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Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach

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  • Dadashi, Mojtaba
  • Haghifam, Sara
  • Zare, Kazem
  • Haghifam, Mahmoud-Reza
  • Abapour, Mehdi

Abstract

This article proposes a novel short-term decision-making model for electricity retailers within the electricity market and in the presence of Demand Response Aggregators (DRA). In this framework, retailers provide their required energy through the Day-ahead market, Real-time market and forward bilateral contracts. On the other hand, retailers participate in demand response (DR) programs based on the DRA’s DR offers to maximize their benefits. In this study, to cope with the uncertainty, the two-stage stochastic programming scheme is utilized. In the first stage, the participation of retailers in the Day-ahead market, as well as power purchased from the forward contracts are decided. In the second stage, the power exchanged with the Real-time market, and the DRA are determined after the realization of stochastic parameters. On the contrary, the DRA enhances its profit through submitting offers to retailers for two DR options. For solving the problem, a strategy based on the Game Theory, the Bi-Level stochastic programming approach is exploited to maximize the profit of both players in a competitive environment. The model is turned into a Single-Level problem by substituting the lower-level with its Karush–Kuhn–Tucker conditions. Finally, merits of the presented method are illustrated in a typical case study.

Suggested Citation

  • Dadashi, Mojtaba & Haghifam, Sara & Zare, Kazem & Haghifam, Mahmoud-Reza & Abapour, Mehdi, 2020. "Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220310331
    DOI: 10.1016/j.energy.2020.117926
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    References listed on IDEAS

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    6. Mohammad Hossein Nejati Amiri & Mehdi Mehdinejad & Amin Mohammadpour Shotorbani & Heidarali Shayanfar, 2023. "Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model," Energies, MDPI, vol. 16(3), pages 1-21, January.
    7. Beraldi, Patrizia & Khodaparasti, Sara, 2023. "Designing electricity tariffs in the retail market: A stochastic bi-level approach," International Journal of Production Economics, Elsevier, vol. 257(C).
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    9. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    10. Sara Haghifam & Kazem Zare & Mehdi Abapour & Gregorio Muñoz-Delgado & Javier Contreras, 2020. "A Stackelberg Game-Based Approach for Transactive Energy Management in Smart Distribution Networks," Energies, MDPI, vol. 13(14), pages 1-34, July.
    11. Gong, J.W. & Li, Y.P. & Lv, J. & Huang, G.H. & Suo, C. & Gao, P.P., 2022. "Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty," Applied Energy, Elsevier, vol. 308(C).
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    13. Ching-Jui Tien & Chia-Sheng Tu & Ming-Tang Tsai, 2022. "Risk Assessment of User Aggregators in Demand Bidding Markets," Energies, MDPI, vol. 16(1), pages 1-14, December.

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