Placement of bidirectional electric vehicle charging stations in power systems using optimization algorithms

As a result of the dependence on fossil fuels, the reduction of resources and the damage caused to nature by greenhouse gas emissions from fossil fuels have played an important role in the transition to electric vehicles. The widespread use of electric vehicles and requirement for charging power cause consequences that will negatively affect power systems such as increase in energy demand and peak loads. On the other hand, bidirectional electric vehicle charging stations (BEVCS) technology can support the electricity grid by transmitting the electrical energy stored in the electric vehicle battery to the grid with bidirectional transmission. Thanks to the bidirectional transmission, it can contribute to the grid by transferring the electrical energy contained in the electric vehicle batteries to the grid during peak hours. In this concept, since electric vehicles are considered as a distributed generation system, the location and capacity of charging stations becomes an important problem. In this thesis, Grey Wolf Optimization Algorithm (GKO), Sine Cosine Algorithm (SKA) and Chimpanzee Optimization Algorithm (SOA) algorithms were empoyed to decide the most appropriate capacity and location of the BEVCS into the electrical grid. In order to reduce the active power losses, the objective function has been determined and minimized. The algorithms were applied separately to IEEE 34-bus and 85-bus test systems, and the results of GWO, SCA and COA algorithms were compared with each other. As comparison GWO performed better than other algorithms in terms of both active and reactive power losses.