Fog-Computing-Based Energy Storage in Smart Grid: A Cut-Off Priority Queuing Model for Plug-In Electrified Vehicle Charging

Abstract

Electric vehicles (EVs) are likely to become very popular within the next few years. With possibly millions of such vehicles operating across the smart cities, smart grid energy providers can be directly impacted by the charging of EV batteries. In order to reduce this impact and optimize energy saving, in this article, 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 in order to reduce the completion and communication delay of EV energy demand scheduling. To enhance the scheduling of EV 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 multipriority 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 EV public supply station. To the best of our knowledge, this is the first article that proposes a model that tries to save energy by planning the plug-in of EVs using a cut-off priority queuing model and a decentralized Fog architecture. We evaluate the performances of our solution via extensive simulations using a realistic energy loads from the city of Toronto, and we compare it with other recent works.