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This paper describes load torque estimation (LTE) issue in induction motors with uncertainty, using dynamic sliding mode control (DSMC). In DSMC the chattering is removed due to the integrator which is placed before the input control of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system and then, the plant model should be completely known. To solve this problem, a new nonlinear observer called integral-chain observer (ICO) has been used. The advantage of the proposed approach is to have the system controlled as well as its main task i.e. LTE. Moreover, we assume that only output of system is accessible which is important in practical implementation. Simulation results are presented to demonstrate the approach.

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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