Discrete event-driven approach for electric vehicle charging and uncertainty evaluation

Abstract

Reduction of greenhouse gas (GHG) emission is gaining momentum in recent years, and the transportation industry is one of the major contributors. Electric vehicles are proven to have very low greenhouse gas emissions in comparison to internal combustion engine (ICE) vehicles. In addition, the electrical vehicle (EV) market is growing rapidly, and the charging of EVs will affect the power grid because charging a single electric vehicle is estimated to consume as much power as an average household for an entire day. This thesis modeled the charging of electric vehicles at the residential level and proposed different controller models for charging EVs in order to minimize the impact of their penetration. In addition, this thesis modeled and evaluated the uncertainty factor for load due to the EV penetration. This work resulted in significant improvement to the charging process using the proposed controllers and a significant reduction in the uncertainty factor of load forecasting caused by EV charging on the secondary side distribution transformer and substation.