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<p>The growing adoption of electric vehicles (EVs) significantly impacts the stability of low-voltage (LV) distribution systems due to increased load demands. Large-scale deployment of EV charging infrastructure, particularly fast charging stations (EVCS), can lead to substantial grid congestion and voltage fluctuations, threatening grid reliability. Therefore, grid stability enhancement while ensuring the grid-friendly integration of EVCS is crucial. This manuscript proposes a novel hybrid method called CLOA-HMPGNN, which combines the clouded leopard optimization algorithm (CLOA) and hierarchical message passing graph neural network (HMPGNN) to enhance grid stability. The CLOA minimizes voltage fluctuations in the grid, while the HMPGNN regulates the charging power of EVs, achieving optimal power distribution and maintaining grid stability. The proposed CLOA-HMPGNN approach is implemented in MATLAB and evaluated against existing methods. Results demonstrate that the proposed method outperforms achieving lower error rates and higher efficiency in minimizing voltage fluctuations and regulating EV charging power. Consequently, CLOA-HMPGNN proves to be an effective solution for grid-friendly integration of EVCS, ensuring reliable and stable power distribution in modern electrical grids.</p>

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Enhancing grid stability through CLOA-HMPGNN-based electric vehicle charging system

  • 期刊论文

  • 发表日期：

    2025年9月16日

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  • Dhinesh Vasudevan,
  • Vijayakumar Govindasamy,
  • Mohamedyaseen Abdulsalamazad,
  • Vijayakumar Singaram

摘要

The growing adoption of electric vehicles (EVs) significantly impacts the stability of low-voltage (LV) distribution systems due to increased load demands. Large-scale deployment of EV charging infrastructure, particularly fast charging stations (EVCS), can lead to substantial grid congestion and voltage fluctuations, threatening grid reliability. Therefore, grid stability enhancement while ensuring the grid-friendly integration of EVCS is crucial. This manuscript proposes a novel hybrid method called CLOA-HMPGNN, which combines the clouded leopard optimization algorithm (CLOA) and hierarchical message passing graph neural network (HMPGNN) to enhance grid stability. The CLOA minimizes voltage fluctuations in the grid, while the HMPGNN regulates the charging power of EVs, achieving optimal power distribution and maintaining grid stability. The proposed CLOA-HMPGNN approach is implemented in MATLAB and evaluated against existing methods. Results demonstrate that the proposed method outperforms achieving lower error rates and higher efficiency in minimizing voltage fluctuations and regulating EV charging power. Consequently, CLOA-HMPGNN proves to be an effective solution for grid-friendly integration of EVCS, ensuring reliable and stable power distribution in modern electrical grids.