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Optimal combined scheduling of generation and demand response with demand resource constraints

Author

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  • Kwag, Hyung-Geun
  • Kim, Jin-O

Abstract

Demand response (DR) extends customer participation to power systems and results in a paradigm shift from simplex to interactive operation in power systems due to the advancement of smart grid technology. Therefore, it is important to model the customer characteristics in DR. This paper proposes customer information as the registration and participation information of DR, thus providing indices for evaluating customer response, such as DR magnitude, duration, frequency and marginal cost. The customer response characteristics are modeled from this information. This paper also introduces the new concept of virtual generation resources, whose marginal costs are calculated in the same manner as conventional generation marginal costs, according to customer information. Finally, some of the DR constraints are manipulated and expressed using the information modeled in this paper with various status flags. Optimal scheduling, combined with generation and DR, is proposed by minimizing the system operation cost, including generation and DR costs, with the generation and DR constraints developed in this paper.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:161-170
    DOI: 10.1016/j.apenergy.2011.12.075
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    Cited by:

    1. Arasteh, Hamidreza & Sepasian, Mohammad Sadegh & Vahidinasab, Vahid, 2016. "An aggregated model for coordinated planning and reconfiguration of electric distribution networks," Energy, Elsevier, vol. 94(C), pages 786-798.
    2. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    3. 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.
    4. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    5. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    6. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    7. Reihani, Ehsan & Motalleb, Mahdi & Thornton, Matsu & Ghorbani, Reza, 2016. "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, Elsevier, vol. 183(C), pages 445-455.
    8. K. Selvakumar & K. Vijayakumar & C. S. Boopathi, 2017. "Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit," Energies, MDPI, vol. 10(10), pages 1-18, September.
    9. Toh, G.K. & Gooi, H.B., 2012. "Procurement of interruptible load services in electricity supply systems," Applied Energy, Elsevier, vol. 98(C), pages 533-539.
    10. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    11. M.‐Elisabeth Paté‐Cornell & Marshall Kuypers & Matthew Smith & Philip Keller, 2018. "Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies," Risk Analysis, John Wiley & Sons, vol. 38(2), pages 226-241, February.
    12. Xu, Fang Yuan & Zhang, Tao & Lai, Loi Lei & Zhou, Hao, 2015. "Shifting Boundary for price-based residential demand response and applications," Applied Energy, Elsevier, vol. 146(C), pages 353-370.
    13. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    14. 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.
    15. Roos, Aleksandra & Bolkesjø, Torjus Folsland, 2018. "Value of demand flexibility on spot and reserve electricity markets in future power system with increased shares of variable renewable energy," Energy, Elsevier, vol. 144(C), pages 207-217.
    16. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    17. 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.
    18. Eissa, M.M., 2019. "Developing incentive demand response with commercial energy management system (CEMS) based on diffusion model, smart meters and new communication protocol," Applied Energy, Elsevier, vol. 236(C), pages 273-292.
    19. Seungmi Lee & Jinho Kim, 2018. "Analytical Assessment for System Peak Reduction by Demand Responsive Resources Considering Their Operational Constraints in Wholesale Electricity Market," Energies, MDPI, vol. 11(12), pages 1-15, November.

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