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In this paper, electric power production using airborne systems (kites) has been investigated. In the first step, an appropriate model is extracted to describe the behavior of airborne systems. Based on this model, a new path planning algorithm is proposed for the airborne system in the traction phase. Then, in order to achieve the proper operation, tracking the desired path and thus extracting optimal wind energy, a robust controller based on the sliding mode approach is designed in the presence of variations in atmospheric parameters and uncertainties in the system model. In the proposed method, the control strategy is obtained based on the speed vector angle of the airborne. In the proposed approach, six target points are used for the path designing of the kite motion in the traction phase, which increases the precision and flexibility of the designed path. Furthermore, the effect of adjusting the shape of the flight path of the airborne system during the traction phase on the system performance and extraction of the maximum wind force is also investigated.

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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