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Consideration of Wind-Solar Uncertainty and V2G Mode of Electric Vehicles in Bi-Level Optimization Scheduling of Microgrids

Author

Listed:
  • Zezhou Chang

    (State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China)

  • Xinyuan Liu

    (State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China)

  • Qian Zhang

    (State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China)

  • Ying Zhang

    (State Grid Shanxi Electric Power Company Electric Power Research Institute, Taiyuan 030001, China)

  • Ziren Wang

    (Department of Economics and Management, North China Electric Power University, Baoding 071000, China)

  • Yuyuan Zhang

    (Department of Economics and Management, North China Electric Power University, Baoding 071000, China)

  • Wei Li

    (Department of Economics and Management, North China Electric Power University, Baoding 071000, China)

Abstract

In recent years, the global electric vehicle (EV) sector has experienced rapid growth, resulting in major load variations in microgrids due to uncontrolled charging behaviors. Simultaneously, the unpredictable nature of distributed energy output complicates effective integration, leading to frequent limitations on wind and solar energy utilization. The combined integration of distributed energy sources with electric vehicles introduces both opportunities and challenges for microgrid scheduling; however, relevant research to inform practical applications is currently insufficient. This paper tackles these issues by first introducing a method for generating typical wind–solar output scenarios through kernel density estimation and a combination strategy using Frank copula functions, accounting for the complementary traits and uncertainties of wind and solar energy. Building on these typical scenarios, a two-level optimization model for a microgrid is created, integrating demand response and vehicle-to-grid (V2G) interactions of electric vehicles. The model’s upper level aims to minimize operational and environmental costs, while the lower level seeks to reduce the total energy expenses of electric vehicles. Simulation results demonstrate that this optimization model improves the economic efficiency of the microgrid system, fosters regulated EV electricity consumption, and mitigates load variations, thus ensuring stable microgrid operation.

Suggested Citation

  • Zezhou Chang & Xinyuan Liu & Qian Zhang & Ying Zhang & Ziren Wang & Yuyuan Zhang & Wei Li, 2025. "Consideration of Wind-Solar Uncertainty and V2G Mode of Electric Vehicles in Bi-Level Optimization Scheduling of Microgrids," Energies, MDPI, vol. 18(4), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:823-:d:1588087
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    References listed on IDEAS

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    1. Huo, Yuchong & Bouffard, François & Joós, Géza, 2021. "Decision tree-based optimization for flexibility management for sustainable energy microgrids," Applied Energy, Elsevier, vol. 290(C).
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    3. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
    4. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
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