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Risk evaluation and retail electricity pricing using downside risk constraints method

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  • Deng, Tingting
  • Yan, Wenzhou
  • Nojavan, Sayyad
  • Jermsittiparsert, Kittisak

Abstract

Electricity in the retail market has a different value for different types of consumers. Therefore, different retail prices are usually determined for various consumers in the retail market. However, imposed risks from uncertain parameters are a big challenge in the real-time retail market pricing process. This paper proposed a real-time pricing (RTP) framework for various users including residential, commercial, and industrial consumers by the electricity retailer. In addition, uncertainties of various input parameters such as output power of renewable energy resources, electricity demand, and pool market price are modeled using scenario-based stochastic approach while downside risk constraints method is proposed to model risk associated with uncertainties. By implementing this method, electricity retailer will be able to select various risk-based strategies. Furthermore, numerical results illustrate the various risks versus various profits by the occurring of each scenario which helps the retailer for decisions-making in different scenarios. According to obtained results, retailer by choosing of zero risk strategy can reduce its risk by 100% while expected profit is reduced by 2.07%. In addition, offered RTP by the retailer is higher for industrial, commercial, and residential customers, respectively. Finally, risk-averse and risk-neutral strategies of electricity retailer are determined in the power procurement problem.

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  • 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).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219323679
    DOI: 10.1016/j.energy.2019.116672
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    5. Román Pérez-Santalla & Miguel Carrión & Carlos Ruiz, 2022. "Optimal pricing for electricity retailers based on data-driven consumers’ price-response," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 430-464, October.
    6. 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.
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    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. 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).
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