. Neda Hajibandeh & Miadreza Shafie-khah & Sobhan Badakhshan & Jamshid Aghaei & Sílvio J. P. S. Mariano & João P. S. Catalão, 2019. "Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme," Energies, MDPI, vol. 12(7), pages 1-16, April.
    23. 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.
    24. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.

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