Date of Award
Master of Science
Fangxing Li, Kevin Tomsovic, Kevin Bai
The electric grid has become one of the most important pieces of technology for modern society, providing power to homes and businesses at all hours of the day. Future increases of worldwide electricity demand along with growing threats of contingency scenarios such as cyberattacks cause great concerns regarding the reliability of the grid. Further, the gradual shift from fossil fuels to renewable energy sources creates a significant need for grid support services to aid in its operation. Electric vehicles will become more prevalent in coming decades; these modules present unique loads to the system that are prime candidates for use as both renewable energy storage and grid supporting devices.
This thesis focuses on the behavior of electric vehicles connected to fast-charging stations, which is largely ignored in many power systems studies. It turns out that the power drawn by the vehicles during fast-charging varies significantly throughout the charging process; experimentally derived charging curves give insight to this behavior and can be used to improve the accuracy of grid models with large electric vehicle loads. An aggregator based on charging curve data is developed to reflect the true load of an electric vehicle fleet as well as its ability to provide grid support during contingency events.
By comparing electric vehicle fleets with different levels of state-of-charge, the study shows that the aggregation of these fleets is important for power system operators to consider. During the fast-charging process, an electric vehicle fleet that is more charged overall is capable of less unidirectional grid support than a less charged fleet. The typical assumption that the vehicles are charging at their maximum rated power is insufficient for grid support planning when considering fast-charging, and this thesis demonstrates the significance of a fast-charging electric vehicle aggregator for use in these services.
Mingee, Trey I., "Electric Vehicle Aggregation Considering Fast-Charging for Power System Applications. " Master's Thesis, University of Tennessee, 2022.