LAUSR

This paper presents the energy management tool of a power system operating in a smart grid that contains electric vehicles. The intention of this work is to make a comparison between a metaheuristic optimization technique and two fuzzy logic controllers, and with that highlight the advantages of using fuzzy logic and validate it to the detriment of other metaheuristic techniques. The optimization technique used was simulated annealing, in order to minimize the total energy cost of the system being studied. Tree charging strategies were adopted: peak charging, off-peak charging, and smart charging besides demand-side management techniques. In addition to the charging process will also be studied the battery electric vehicles discharging, preferably at the peak of the load curve, through the creation of a charging/discharging station. In this work, the system used is the IEEE-39 bus New England power system. Two fuzzy logic controllers have been developed, namely the charging station controller and the vehicle-to-grid controller. Together they decide the proper energy flow between the EVs and the grid. Energy discharge to the grid from EVs or energy required for charging EVs is controlled and tested for the real-time scenario. The results proved the effectiveness of the proposed method in studies of planning and expansion of electric energy systems that contain electric vehicles.