Giant Trevally Optimization for Enhanced Vehicle-to-Grid and Grid-to-Vehicle Operations in EV Charging Stations

Implementation of the Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) capabilities in electric vehicle charging stations (EVCS) is crucial for supporting grid balance and improving the energy consumption. However, the conventional techniques of optimizing this problem are unlikely to accurately transfer energy to between the GRID and EVs, causing poor load regulation and high expenses in operation. In efforts to solve these challenges, this paper introduces the Giant Trevally Optimization (GTO) algorithm that ushers in efficiency in the V2G and G2V processes of the EV charging stations. Thus, the fundamental goal of the proposed method is to minimize charging time, strengthen the power stability and optimize the efficiency of the operation in the EV charging processes. The GTO algorithm is applied to control energy exchange in V2G and G2V transactions seeking to achieve efficient charging /discharging timetable in a manner that can prevent high costs as well as instabilities in the electric grid. By then, the proposed GTO approach in implemented on MATLAB platform and evaluate their performance with various existing methods include Whale Optimization Algorithm (WOA), Grey Wolf Optimization Algorithm (GWO), Fuzzy Logic-Based Optimization (FLO) and Teaching-Learning-Based Optimization (TLBO). The Gesturing Trade Off method is proposed in this work and it has an operating cost of $2628.12 conclusively showing that the proposed method is cost effective. Compared to the current optimization techniques, the proposed GTO method yields a settling time of 0.52s, a rising time of 0.25s, and marginal overshoot of only 0.8% meaning that the adaptation of the proposed GTO method can improve the efficiency and response time of adaptive V2G and G2V controllers of EVCS.