Graph Attention Fusion Network for Electric Vehicle Charging Stations Management in Vehicle-to-Grid Systems

Vehicle-to-Grid (V2G) systems enable bidirectional energy flow between Electric Vehicles (EVs) and the grid, facilitating Energy Management (EM) and grid stability. Effective management of EV charging stations within V2G networks optimizes power distribution and supports renewable energy integration. However, high infrastructure costs associated with bidirectional chargers and grid upgrades pose financial challenges. Additionally, frequent charging and discharging cycles can lead to increased battery degradation, indirectly contributing to higher emissions from battery production and disposal. To overcome these drawbacks, this manuscript proposes a method for EV Charging Stations (EVCSs) in V2G systems. The proposed method is Graph Attention Fusion Network (GAF-Net). The main aim of the proposed method is to reduce the operational cost, charging time and emission of the system. The proposed GAF-Net predicts energy demand patterns and renewable energy generation, facilitating proactive decision-making for efficient management of EVCSs. Artificial Neural Network (ANN), Assailant Inspired Chimp Optimization Algorithm (AIChOa), and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) are some of the existing techniques that are compared with the proposed method once it is implemented in MATLAB. By achieving the lowest cost of 1504cents and the lowest emission of 60.4ppm, the proposed GAF-Net technique outperforms existing methods while ensuring cost-effective and efficient EVCS management in V2G systems integrating Photovoltaic (PV) systems and battery storage.