A robust optimization method for optimizing day-ahead operation of the electric vehicles aggregator

Due to consistently growing electric vehicles (EVs) penetration energy demand of the power grids is increasing all over the world. In near future a proper coordination system of several EVs with the main grid is necessary to be developed. This system will mainly deal with the uncertain charging and discharging patterns of each associated EV to stabilize the grid. By deploying the vehicle to grid (V2G) framework excess energy stored in the EV battery could be supplied back to the grid in very controlled manners. In order to motivate EV owners to take part in the V2G operation this paper proposes a profit enhancing EV-aggregation system which contributes to the Real Time and Day-ahead (DA) energy markets to minimize the energy consumption cost while enhancing the profits of both EV owners and energy suppliers. This framework determines the technical characteristics and uncertain driving patterns of each associated EV. A multi stage hierarchical optimization model is proposed to regulate the decision making process for effective EVs aggregation. The upper stage optimizes the profit maximizing aggregation decisions for smoothening the EVs charging and discharging operation, while a sequence of lower stage constraints deals with the contingencies i.e., worst EVs State of Charge (SOC) and reduced energy trading with the market. For robust operation in last, these two layers are conjointly transformed into a mixedintegral linear uni-lateral equivalence model which provides a completely uni-modular characteristic of the lower stage problem matrices which convexifies. In last, we have extensively analyzed the performance of the proposed hierarchical method with the pre-established stochastic and optimization models as well as comparison with the state of the art techniques proves its effectiveness.