Risk evaluation and retail electricity pricing using downside risk constraints method. (1st February 2020)

• Record Type: Journal Article
• Authors: Deng, Tingting; Yan, Wenzhou; Nojavan, Sayyad; Jermsittiparsert, Kittisak
• Is Part Of: Energy. Volume 192(2020)
• Publication Date: 2020-02-01
• Subjects: Electricity retailer -- Energy pricing policy -- Social welfare -- Energy business and management -- Downside risk constraints; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116672
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: Real-time pricing by electricity retailers in the risk environment is determined. Uncertainties of input parameters are modeled via stochastic programming. Downside risk constraint method is proposed to model risk related to uncertainties. Risk-averse and risk-neutral strategies for electricity retailers are introduced. Exact amounts of retailer risk is directly measured and reduced in each scenario.