Grid Integration of New and Old Electric Vehicles with Battery Degradation Models

Abstract

Electric Vehicles (EVs) are becoming popular for environmentally friendly transport solutions and as demand dispatch options to a distribution grid. EVs are, at present, the most reliable substitutes for standard fossil fuel-based cars. However, the increasing integration of EVs poses unique challenges for voltage and frequency regulation in electric distribution grids. In order to overcome the intermittency of renewable energy sources, energy storage systems (ESS) are required for proper energy management with fewer fluctuations in voltages. The mobility and variability due to EV loads exacerbate the voltage regulation problem in distribution grids characterized by X/R ratios. The large-scale grid integration of EVs dramatically depends on the electricity price, renewable energy, and ESS. A system for EVs to charge and discharge according to the user’s requirements with an effective method of charge flow analysis from the energy management system is presented. It comprises a controlled operational method that works with the EVs state of charge (SOC). The operation is mainly focused on two modes, with each mode performing the task through early detection of SOC. This would trigger the user to decide to charge using the automatically controlled technique or manual user-input method. Both methods ensure the proper load flow when charging and discharging of EVs occur at any time of the day. The effects of different operations and the impact of EVs on the system can be analyzed by analyzing voltage sensitivity and choosing an appropriate time for charging and discharging cycles. Coding was done on the distributed system simulator, and this new method involves local/remote, active/reactive power injection on a bus, calculated by voltage sensitivity analysis. The battery degradation algorithm connects and distinguishes EV types and identifies the battery’s SOC and depth of discharge (DOD). The model has four discrete stations: dedicated EV charging stations, commercial, offices, and residential. This approach is promising; analyzing several human factors in large-scale grid integration for both new and old EVs is tested. An accurate estimation of the SOC of an EV battery is critical, especially when assessing the driving range of an EV. However, most research has focused on the standard driving cycle (SDC) or the battery’s internal parameters to simulate the outcomes. A drive cycle that describes the vehicle’s activity is critical in quantitative research. Well-developed drive cycles capture the driving pattern, human behavior and represent the traffic conditions.