Determination of the optimal strategy for the presence of a retailer in the next day's electricity market based on the risk index

Document Type : Original Article

Authors

1 KGUT

2 Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology

3 shahid bahonar keman

Abstract

Recently in Iran, due to the growing trend of electric energy consumption and the limitations of state resources in creating new capacities in the sectors of production, transmission, and distribution, the issue of privatization and restructuring in the electricity industry has been the same as in other countries of the world. At the moment, the wholesale market has begun its business, and soon it will include other sectors of the electricity industry, and distribution companies will be the primary buyers of electricity in this market. In this paper, due to the importance of the issue, the determination of the optimal strategy for the presence of a retailer in the competitive markets has been addressed. A linear model, which considers the behavior of a retailer in wholesale and retail markets, has been presented. Artificial neural networks are used to generate scenarios of price and consumer demand. In addition, the retailer determines its maximum profit based on taking into account different pricing methods offered to the end consumers. In this study, by presenting an indicator for calculating the risk in the decisions of the retail company, the role of this factor is discussed in the company's profits. According to the results, in the real-time pricing mode, the retailer increases its dependency on the pool, compared with the time of use and fixed pricing methods. In addition, the expected profit of the retailer has an inverse relationship with the amount of risk-aversion by the retailer.

Keywords

Main Subjects

References

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Journal of Energy Management and Technology
Volume 4, Issue 2
June 2020
Pages 60-68

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History

  • Receive Date: 05 October 2019
  • Revise Date: 23 December 2019
  • Accept Date: 13 February 2020

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