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An Effective Method of Equivalent Load-Based Time of Use Electricity Pricing to Promote Renewable Energy Consumption

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  • Xiaoqing Zeng

    (School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China)

  • Zilin He

    (College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China)

  • Yali Wang

    (School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China)

  • Yongfei Wu

    (School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China)

  • Ao Liu

    (School of Economics and Management, Changsha University of Science and Technology, Changsha 410075, China)

Abstract

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users’ social network interactions to gain popularity. By incorporating the users’ mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users’ electricity charges.

Suggested Citation

  • Xiaoqing Zeng & Zilin He & Yali Wang & Yongfei Wu & Ao Liu, 2024. "An Effective Method of Equivalent Load-Based Time of Use Electricity Pricing to Promote Renewable Energy Consumption," Mathematics, MDPI, vol. 12(9), pages 1-27, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1408-:d:1388615
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    References listed on IDEAS

    as
    1. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    2. Huilan Jiang & Bingqi Liu & Yawei Wang & Shuangqi Zheng, 2014. "Multiobjective TOU Pricing Optimization Based on NSGA2," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-8, July.
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