Plug-in Electric Vehicle Load Modeling for Smart Charging Strategies in Microgrids

The widespread adoption of plug-in electric vehicles (PEVs) is a path to be taken towards a green energy future, yet the uncoordinated penetration of PEVs prompts overloadings and low voltage violations that the existing power grid is not capable of managing. This issue can be addressed by utilizing PEV load models in component selection and smart charging strategies. PEV load modeling researches focus on the aggregator's and system operator's perspectives, and consideration of individual PEV loads in charging strategies tend to use generalized assumptions. However, consumers' perspectives should also be considered in real-time charging strategies. This paper presents a method to develop the individual load models of PEV users with Kernel Density Estimation (KDE) for smart charging strategies. A simulations section that compares uncoordinated, coordinated, and First Come First Serve (FCFS) charging approaches is presented. The results show that individual load models complement smart charging algorithms' decision process.