Optimal Scheduling and Sizing of Energy Storage System using Hybrid Algorithm for Electric Vehicles

The rapid exhaustion of fossils shaded concern about environmental problems which enforce researchers to embed this renewable distribution generation into power system networks. Electric vehicles are increasing threat to reliability of power grid by overloaded equipment and disturbed stability in voltage. However, it is important that electric vehicles must provide support to grids through vehicle to grid optimized V2G scheduling to minimize load variances. The purpose of this study is to employ a hybrid approach to improve energy storage system scheduling and sizing for electric vehicles. The hybrid version of Flower Pollination and golden section search is used. This research considers peak shaving, valley filling and priority charging mode of Energy Storage System for effective load management. Results indicated that optimized and scheduled energy storage system can fulfill the proper load allocation at grid.