Genetic Algorithm for Optimal Charge Scheduling of Electric Vehicle Fleet

Abstract

Electric Vehicles (EV) are gradually conquering more roads and replacing pollutant conventional vehicles. They seem to be used to store energy in clean and smart grids to mitigate greenhouse gas emissions and eliminate harmful peak loads. This paper establishes a stochastic procedure for modeling and analyzing an electric vehicle (EV) fleet to generate an accurate charging and discharging profiles. For the purpose of managing the EV fleet we use a single-objective optimization, namely, the Genetic Algorithm (GA) to determine the optimal charging/discharging schedule for each EV in the fleet. The proposed optimization allows to make the optimal tradeoff between V2G and G2V operations cost to highly increase benefits from EV batteries by scheduling the charging mode in the low power price periods and discharging mode in the high-power price periods. Moreover, we compare our approach that considers the stochastic nature in the initial state-of-charge (SOC), arriving and departing times to the grid and the characteristic of EV battery packs for each connected EV in workplace and home parking lots, with a naive charging strategy.