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An improved incentive-based demand response program in day-ahead and intra-day electricity markets

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  • Shahryari, E.
  • Shayeghi, H.
  • Mohammadi-ivatloo, B.
  • Moradzadeh, M.

Abstract

By advancement and vogue of smart grid technologies, there is a strong attitude toward utilizing different strategies for participating in demand response (DR) programs in electricity markets. DR programs can be classified into two main categories namely incentive-based programs (IBPs) and time-based rate programs (TBRPs). In this paper, an improved incentive-based DR (IBDR) model is proposed. In our proposed IBP, the concept of elasticity is improved where it depends not only on the electricity price, but also is a function of consumption hour and customer type. In this program, the incentive value which is paid to the participating consumers is not a fix value and relates to the peak intensity of each hour. The proposed IBP can participate in both of day-ahead and intra-day electricity markets. The property of considering intra-day market enables consumers to provide maximum DR if possible. The proposed model is implemented on peak load curve of Spanish electricity market and a 200-unit residential complex. Different scenarios are considered to show effectiveness of the proposed DR model from various aspects including peak shaving as well as economic indices.

Suggested Citation

  • Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    • Handle: RePEc:eee:energy:v:155:y:2018:i:c:p:205-214
    DOI: 10.1016/j.energy.2018.04.170
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    6. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
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    14. Lu, Jun & Liu, Tianqi & He, Chuan & Nan, Lu & Hu, Xiaotong, 2021. "Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy," Renewable Energy, Elsevier, vol. 178(C), pages 466-482.
    15. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    16. Saebi, Javad & Ghasemi, Abolfazl & Hojjat, Mehrdad, 2022. "Design and implementation of a competitive framework for a day-ahead demand-response program in Iran," Utilities Policy, Elsevier, vol. 77(C).
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