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The increasing adoption of electric vehicles (EVs) due to technical advancements and environmental concerns requires wide deployment of public charging stations (CSs). In order to accelerate the EV penetration and predict the future CSs requirements and adopting proper policies for their deployment, studying the charging behavior of EV drivers is inevitable. This paper introduces a stochastic model that takes into the consideration the behavioral characteristics of EV drivers in particular in terms of their reaction to the EV battery charge level when deciding to charge or disconnect at a CS. The proposed model is applied in two case studies to describe the resultant collective behavior of EV drivers in a community using real field EV data obtained from a major North American campus network and part of London urban area. The model fits well to the datasets by tuning the model parameters. The sensitivity analysis of the model indicates that changes in the behavioral parameters affect the statistical characteristics of charging duration, vehicle connection time and EV demand profile, which has a substantial effect on congestion status in CSs. This proposed model is then applied in several scenarios to simulate the congestion status in public parking lots and predict the future charging points needed to guarantee the appropriate level of service quality. The results show that studying and controlling the EV drivers' behavior leads to a significant saving in CS capacity and results in consumer satisfaction, thus, profitability of the station owners. Index Terms-Electric Vehicle (EV), characteristic modeling, congestion control, electric vehicle charging. NOMENCLATURE CP Charging Point. CS Charging Station. M CS Monte Carlo Simulation. M DP Markov Decision Process. c i The state of the EV connection to the CP in Markov chain with i% level of charge. d i The state of the EV disconnection from the CP in Markov chain with i% level of charge. p iThe probability of EV driver's decision to disconnect with i% EV's level of charge, defined by logistic function. q iThe probability of EV driver's decision to connect while the EV is moving with i% EV's level of charge, defined by logistic function.tThe probability of EV parking in time t when the EV is disconnected from CP.

Demand Side Management (DSM) will be one of the most important parts of future smart power grid. The DSM algorithms help consumers to be more active contributors in the power system in order to achieve system objectives by scheduling their shiftable load. In this paper, we review the challenges in this area of research by categorizing the DSM problems into four important categories. The load scheduling can be done using a proper two-way communication network which has its own challenges in the smart grid. The important issues of security and privacy are considerable in every communication network and consequently in DSM network system. On the other hand, successful DSM programs need consumers' contribution in the system which can be achieved in a fair system. Recently, there are some works on the fairness of the DSM algorithms with different definition for the fair system.