Optimal Scheduling of Vehicle-To-Grid Power Exchange Using Hybrid PSO and GSA Approach

Electric vehicles (EVs) are set to revolutionize the transportation industry due to their benefits and incentives. They possess the unique characteristic of being a distributed energy storage device due to their onboard batteries. This function allows them to provide valuable ancillary services through vehicle-to-grid (V2G) operation, including load variance minimization on the grid. However, the issues of uncertain EV availability and ever-changing SOC present obstacles to practical V2G implementation. This paper proposes a scheduling method for V2G power exchange that addresses these challenges by examining the unpredictable nature of EV grid connectivity. In this paper, we suggest a novel technique for optimizing the scheduling of V2G power exchange utilizing particle swarm optimization (PSO) with gravitational search algorithm (GSA). This hybrid optimization algorithm of PSO with GSA has a faster convergence rate compared to using either PSO or GSA alone, making it a more effective method for optimizing the scheduling of V2G power exchange. The suggested V2G scheduling technique significantly flatter the load profile, according to the statistical evaluation of the information. Comparisons were made between the production of the suggested technique best-case phase and the accurate performance of approach suggested in earlier work on a related topic in order to concern the outcomes and validate the suggested approach.