The introduction of electric vehicles on the power grid translates to operational challenges and opportunities of using these vehicles as distributed energy resources. This paper presents an algorithm for utilities to identify optimal scenarios for controlling electric load of plug-in electric vehicles on a neighborhood transformer, and their potential contribution to reducing overloading through controlled charging and feeding power back to the grid. A dataset of real-world driving data of 2800 vehicles in Canada is used to emulate realistic driving patterns for the simulations. The electric load of vehicles charging is estimated for different charging scenarios, battery capacities, and numbers of electric vehicle. Operational energy consumption data of single-family homes are used to predict realistic power profiles of a residential neighborhood. Different scenarios of controlling the electric load on a pole transformer are investigated for two seasons to account for ambient temperatures. By simulating the charging impacts of electric vehicles on a neighborhood transformer, the research presents a simplified control algorithm of optimal strategies to control and reduce transformer overloading. Results show that vehicle to-grid is an effective measure to reduce peak loads, while climate condition, vehicle penetration rate, and driving profile are the key factors to the control strategies. Crown Copyright (C) 2017 Published by Elsevier Ltd. All rights reserved.