Impact of Electric Vehicle Charging Profiles in Data-Driven Framework on Distribution Network

Abstract

In the field of transportation and energy production, Electric Vehicles (EVs) with rechargeable property is encouraged to using in many countries against carbon emissions. EVs are produced with different charging rate and energy capacity in last years. The uncertainties of EVs and EV users show the negative effects of charging at times of bulk charging on the grid. A successful distribution network operator has the option to charge EVs, which are increasing day by day with new investments in infrastructure and other equipment. However, new investments do not please both EV users and charging service providers in terms of cost and time. In this paper, the power management with the SOC-based coordinated charging method, which enable dynamic charging of EVs using real data-driven charging profiles, was proposed in the existed grid infrastructure. Firstly, 30 different EV types in 50 EV charging units connected to added between Bus 35 and Bus 36 in the Roy Billinton Bus-2 Test System. The coordinated charging method was compared with the uncoordinated charging method in terms of grid drawn active power at peak time and line loading. Secondly, peak load conditions of the grid were reduced with the integration of photovoltaic (PV) generation and battery energy storage (BES) system to the relevant bus on the test system. In addition, energy efficiency in terms of line loading has been demonstrated according to the uncoordinated charging method of the proposed coordinated charging approach.