Daily life and human development require the fatal and permanent presence of energy. To ensure the reliability and efficiency of the energy system and guarantee optimum performance of hybrid systems based on renewable energies, effective control based on efficient management techniques is required. Microgrids refer to any electrical installation containing a set of renewable energy sources of different types connected to the grid, together with an energy storage system, electric vehicles and AC and DC loads. Microgrids provide an ideal environment for creating, testing and applying an energy management system. This work presents a grid-connected energy management system composed mainly of three renewable energy sources: a photovoltaic (PV) system, a wind power system, a fuel cell, an energy storage system (ESS) and a diesel energy source. The main and most important role of the EMS is to ensure an efficient balance between the energy produced and the energy consumed by controlling in real time and continuously the different energy sources, including photovoltaic, wind, fuel cell, energy storage, and grid systems. The proposed Energy Management System (EMS) attempts to control the flow of energy via the two AC and DC buses in order to choose the best charging periods for the Energy Storage System (ESS), taking into consideration the use of supplementary renewable generation (PW, Wind and FC), and injecting into the grid the excess energy produced by the distributed sources (DG). Also, the proposed system contributes to minimizing electricity bills by charging batteries (ESS and VE) during periods of lowest tariff or through the production of renewable sources (PW, Wind and FC). This system uses control and conversion techniques to extract maximum power from the PV unit and wind system using a maximum power point tracking (MPPT) algorithm. PV, wind system and FC are sized to meet load demand, while ensuring battery charging in the event of excess energy. Matlab/Simulink is the numeric computing platform used to realize and test the energy management system proposed in this work. Moreover, the algorithm was tested under the following configurations: 20 kW photovoltaic system, 20 kW wind system, 20 kW fuel cell system and 35 kVA diesel system.