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Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs

Author

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  • Mahmood Hosseini Imani

    (Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht 4199613776, Iran)

  • Shaghayegh Zalzar

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

  • Amir Mosavi

    (Institute of Structural Mechanics, Bauhaus University Weimar, 99423 Weimar, Germany
    Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1431 Budapest, Hungary)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

Abstract

Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers’ constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer’s risk and increasing its profit.

Suggested Citation

  • Mahmood Hosseini Imani & Shaghayegh Zalzar & Amir Mosavi & Shahaboddin Shamshirband, 2018. "Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs," Energies, MDPI, vol. 11(6), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1602-:d:153270
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    References listed on IDEAS

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    2. Saeed Nosratabadi & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Andry Rakotonirainy & Kwok Wing Chau, 2019. "Sustainable Business Models: A Review," Sustainability, MDPI, vol. 11(6), pages 1-30, March.
    3. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints u4xw3, Center for Open Science.
    4. Diaz-Londono, Cesar & Enescu, Diana & Ruiz, Fredy & Mazza, Andrea, 2020. "Experimental modeling and aggregation strategy for thermoelectric refrigeration units as flexible loads," Applied Energy, Elsevier, vol. 272(C).
    5. Ghadi, Mojtaba Jabbari & Rajabi, Amin & Ghavidel, Sahand & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2019. "From active distribution systems to decentralized microgrids: A review on regulations and planning approaches based on operational factors," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints ts54m, Center for Open Science.
    7. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    8. Morteza Neishaboori & Alireza Arshadi Khamseh & Abolfazl Mirzazadeh & Mostafa Esmaeeli & Hamed Davari Ardakani, 2024. "Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 435-451, October.
    9. Chang Ye & Shihong Miao & Yaowang Li & Chao Li & Lixing Li, 2018. "Hierarchical Scheduling Scheme for AC/DC Hybrid Active Distribution Network Based on Multi-Stakeholders," Energies, MDPI, vol. 11(10), pages 1-16, October.
    10. 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.
    11. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.

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