Fog Computing Based Energy Storage in Smart Grid: A Cut-off Priority Queuing Model for plug-in Electrified Vehicles Charging
Abstract
Electric vehicles (EVs) are likely to become very popular within the next few years. With possibly millions of such vehicles operating across smart cities, the charging of EVs batteries can directly impact smart grid energy providers. In order to reduce this impact and optimize energy saving, in this paper, we propose a coordinated model for scheduling the plug-in of EVs for charging and discharging energy. The model is based on a new decentralized Fog architecture for smart grid environment. To enhance the scheduling of EVs demands and predict the future energy flows, we propose a plug-in system of EVs based on calendar planning. We develop a mathematical formalism based on Markov chains using a multi-priority queuing theory with cut-off discipline in order to reduce the waiting time to plug-in. We implement three planning algorithms in order to assign priority levels, and then optimize the plug-in time into each electric vehicles public supply station (EVPSS). To the best of our knowledge, this is one of the first papers proposing decentralized Fog architecture for energy saving by planning the plug-in of EVs. We evaluate the performance of our solution via extensive simulations using a realistic energy loads from the city of Toronto, and we compare it with other recent works.