Abstract

It has been predicted that electric vehicles will play a crucial role in incorporating a large renewable component in the energy sector. If electric vehicles are integrated in a naive way, they may exacerbate issues related to peak demand and transmission capacity limits while not reducing polluting emissions. Optimizing the charging of electric vehicles is paramount for their successful integration. This paper presents a model to describe the driving patterns of electric vehicles in order to provide primary input information to any mathematical programming model for optimal charging. Specifically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented. The model is defined by the time-varying probabilities of starting and ending a trip, and is justified due to the uncertainty associated with the use of the vehicle. The model is fitted to data collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters. The number of parameters in the proposed model is reduced using B-splines. Document type: Book

Abstract

Greenhouse gas emissions, in particular CO2 emissions, are a major environmental problem caused mainly by the transportation and the energy sectors. Electric vehicles have been proposed as a solution for mitigating greenhouse gas emissions in road transport. At the same time their potential emission reduction depends on the emissions from the generation of electricity used to charge the vehicles. This study analyzes indirect emissions of electric vehicles to examine optimization potential using real-world data. The results of the study suggest that charging during the daytime is associated with the usage of more electricity from renewable energy sources than charging in typical non-work hours. However, due to the highly violate character of renewable energy sources the differences are rather small. Also, CO2 emissions per kilometer driven depend on driving patterns influencing the energy demand of the vehicles. Accordingly, optimization potential using renewable energy-oriented time course of charging is found to be rather small with an average greenhouse gas emission reduction of 4%. Thus to achieve the potential of electric vehicles to solve environmental issues requires the optimization of driving and charging patterns as well as measures for reducing the carbon intensity in the electricity grid.