Transportation has become one of the major contributors to emissions. Global concerns on emissions reduction have led to the drastic adoption of renewable sources and Electric Vehicles (EV). Stochastic nature is the predominant aspect of renewables for which energy storage is indispensable. The coordination of renewables and EVs storage will bring new opportunities in handling the stochastic nature of heterogeneous entities of Micro Grids (MG). This article proposes a novel EV fleet control strategy for MG Energy Management System (EMS). Here, reducing grid dependency and operating energy cost of the MG are the key objectives. A smart FUZZY based EV prioritization is developed. A probabilistic approach is developed to estimate the chance of EV usage for the smart decision on choosing EVs while supporting EMS. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. Optimal Energy Distribution (OED) is accomplished using Water Filling Algorithm (WFA). The proposed EMS is implemented in a real time on-grid MG scenario hosting solar Photovoltaic (Solar PV), EV fleet, and

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The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed.