Customer and aggregator balanced dynamic Electric Vehicle charge scheduling in a smart grid framework

Electric Vehicles (EVs) are already an integral part of the smart grid framework. The problem of scheduling the charging of these plug-in vehicles in coordination with the dynamic electricity tariffs and load-changes throughout the day is important and must be considered for efficient integration of such units. Since EVs are active and distributed storage units, they have the capability to provide regulation services to the power grid and hence can be used in schemes where the aggregator can compensate EVs for this service. The EV charging must also be scheduled in such a way that the customer or the vehicle owner incurs the least cost and the aggregator also makes the maximum revenue. The work presented in this paper attempts to satisfy the economy of both the EV owner and the aggregator by mathematically formulating this scheduling problem and solving it by different optimization techniques to arrive at the optimal cost and revenue for the customer and the aggregator respectively. Different heuristic techniques based evolutionary optimization algorithms have been applied to solve the optimal charge scheduling problem and the results compared and analyzed for assessing practical feasibility.