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<p>Many electric vehicles (EVs) are being connected to the power grid. Large-scale charging increases the gap between peak and off-peak loads. A coordinated charge–discharge schedule is proposed for large-scale EVs in vehicle-to-grid (V2G) mode. The goal is to reduce load variance, peak–valley difference on the load curve, and the cost of network losses. EV power limits, grid supply limits, and load conditions are included as constraints. The multi-stage grouping competition swarm optimization (MGCSO) method is used to solve the model. Under the same test settings, MGCSO is compared with Harris Hawks Optimization (HHO), Tunicate Swarm Algorithm (TSA), and African Vultures Optimization Algorithm (AVOA). After optimization, the largest voltage swing is 5.08% for MGCSO. This value is lower than 7.89% for HHO, 8.05% for TSA, and 7.92% for AVOA. Annual network loss is reduced by 4.3% for MGCSO (from 8485&#xa0;MWh to 8122&#xa0;MWh). The reductions for HHO, TSA, and AVOA are 3.9%, 2.8%, and 3.5%, respectively. These results suggest that MGCSO is effective for large-scale EV scheduling.</p>

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Coordinated charge-discharge scheduling of large-scale EVs in V2G scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Songling Pang,
  • Meiyi Huo,
  • Kaidi Fan

摘要

Many electric vehicles (EVs) are being connected to the power grid. Large-scale charging increases the gap between peak and off-peak loads. A coordinated charge–discharge schedule is proposed for large-scale EVs in vehicle-to-grid (V2G) mode. The goal is to reduce load variance, peak–valley difference on the load curve, and the cost of network losses. EV power limits, grid supply limits, and load conditions are included as constraints. The multi-stage grouping competition swarm optimization (MGCSO) method is used to solve the model. Under the same test settings, MGCSO is compared with Harris Hawks Optimization (HHO), Tunicate Swarm Algorithm (TSA), and African Vultures Optimization Algorithm (AVOA). After optimization, the largest voltage swing is 5.08% for MGCSO. This value is lower than 7.89% for HHO, 8.05% for TSA, and 7.92% for AVOA. Annual network loss is reduced by 4.3% for MGCSO (from 8485 MWh to 8122 MWh). The reductions for HHO, TSA, and AVOA are 3.9%, 2.8%, and 3.5%, respectively. These results suggest that MGCSO is effective for large-scale EV scheduling.