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Price Strategy Analysis of Electricity Retailers Based on Evolutionary Game on Complex Networks

Author

Listed:
  • Xinyi Xie

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Liming Ying

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Xue Cui

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

This paper analyzes the price strategy of electricity retailers in different trading scenarios. In our empirical work, we use the evolutionary game model based on the complex network to describe the information interaction relationship and game relationship between electricity retailers, and reflect the user’s switching behavior through the market share function. The purpose of our work is to study the impact of network structure, contract transaction, user’s switching behavior and strategy updating rules on the price strategy of electricity retailers by applying the evolutionary game model on complex network to the retail market. The results show that network scale, contract electricity, user switching degree and overconfidence level have important influence on the price strategy selection of electricity retailers, and these parameters should be maintained within a reasonable range in order to maximize the interests of electricity retailers and achieve the balance of retail bidding. By mapping the results of the game model to the actual policy and retail market, we believe that the evolutionary game model on network is a useful tool to analyze the competition of electricity retailers. More importantly, the conclusions can provide a reference for electricity retailers when choosing a retail price strategy and for future works which aim to analyze the development of the retail electricity market.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9487-:d:878603
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    References listed on IDEAS

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    Cited by:

    1. Songtao He & Yifan Tang, 2024. "Effects of Personalized Demands on the Digital Diffusion of Enterprises: A Complex Network Evolution Game Model-Based Study," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 12854-12880, September.

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