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Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

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  • Haihong Bian

    (School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Quance Ren

    (School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Zhengyang Guo

    (School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China
    Hangzhou Power Supply Company of State Grid Zhejiang Electricity Power Co., Ltd., Hangzhou 310016, China)

  • Chengang Zhou

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Zhiyuan Zhang

    (School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Ximeng Wang

    (School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

Abstract

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

Suggested Citation

  • Haihong Bian & Quance Ren & Zhengyang Guo & Chengang Zhou & Zhiyuan Zhang & Ximeng Wang, 2024. "Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation," Energies, MDPI, vol. 17(11), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2606-:d:1403865
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    References listed on IDEAS

    as
    1. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    2. Sagar Hossain & Md. Rokonuzzaman & Kazi Sajedur Rahman & A. K. M. Ahasan Habib & Wen-Shan Tan & Md Mahmud & Shahariar Chowdhury & Sittiporn Channumsin, 2023. "Grid-Vehicle-Grid (G2V2G) Efficient Power Transmission: An Overview of Concept, Operations, Benefits, Concerns, and Future Challenges," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    3. Qian Wang & Xiaolong Yang & Xiaoyu Yu & Jingwen Yun & Jinbo Zhang, 2023. "Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
    4. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).
    5. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    6. Yang, Liya & Shen, Qing & Li, Zhibin, 2016. "Comparing travel mode and trip chain choices between holidays and weekdays," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 273-285.
    7. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
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