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Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis

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  • Lyu, Lin
  • Yang, Xinran
  • Xiang, Yue
  • Liu, Junyong
  • Jawad, Shafqat
  • Deng, Runqi

Abstract

Electric vehicles (EVs) have received significant attention in recent years. The high penetration of EVs increased the charging load in the distribution network, which has an enormous impact on “transportation and power grid” coupled energy networks. This paper developed the models and methods to evaluate the capacity of electrical energy supply based on voltage stability in the worst charging case scenario, under the existing coupled network. An optimization method with transportation network constraints is proposed to find the worst charging case scenario. In addition, the mobility of EVs is considered to calculate the load margin and the maximum number of EVs charging that a given grid can support in the critical situation. The method and model are simulated by test cases, which provide a perspective for realizing the EVs integration capacity limitation in urban areas considering the coupling relationship between transportation and power grids. Finally, the impact of charging location and route choice in the voltage margin limitation is emphasized.

Suggested Citation

  • Lyu, Lin & Yang, Xinran & Xiang, Yue & Liu, Junyong & Jawad, Shafqat & Deng, Runqi, 2020. "Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304084
    DOI: 10.1016/j.energy.2020.117301
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    References listed on IDEAS

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    1. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
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    Cited by:

    1. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    2. Muhammad Usman & Wajahat Ullah Khan Tareen & Adil Amin & Haider Ali & Inam Bari & Muhammad Sajid & Mehdi Seyedmahmoudian & Alex Stojcevski & Anzar Mahmood & Saad Mekhilef, 2021. "A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization," Energies, MDPI, vol. 14(17), pages 1-16, August.
    3. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    4. Wei Chen & Lei Zheng & Hengjie Li & Xiping Pei, 2022. "An Assessment Method for the Impact of Electric Vehicle Participation in V2G on the Voltage Quality of the Distribution Network," Energies, MDPI, vol. 15(11), pages 1-14, June.
    5. Torres, S. & Durán, I. & Marulanda, A. & Pavas, A. & Quirós-Tortós, J., 2022. "Electric vehicles and power quality in low voltage networks: Real data analysis and modeling," Applied Energy, Elsevier, vol. 305(C).
    6. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).

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