Uncertainty handling for Electric Vehicle aggregator using IGDT

Electric vehicles are an integral part of futuristic smart grids. Electric vehicles give rise to a new player in the retail market called as aggregator. This paper proposes an intelligent charging scheduling problem for an Electric Vehicle (EV) aggregator considering vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capabilities with an objective to minimize the total charging cost. Since electricity price at the charging node may be subject to uncertainties, Information Gap Decision Theory (IGDT) is proposed in this paper to handle uncertainties in the price. The original intelligent charging scheduling problem is non-linear. The paper proposes a modified Mixed Integer Linear Programming (MILP) based reformulation and solves with CPLEX using GAMS as an aggregator.