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Design and implementation of a competitive framework for a day-ahead demand-response program in Iran

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  • Saebi, Javad
  • Ghasemi, Abolfazl
  • Hojjat, Mehrdad

Abstract

To replace traditional demand-side management (DSM) programs with demand-response (DR) programs, market operators have considered market-based DR. In recent years, Iran's power system has faced increasing pressure to meet the total demand, especially in warm seasons. In this regard, DSM activities have become an important tool used by Iran's power system operator for peak reduction purposes. However, there are concerns regarding the implementation of these activities, such as fair remuneration of participants, the efficiency of these programs, and customers' motivation to participate. Therefore, Iran's power system operator has started transitioning from traditional DSM activities to modern DR programs, i.e., market-based DR. Khorasan Regional Electricity Company (KREC) has taken the first step to design and implement such a market-based DR program in Iran. Hence, this paper focuses on designing and implementing a proposed day-ahead DR (DADR) program in KREC. The framework, timeline, and mathematical formulation for the clearance of the proposed DADRP via a pool-based environment are presented. Industrial customers have chosen to participate in the market in the first phase of implementing the DADRP. The real case of KREC is used to show the feasibility and efficiency of the proposed model. The results show that the proposed model can effectively determine the optimal market configuration to meet DR demand, delivering more than KREC's current DR program.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:juipol:v:77:y:2022:i:c:s095717872200056x
    DOI: 10.1016/j.jup.2022.101391
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    References listed on IDEAS

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    1. Palaniyappan, Balakumar & T, Vinopraba & Chandrasekaran, Geetha, 2023. "Solving electric power distribution uncertainty using deep learning and incentive-based demand response," Utilities Policy, Elsevier, vol. 82(C).

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