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<p>Current research on balancing grid stability and battery deterioration to maintain consistent electricity delivery is insufficient, and there is an urgent need for optimal control measures. This research introduces an innovative hybrid algorithm that uses Vehicle-To-Grid (V2G) technology to smoothly integrate electric vehicles into the smart grid via a network of charging stations. The main objective is to enhance the stability and reliability of the grid and incorporate cost considerations such as battery degradation and energy efficiency. The exponential gannet red panda optimization algorithm and position-aware subgraph neural networks are combined in the hybrid algorithm. Using data acquired from the smart network, the system determines the charging/discharging process during periods of instability/peak demand, and ensures bi-directional energy flow control with better voltage and frequency regulation. To verify the algorithm's resilience, the MATLAB framework with IEEE 118 and 300 bus-test node feeder power distribution system, coupled with charging stations, is analysed. Analysed policy implications include time-of-use pricing, V2G feed-in tariffs, and performance-based incentives. By avoiding battery degradation and guaranteeing dependable energy delivery, the suggested strategy successfully strikes a balance between grid stability and Electric Vehicle (EV) owner requirements, which ensured grid reliability of 90% and 25% cost savings.</p>

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Optimal integration of electric vehicle into smart grid: a fusion procedure for grid reliability, stability and cost reduction

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • K. Suresh,
  • Sivakumar Thankaraj Ambujam,
  • M. Mary Linda,
  • M. Thamil Alagan

摘要

Current research on balancing grid stability and battery deterioration to maintain consistent electricity delivery is insufficient, and there is an urgent need for optimal control measures. This research introduces an innovative hybrid algorithm that uses Vehicle-To-Grid (V2G) technology to smoothly integrate electric vehicles into the smart grid via a network of charging stations. The main objective is to enhance the stability and reliability of the grid and incorporate cost considerations such as battery degradation and energy efficiency. The exponential gannet red panda optimization algorithm and position-aware subgraph neural networks are combined in the hybrid algorithm. Using data acquired from the smart network, the system determines the charging/discharging process during periods of instability/peak demand, and ensures bi-directional energy flow control with better voltage and frequency regulation. To verify the algorithm's resilience, the MATLAB framework with IEEE 118 and 300 bus-test node feeder power distribution system, coupled with charging stations, is analysed. Analysed policy implications include time-of-use pricing, V2G feed-in tariffs, and performance-based incentives. By avoiding battery degradation and guaranteeing dependable energy delivery, the suggested strategy successfully strikes a balance between grid stability and Electric Vehicle (EV) owner requirements, which ensured grid reliability of 90% and 25% cost savings.