A queuing model with random interruptions for electric vehicle charging systems

We consider a queuing model with applications to electric vehicle (EV) charging systems in smart grids. We adopt a scheme where Electric Service Company (ESCo) broadcasts one bit signal to consumers indicating on-peak periods for the grid. EVs randomly suspend/resume charging based on the signal. To model the dynamics of the population of EVs we analyze an M/M/∞ queue with random interruptions, and propose estimates using time-scale decomposition. Using the estimates we show how ESCo can optimally adjust the indicator signal so as to minimize the mean number of charging EVs during the actual on-peak periods. Next we consider the case where EVs respond to the signal based on the individual loads. Simulation results show that performance is improved if the EVs carrying higher loads are less sensitive to the on-peak indicator signal.