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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 this paper, a new adaptive model predictive control based on Laguerre functions is proposed for the load-frequency control problem of a multi-area power system, in which the estimation of the internal model of the power system is updated online using the recursive least squares method. The use of the adaptive reduced-order internal model in the structure of model predictive control is the innovation of this research. In the studied system, the controller of each area is designed independently so that the stability of the overall closed-loop system is guaranteed. Numerical simulations for a three-area power system are carried out to validate the effectiveness of the proposed scheme and the results were compared with those of conventional model predictive control (MPC) and proportional-integral-derivative control (PID). The simulation results show that the proposed scheme performs better than PID and MPC in rejecting step load disturbance (with respect to nominal and uncertain parameters) and nevertheless, thanks to the use of the reduced-order model and Laguerre functions, reduces the computational burden significantly compared to conventional MPC.