Implementation of vehicle to grid concept using ANN and ANFIS controller

As the number of Electric Vehicles [EVs] is increasing, the problem with the charging and discharging of these EVs is also increased. Charging of EVs in bulk will cause overloading with high peaks in the load profile. To overcome this we propose Vehicle to Grid [V2G] concept, which allows us to communicate with electric grid and Electric Vehicles [EVs]. Also it allows us to check and control the flow of power. In this paper we took EVs batteries to support grid during over loading and try to shave peaks in the load profile when the demand is high and allow them to charge when the electric grid is lightly loaded and try to fill the valley occur in the load profile during this period. We have proposed Artificial Neural Network and ANFIS Adaptive Neural Fuzzy Inference System controllers to control the power flow between electric grid and EVs batteries. The performance of these two controllers with the Fuzzy Logic Controller [FLC] has been compared and presented in this paper.