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Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks

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  • Shafqat Jawad

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Junyong Liu

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at different intervals. Therefore, the EV spatial–temporal approach for load mobility forecasting is presented in this article. Furthermore, the reliability indicators of large-scale EV distribution network penetration are analyzed. The Markov decision process (MDP) theory and Monte Carlo simulation are applied to efficiently forecast the charging load and stochastic path planning. A spatial–temporal model is established to robustly forecast the load demand, stochastic path planning, traffic conditions, and temperatures under different scenarios to evaluate the charging load mobility and EV drivers’ behavior. In addition, the distribution network performance indicators are explicitly evaluated. A Monte Carlo simulation is adopted to examine system stability considering various charging scenarios. Urban coupled traffic-distribution networks comprising 30-node transportation and 33-bus distribution networks are considered as a test case to illustrate the proposed study. The results analysis reveals that the proposed method can robustly estimate the charging load mobility. Furthermore, significant EV penetrations, weather, and traffic congestion further adversely affect the performance of the power system.

Suggested Citation

  • Shafqat Jawad & Junyong Liu, 2023. "Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks," Energies, MDPI, vol. 16(13), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5178-:d:1187393
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    References listed on IDEAS

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    1. Azhar Ul-Haq & Marium Azhar & Yousef Mahmoud & Aqib Perwaiz & Essam A. Al-Ammar, 2017. "Probabilistic Modeling of Electric Vehicle Charging Pattern Associated with Residential Load for Voltage Unbalance Assessment," Energies, MDPI, vol. 10(9), pages 1-18, September.
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    4. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    5. Guozhong Liu & Li Kang & Zeyu Luan & Jing Qiu & Fenglei Zheng, 2019. "Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs)," Energies, MDPI, vol. 12(13), pages 1-22, July.
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    Cited by:

    1. Chengyu Yang & Han Zhou & Ximing Chen & Jiejun Huang, 2024. "Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism," Energies, MDPI, vol. 17(9), pages 1-17, April.

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