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The very low cost of renewable energy resources and the increase of the greenhouse gas emissions and fuel cost have led to a simultaneous increase in utilizing the renewable energy resources (RESs) and electric vehicles (EVs). In this paper, a mixed-integer linear programming (MILP) model is proposed for the stochastic unit commitment problem with the aim of minimizing the operation cost and the emission in the presence of EVs and RESs. EVs with the capability of vehicle-to-grid (V2G) can operate as energy storage units in the smart grid and, if necessary, be connected to the network as generation resources. In this paper, an aggregator is responsible for coordinating the charging and discharging of EVs. The RESs uncertainties has complicated the management of electric vehicles and the unit commitment problem. Therefore, in this paper, Monte Carlo simulation method is used for modeling the uncertainties of the wind and solar power and the load demand. The simulation results show that the simultaneous utilization of the proposed MILP model and the probability distance method for reducing the number of scenarios, can minimize the operation cost of thermal units and pollutant emissions while reduces the solution time, significantly

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In order to reduce the cost of electric power generation and also reduce greenhouse gas emissions in ships with electric propulsion system, renewable energy resources and energy storage systems are used along with thermal units. Therefore, in this paper, a stochastic mixed integer linear model has been suggested for optimal management of electrical energy of a ship with electric propulsion system, energy storage, heat generators and renewable solar resources with the aim of minimizing the cost of electricity production and determining the optimal ship speed. In this paper, the Monte Carlo simulation method is used to model the uncertainty in predicting the solar power and the ship's electric load. The proposed model is implemented and analyzed in GAMS optimization software. The simulation results show the efficiency of the proposed model and the introduced energy management strategy.