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Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends

Author

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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

The growing trend in electrical vehicle (EV) deployment has transformed independent power network and transportation network studies into highly congested interdependent network performance evaluations assessing their impact on power and transportation systems. Electrified transportation is highly capable of intensifying the interdependent correlations across charging service, transportation, and power networks. However, the evaluation of the complex coupled relationship across charging services, transportation, and power networks poses several challenges, including an impact on charging scheduling, traffic congestion, charging loads on the power grid, and high costs. Therefore, this article presents comparative survey analytics of large-scale EV integration’s impact on charging service network scheduling, transportation networks, and power networks. Moreover, price mechanism strategies to determine the charging fares, minimize investment profits, diminish traffic congestion, and reduce power distribution constraints under the influence of various factors were carried out. Additionally, the survey analysis stipulates the interdependent network performance index, ascertaining travel distance, route selection, long-term and short-term planning, and different infrastructure strategies. Finally, the limitations of the proposed study, potential research trends, and critical technologies are demonstrated for future inquiries.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3371-:d:378972
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    2. 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.
    3. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
    4. António Sérgio Faria & Tiago Soares & Tiago Sousa & Manuel A. Matos, 2020. "Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization," Energies, MDPI, vol. 13(16), pages 1-12, August.
    5. Nandini K. Krishnamurthy & Jayalakshmi N. Sabhahit & Vinay Kumar Jadoun & Dattatraya Narayan Gaonkar & Ashish Shrivastava & Vidya S. Rao & Ganesh Kudva, 2023. "Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method," Energies, MDPI, vol. 16(4), pages 1-27, February.
    6. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.
    7. Natascia Andrenacci & Mauro Di Monaco & Giuseppe Tomasso, 2022. "Influence of Battery Aging on the Operation of a Charging Infrastructure," Energies, MDPI, vol. 15(24), pages 1-18, December.

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