Optimal scheduling of vehicle-to-Grid power exchange using particle swarm optimization technique

Electric vehicles (EV) are the inevitable future of the transportation industry considering the current energy scenario and all the incentives and benefits they provide. Nonetheless, EVs exhibit a novel characteristic of a distributed energy storage device (ESD) owing to their large onboard batteries. This feature of EV can be utilized to provide many ancillary services by the virtue of vehicle-to-grid (V2G) operation. One such service is the minimization of load variation on the grid. Two key challenges, concerning the practical execution of the V2G operation, are the availability of EVs for V2G operation and their ever-changing State of Charge (SOC). Due to the mobility of EVs as a transportation medium, their availability is highly uncertain. This paper addresses these issues by introducing a scheduling scheme for V2G power exchange by considering the stochastic nature of EV grid connectivity. This is done by first developing an optimization algorithm using the particle swarm optimization technique, with input data that best represents the stochastic nature of EV availability. Then, the performance of the algorithm is evaluated by conducting several case studies. The results obtained for various case studies by performing simulations are represented and elaborated. Finally, the statistical analysis of the results signifies that the proposed V2G scheduling scheme can substantially flatten the load profile. Apart from the algorithm itself, another novel nature of the paper lies within the wide range of analyses carried out to study the effect of such scheduling schemes. These studies include the effect on the SOC level of different EVs, the effect on price fluctuation, time complexity analysis of the algorithm, etc.