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A Two-Stage Scheduling Strategy for Electric Vehicles Based on Model Predictive Control

Author

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  • Wen Wang

    (State Grid Smart Internet of Vehicles Co., Ltd., Beijing 100052, China)

  • Jiaqi Chen

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yi Pan

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Ye Yang

    (State Grid Smart Internet of Vehicles Co., Ltd., Beijing 100052, China)

  • Junjie Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs’ uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations.

Suggested Citation

  • Wen Wang & Jiaqi Chen & Yi Pan & Ye Yang & Junjie Hu, 2023. "A Two-Stage Scheduling Strategy for Electric Vehicles Based on Model Predictive Control," Energies, MDPI, vol. 16(23), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7737-:d:1286460
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    References listed on IDEAS

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    1. Prasannaa Poongavanam & Aneesh A. Chand & Van Ba Tai & Yash Munnalal Gupta & Madhan Kuppusamy & Joshuva Arockia Dhanraj & Karthikeyan Velmurugan & Rajasekar Rajagopal & Tholkappiyan Ramachandran & Kus, 2023. "Annual Thermal Management of the Photovoltaic Module to Enhance Electrical Power and Efficiency Using Heat Batteries," Energies, MDPI, vol. 16(10), pages 1-18, May.
    2. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
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    Cited by:

    1. Muhammed Cavus & Dilum Dissanayake & Margaret Bell, 2025. "Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management," Energies, MDPI, vol. 18(5), pages 1-41, February.

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