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Equilibria in the competitive retail electricity market considering uncertainty and risk management

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  • Kharrati, Saeed
  • Kazemi, Mostafa
  • Ehsan, Mehdi

Abstract

In a medium term planning horizon, a retailer should determine its forward contracting and pool participating strategies as well as the selling price to be offered to the customers. Considering a competitive retail electricity market, the number of clients being supplied by any retailer is a function of the selling prices and some other characteristics of all the retailers. This paper presents an equilibrium problem formulation to model the retailer's medium term decision making problem considering the strategy of other retailers. Decision making of any single retailer is formulated as a risk constraint stochastic programming problem. Uncertainty of pool prices and clients' demands is modeled with scenario generation method and CVaR (conditional value at risk) is used as the risk measure. The resulting single retailer planning problem is a quadratic constrained programming problem which is solved using the Lagrangian relaxation method and the Nash equilibrium point of the competitive retailers is achieved by successive solving of this problem for all the retailers. The performance of the proposed method is demonstrated using a realistic case study of Texas electricity market.

Suggested Citation

  • Kharrati, Saeed & Kazemi, Mostafa & Ehsan, Mehdi, 2016. "Equilibria in the competitive retail electricity market considering uncertainty and risk management," Energy, Elsevier, vol. 106(C), pages 315-328.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:315-328
    DOI: 10.1016/j.energy.2016.03.069
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    References listed on IDEAS

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    1. Yu, Nanpeng & Tesfatsion, Leigh & Liu, Chen-Ching, 2012. "Financial Bilateral Contract Negotiation in Wholesale Electricity Markets Using Nash Bargaining Theory," ISU General Staff Papers 201201010800001470, Iowa State University, Department of Economics.
    2. Hajati, Maryam & Seifi, Hossein & Sheikh-El-Eslami, Mohamad Kazem, 2011. "Optimal retailer bidding in a DA market – a new method considering risk and demand elasticity," Energy, Elsevier, vol. 36(2), pages 1332-1339.
    3. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    4. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    5. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    6. Xinmin Hu & Daniel Ralph, 2007. "Using EPECs to Model Bilevel Games in Restructured Electricity Markets with Locational Prices," Operations Research, INFORMS, vol. 55(5), pages 809-827, October.
    7. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    8. Deng, Shi-Jie & Xu, Li, 2009. "Mean-risk efficient portfolio analysis of demand response and supply resources," Energy, Elsevier, vol. 34(10), pages 1523-1529.
    9. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    10. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    11. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
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    4. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.
    5. 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).
    6. Guo, Hongye & Chen, Qixin & Zhang, Yan & Liu, Kai & Xia, Qing & Kang, Chongqing, 2020. "Constraining the oligopoly manipulation in electricity market: A vertical integration perspective," Energy, Elsevier, vol. 194(C).
    7. Chen, Yue & Wei, Wei & Liu, Feng & Wu, Qiuwei & Mei, Shengwei, 2018. "Analyzing and validating the economic efficiency of managing a cluster of energy hubs in multi-carrier energy systems," Applied Energy, Elsevier, vol. 230(C), pages 403-416.
    8. Xinyi Xie & Liming Ying & Xue Cui, 2022. "Price Strategy Analysis of Electricity Retailers Based on Evolutionary Game on Complex Networks," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    9. Juan M. Gómez & Yeny E. Rodríguez, 2022. "Multiperiod Portfolio of Energy Purchasing Strategies including Climate Scenarios," Energies, MDPI, vol. 15(9), pages 1-25, April.
    10. Hui Wang & Congcong Wang & Wenhui Zhao, 2022. "Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies," Energies, MDPI, vol. 15(24), pages 1-23, December.
    11. Chen Zhao & Jiaqi Sun & Ping He & Shaohua Zhang & Yuqi Ji, 2023. "Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market," Energies, MDPI, vol. 16(8), pages 1-18, April.

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