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Optimizing Microgrid Load Fluctuations through Dynamic Pricing and Electric Vehicle Flexibility: A Comparative Analysis

Author

Listed:
  • Mahdi A. Mahdi

    (Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China)

  • Ahmed N. Abdalla

    (Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China)

  • Lei Liu

    (Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China)

  • Rendong Ji

    (Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China)

  • Haiyi Bian

    (Faculty of Electronic Information Engineering, Huayin Institute of Technology, Huai’an 223003, China)

  • Tao Hai

    (Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates)

Abstract

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Suggested Citation

  • Mahdi A. Mahdi & Ahmed N. Abdalla & Lei Liu & Rendong Ji & Haiyi Bian & Tao Hai, 2024. "Optimizing Microgrid Load Fluctuations through Dynamic Pricing and Electric Vehicle Flexibility: A Comparative Analysis," Energies, MDPI, vol. 17(19), pages 1-11, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4994-:d:1493771
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    References listed on IDEAS

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