---

One of the solution for decreasing pollution in big cities is using of electric vehicles. Old combustion engine vehicles decreased Contamination of the environment. By using Hybrid Electric vehicles environmental pollution will reduce economically. One of these HEVs is plug-in HEV or PHEV. These types of vehicles are using electricity charger to charge their batteries from electricity distribution networks. So they impact on distribution transformer loading. Because many of vehicle owners start to charge their vehicles as arrive to home. In this paper assumed that vehicle owners can charge their vehicles at work and in home. This uncoordinated charging impact on distribution transformers daily aging and daily trip cost of owner vehicles are simulated based on stochastic modeling of PHEVs owner behavior at any day. Results of this topic on IEEE RBTS distribution network are presented.

---

Problems with the cost and pollution of fossil fuels have increased the incentive to operate on electric vehicles. However, the use of these vehicles is a challenge due to the additional loads which imposed on the power grid. Accordingly, a method has been proposed to improve the electrical parameters of the network including losses and voltage profiles by optimally managing the charge and discharge of plug in hybrid electric vehicles (PHEVs). The optimal management of the present paper involves the simultaneous management of active and reactive power of PHEVs. In order to implement the optimal management, in this paper, the probabilistic behavior of consumers and PHEVs are modeled on the factors affecting them. Regarding the multiplicity of factors considered and the non-convergence of the problem by conventional methods of optimization, a two-stage optimization method is proposed which provides the ability to achieve the desired goals by managing active and reactive power of PHEVs. The advantages of the proposed method can be to reduce the computational volume with respect to problem solving in each step of the time independently and thus reduce the optimization problem solving time. The proposed method is implemented by performing Monte Carlo repetitions on six power management scenarios implemented by GAMS and DIgSILENT software on real network of 20 kV distribution of Sirjan in Kerman province. The results of various scenarios show that the management of charge and discharge of PHEVs has smooth the voltage profile and reduced network losses. Therefore, using the proposed method, the additional loads imposed by the electric vehicle on the grid will not only increase the energy losses, but, with the proper management of the PHEVs, the network losses will be reduced compared to the absence of them.