A Hybrid Optimization Approach for PV-BESS Integration in Grid-Tied EV Charging System

This study introduces a hybrid optimization approach for optimizing the integration of PV systems and BESS to grid-tied EV charging stations to improve grid performance and sustainability. The proposed hybrid GWO-SA uses Grey Wolf Optimizer (GWO) and Simulated Annealing (SA) algorithms to optimize the location and size of PV units simultaneously and BESS, addressing PV output variability, EV load uncertainty, and peak load management. The proposed method considers the optimization of multi-objectives, including power loss and peak load minimization, voltage profile enhancement, and maximizing renewable energy utilization to facilitate EV charging needs and enhance the overall integrated system performance. The proposed method is validated on the IEEE 33-bus system under different power penetration levels, EV charging loads, and BESS configurations. Results show a significant power loss reduction of up to 53 % and an improvement in voltage stability from 0.903 pu to 1 pu. Moreover, peak demand is effectively managed, which indicates that the proposed framework reduces the load burden on the main grid during high-demand events. The validation and the comparison of the proposed GWO-SA algorithm demonstrated that the EV charging in the daytime aligned with PV generation proved the most efficient, reducing reliance on grid supply and robust convergence, while optimal-sized BESS achieved an optimal balance between EV charging stance and distribution network performance.