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<p>A strong randomness of electric vehicle (EV) charging causes fluctuations in the real-time load of the power grid, affecting the safe, stable and economic operation of the distribution network. Potential low-probability extreme load scenarios will also affect the safe operation of the power grid. This study developed a method to identify and analyze the potential weak nodes of the electric vehicle distribution network under a low-probability extreme load scenario. The new method identified the probability of realistic extreme load scenarios using a mult-node load generation model. First, a trip chain model that accounts for the correlation between destination types and time is established, and typical and extreme charging load scenarios are then presented. Second, generate multi-node conventional load based on conditional generative adversarial network, and filter out typical and extreme conventional load scenarios. Finally, potential low-probability extreme load scenarios are used to evaluate the voltage stability of the distribution network. Results show that this method can effectively reflect the spatial-temporal characteristics of EV charging load and the spatial correlation of conventional loads. It can also effectively identify weak nodes in the distribution network, which is beneficial for planning future distribution network expansions.</p>

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Voltage Stability Analysis Of Distribution Network with Electric Vehicle Based on Multi-Node Load Generation Model

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Nantian Huang,
  • Yaoyao Wang,
  • Jinghan Wu,
  • Bingling Li,
  • Guowei Cai,
  • Liang Zhang

摘要

A strong randomness of electric vehicle (EV) charging causes fluctuations in the real-time load of the power grid, affecting the safe, stable and economic operation of the distribution network. Potential low-probability extreme load scenarios will also affect the safe operation of the power grid. This study developed a method to identify and analyze the potential weak nodes of the electric vehicle distribution network under a low-probability extreme load scenario. The new method identified the probability of realistic extreme load scenarios using a mult-node load generation model. First, a trip chain model that accounts for the correlation between destination types and time is established, and typical and extreme charging load scenarios are then presented. Second, generate multi-node conventional load based on conditional generative adversarial network, and filter out typical and extreme conventional load scenarios. Finally, potential low-probability extreme load scenarios are used to evaluate the voltage stability of the distribution network. Results show that this method can effectively reflect the spatial-temporal characteristics of EV charging load and the spatial correlation of conventional loads. It can also effectively identify weak nodes in the distribution network, which is beneficial for planning future distribution network expansions.