A hybrid optimization and graph network for sustainable electric vehicle charging using a dual active bridge converter and renewable energy

The Hybrid Renewable Energy (HRE) in Electric Vehicle (EV) charging involves combining power from Photovoltaic (PV), Fuel Cell (FC), and batteries, as well as charging batteries for upcoming charging cycles. However, power conversion efficiency and grid stability are required for reliable, smooth charging of EVs. The problems at hand necessitate a hybrid approach to the economic assessment of the EV Charging System (EVCS) implemented with a Dual Active Bridge (DAB) converter and Hybrid Renewable Energy Sources (HRES). The proposed method integrates the Pelican Optimization Algorithm (POA) and Attributed Multi-order Graph Convolutional Network (AMOGCN); therefore, it is called the POA-AMOGCN technique. The objective is to reduce the Levelized Cost of Energy (LCOE) in order to enhance the economic performance of the EVCS combined with HRES. The POA optimizes power management by balancing power generation from RES and battery storage with the demand of EVCS while generating control signals for the DAB converter. The AMOGCN predicts the optimal control signal for the DAB converter to enhance resource distribution, facilitating the integration of EVs with RES. Accordingly, the proposed method is implemented in the MATLAB environment and its performance is assessed against several existing approaches, including the Gorilla Troop Optimizer (GTO), Hybrid Crow Search Algorithm-Particle Swarm Optimization (HCSA-PSO), Neural Network with Improved PSO (NN-IPSO), Forensic Investigation Algorithm-Archimedean Optimization Algorithm (FBIAOA), and conventional PSO. The proposed POA-AMOGCN approach achieves the lowest LCOE of $0.0549/kWh, demonstrating a substantial improvement in cost-effectiveness compared to other optimization methods.