Vehicle-to-grid (V2G) describes a system ill which plug-in electric vehicles (PEV), which includes all electric vehicles and plug-in hybrid electric vehicles, utilize power by plugging into an electric power source and stored in rechargeable battery packs. PEVs significantly increase the load on the grid, much more than you would see in a typical household. The objective of this paper is to demonstrate the use of intelligent solutions for monitoring and controlling the electrical grid when connected to and recharging PEV batteries. In order to achieve this aim, the study examines the distribution of electricity in the power grid of a large-scale city so that PEVs can tap into the system using smart grid electricity. The electricity grid for the large-scale city is modelled, and it can be shown that the vehicle electrification can play a major role in helping to stabilize voltage and load. This developed grid model includes 33 buses, 10 generators, 3 reactors, 6 capacitors, and 33 consumer centers. In addition, the grid model proposes 10 parking servicing 150,000 vehicles per day. The smart grid model uses intelligent controllers. Two intelligent controllers including (i) fuzzy load controllers and (ii) fuzzy voltage controllers have been used in this study to optimize the grid stability of load and voltage. The results show that the smart grid model can respond to any load disturbance in less time, with increased efficiency and improved reliability compared to the traditional grid. In conclusion it is emphasized that smart grid electricity should contribute to PEVs accessing renewable energy. Although the V2G will play a major role in the future portfolio of vehicle technologies, but does not make much sense if the carbon content of the electricity generated by the grid will not be reduced. Thus, the recourse to renewable energy and other alternatives is crucial. The energy is stored in electrochemical power sources (such as battery, fuel cells, supercapacitors, photoelectrochemical) when generated and then delivered to the grid during peak demand times. (C) 2011 Elsevier B.V. All rights reserved.