Optimized electric vehicle charging with intermittent renewable energy sources

Renewable energy and Electric Vehicles (EVs) are promising solutions for energy cost savings and emission reduction. However, integration of renewable energy sources into the electric grid could be a difficult task, because of the generation source intermittency and inconsistency with energy usage. In this paper, we present results of our study on the problem of allocating energy from renewable sources to EVs in a cost efficient manner. We have assumed that the renewable energy supply is time variant and in many ways unpredictable. EVs' charging requests should be satisfied within a specified time frame, which may incur a cost of drawing additional energy (possibly non-renewable energy) from the power grid if the renewable energy supply is not sufficient to meet the deadlines and may also reduce energy efficiency. We have formulated a stochastic optimization problem based on queuing model to minimize the time average cost of using non-renewable energy sources. The proposed approach fully considers the individual charging rate limit and deadline of each EV. The Lyapunov optimization technique is used to solve the problem. The developed dynamic control algorithm does not require knowledge of the statistical distribution of the time-varying renewable energy generation, EV charging demand, or extra energy pricing. Simulation results using different wind power generation profiles were performed and analyzed in the study. The results show that our EV charging scheduling method based on Lyapunov optimization can reduce both charging cost and mean delay time of fulfilling EV charging requests.