Abstarct: Nowadays, the energy management is an important issue in the smart grid. The energy management decides to manage both energy production and energy consumption. This thesis addresses the energy management in smart microgrids with inclusion of electric vehicles (EVs). In this study, the energy management is undertaken from the smart microgrid owner point of view to optimize the corresponding objective function. In this thesis, firstly, the energy scheduling of the EVs is presented to manage the charging/discharging time intervals. In the next stage, the energy management model is developed for a smart microgrid including DG units, renewable energy resources and EVs as a mixed-integer linear programming (MILP) problem. To address uncertainties, the energy management is modeled by a two-stage stochastic programming problem. Then, the concept of energy hub is used in the energy management of a micro energy grid (MEG). In this regard, a two-stage stochastic MILP model is developed for the energy scheduling of an energy hub. A set of scenarios is considered for the uncertainties of loads and renewable energy resources. Finally, the impact of electricity price uncertainty on the MEG operation is investigated (considering DG units and EVs), to maximize the operating profit of the energy network. In this stage, the Monte Carlo simulation technique is employed to address the price uncertainty. In addition, in order to quantify the risk of operating profit variability, the coefficient of variation (CoV) criterion is utilized. All of the proposed models and methods are simulated on several test systems to verify the capability of the proposed methods.