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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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    1. Deng, Tingting & Yan, Wenzhou & Nojavan, Sayyad & Jermsittiparsert, Kittisak, 2020. "Risk evaluation and retail electricity pricing using downside risk constraints method," Energy, Elsevier, vol. 192(C).
    2. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    3. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program," Applied Energy, Elsevier, vol. 187(C), pages 449-464.
    4. Feihu Hu & Xuan Feng & Hui Cao, 2018. "A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing," Energies, MDPI, vol. 11(12), pages 1-18, November.
    5. Qi Zhang & Shaohua Zhang & Xian Wang & Xue Li & Lei Wu, 2020. "Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand," Energies, MDPI, vol. 13(6), pages 1-19, March.
    6. Khojasteh, Meysam & Jadid, Shahram, 2015. "Decision-making framework for supplying electricity from distributed generation-owning retailers to price-sensitive customers," Utilities Policy, Elsevier, vol. 37(C), pages 1-12.
    7. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    8. Ahmadi, Abdollah & Charwand, Mansour & Siano, Pierluigi & Nezhad, Ali Esmaeel & Sarno, Debora & Gitizadeh, Mohsen & Raeisi, Fatima, 2016. "A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company," Energy, Elsevier, vol. 117(P1), pages 1-9.
    9. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    10. Fotouhi Ghazvini, Mohammad Ali & Soares, João & Horta, Nuno & Neves, Rui & Castro, Rui & Vale, Zita, 2015. "A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers," Applied Energy, Elsevier, vol. 151(C), pages 102-118.
    11. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    12. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    Full references (including those not matched with items on IDEAS)

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    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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    15. 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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