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Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load

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  • Liu, Ke
  • Liu, Yanli

Abstract

As the number of electric vehicles (EVs) connected to the grid increases, the EV electricity demand rises dramatically, affecting the grid’s planning and operation and deepening the coupling of the power and transportation systems. Therefore, accurate spatial–temporal distribution prediction of EV charging load is vital for both power system and coupled power-transportation system studies. This paper proposes a novel method based on stochastic user equilibrium (SUE) for predicting the accurate spatial–temporal distribution of EV charging load synchronized with traffic states. A prediction framework of EV charging load based on SUE and trip chain is proposed, which can effectively reflect the actual behavior of EVs in synchronous traffic states. Then, the extended logit-based SUE and equivalent mathematical model are proposed to obtain more detailed traffic states with intersection turning flows and delays. Meanwhile, the unified reachability and charging models are established to ensure that the trip chain is reachable and the charging characteristics are suitable for different EV types. Finally, the method of the successive averages (MSA) and the Dijkstra-based K-shortest paths algorithms are integrated to solve the proposed framework iteratively with stable convergence. Test results on a realistic traffic network show that the proposed method can effectively reflect the charging and trip characteristics of different EV types while ensuring reachability. And it can also accurately predict the overall and individual EV travel costs and total charging loads in detailed synchronous traffic states. In particular, even in the case of high EV penetration with higher peak-to-valley differences and charging demand, the convergence of the prediction is still stable with even more remarkable prediction effectiveness, especially during peak load hours. Furthermore, the quantitative analysis based on proposed criticality indexes reveals that traffic network failures will affect the network-wide traffic states and EV charging loads with different node-level impact characteristics, which should be considered in joint power-transportation restoration scheduling.

Suggested Citation

  • Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003070
    DOI: 10.1016/j.apenergy.2023.120943
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    as
    1. Yang, Tianyu & Guo, Qinglai & Xu, Luo & Sun, Hongbin, 2021. "Dynamic pricing for integrated energy-traffic systems from a cyber-physical-human perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    2. Yu, Qian & Fang, Debin & Du, Wei, 2014. "Solving the logit-based stochastic user equilibrium problem with elastic demand based on the extended traffic network model," European Journal of Operational Research, Elsevier, vol. 239(1), pages 112-118.
    3. Prashker, Joseph N. & Bekhor, Shlomo, 2000. "Some observations on stochastic user equilibrium and system optimum of traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 34(4), pages 277-291, May.
    4. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2022. "Integrated pricing strategy for coordinating load levels in coupled power and transportation networks," Applied Energy, Elsevier, vol. 307(C).
    5. Huang, Hai-Jun & Bell, Michael G. H., 1998. "A study on logit assignment which excludes all cyclic flows," Transportation Research Part B: Methodological, Elsevier, vol. 32(6), pages 401-412, August.
    6. Zhou, Yuekuan, 2022. "Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area," Applied Energy, Elsevier, vol. 318(C).
    7. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    8. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    9. Xu, Luo & Guo, Qinglai & Sheng, Yujie & Muyeen, S.M. & Sun, Hongbin, 2021. "On the resilience of modern power systems: A comprehensive review from the cyber-physical perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    10. Calearo, Lisa & Marinelli, Mattia & Ziras, Charalampos, 2021. "A review of data sources for electric vehicle integration studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    11. Cai, Sinan & Matsuhashi, Ryuji, 2022. "Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization," Applied Energy, Elsevier, vol. 319(C).
    12. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    13. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    14. Selten, R. & Chmura, T. & Pitz, T. & Kube, S. & Schreckenberg, M., 2007. "Commuters route choice behaviour," Games and Economic Behavior, Elsevier, vol. 58(2), pages 394-406, February.
    15. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Sun, Fengchun, 2021. "Data-driven framework for large-scale prediction of charging energy in electric vehicles," Applied Energy, Elsevier, vol. 282(PB).
    16. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    17. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    18. Zhou, Zhe & Zhang, Xuan & Guo, Qinglai & Sun, Hongbin, 2021. "Analyzing power and dynamic traffic flows in coupled power and transportation networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Mangipinto, Andrea & Lombardi, Francesco & Sanvito, Francesco Davide & Pavičević, Matija & Quoilin, Sylvain & Colombo, Emanuela, 2022. "Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries," Applied Energy, Elsevier, vol. 312(C).
    20. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    21. Maher, Mike, 1998. "Algorithms for logit-based stochastic user equilibrium assignment," Transportation Research Part B: Methodological, Elsevier, vol. 32(8), pages 539-549, November.
    22. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    23. Strobel, Leo & Schlund, Jonas & Pruckner, Marco, 2022. "Joint analysis of regional and national power system impacts of electric vehicles—A case study for Germany on the county level in 2030," Applied Energy, Elsevier, vol. 315(C).
    24. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    25. Bekhor, Shlomo & Toledo, Tomer, 2005. "Investigating path-based solution algorithms to the stochastic user equilibrium problem," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 279-295, March.
    26. Horowitz, Joel L., 1984. "The stability of stochastic equilibrium in a two-link transportation network," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 13-28, February.
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