An Adaptive Charging Algorithm for Electric Vehicles in Smart Grids

Integration of renewable energy sources and Electric Vehicles (EVs) into smart grids comes with significant challenges. The uncertainty of the short-term forecasted energy from renewable sources increases the variability of the net-load in the grid. Also, EVs´ charging could exacerbate the load peak in the grid if charging is not coordinated. In this work, firstly, we study the impact of the variability of renewable sources on the short-term forecast of the net-load in the electric grid, and a model of the net-load forecast error is developed. Secondly, a novel online charging algorithm for EVs is proposed not only to shift EVs´ load from the system peak period to more desirable period, but also to decrease the variability of the net-load in the grid. Simulation results show that our algorithm outperforms the traditional scheduling algorithms which optimize the overall load in the system based on short-term load forecast.