With the widespread penetration of plug-in hybrid electric vehicles (PHEVs), the overall demand on micro-grids (MGs) may increase manifold in the near future. Unregulated power demands from PHEVs may increase the demand-supply gap at MG. Thus, in order to keep MGs stabilize, and cater the ever growing energy demands, there is a requirement of an intelligent solution to regulate, and manage PHEVs in vehicle-togrid (V2G) environment. Keeping in view the above issues, this paper proposes a novel scheme which aims to regulate PHEVs? charging, and discharging activities based on MGs? day-ahead load curves. These load curves are obtained by utilizing the existing load forecasting techniques such as-fuzzy logic (FL), and artificial neural networks (ANN). Efficient utilization of PHEVs according to these curves may play a vital role in flattening MG?s load profile. Thus, the proposed scheme works by reserving resources such as-time slots, and charging points for PHEVs during peak shaving, and valley filling. Different algorithms pertaining to resource reservation for PHEVs have also been designed. These algorithms employ the concepts of game theory, and 0/1 knapsack problem for supporting peak shaving, and valley filling respectively. Moreover, PHEVs are also utilized when there are transitions from valley filling to peak shaving areas in the load curves, and vice-versa. PHEVs involved in this process have both charging, and discharging capabilities, and are referred as dual-mode PHEVs. The proposed scheme has been tested with respect to various parameters, and its performance was found satisfactory. © 2015 IEEE