An Evaluation of Electric Vehicle Penetration under Demand Response in a Multi-Agent Based Simulation

Abstract

This paper proposes an electric vehicle charging model and various control algorithms that are further incorporated into a multi-agent system to evaluate impacts of electric vehicle penetration on the power system. Electric vehicles have become increasingly popular due to the high costs of the operation of gas / diesel powered vehicles and the potential to reduce CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission. In this work, we propose the electric vehicle charging model and associated control algorithms to aggregate the electric vehicle load. Simulation results show that uncontrolled charging of electric vehicles can jeopardize the stability of the power system. In a worst-case scenario this can lead to an increase of peak demand by 53.2%, while by using appropriate scheduled charging the electric vehicles can have no contribution to the peak demand. Furthermore, scheduled charging dramatically reduces the standard deviation of the residential load (by up to 51%). Therefore, the aggregation of electric vehicle demand under an appropriate demand response control strategy has the potential to dramatically improve the stability of the power system with virtually no negative impacts. The proposed electric vehicle charging model and the associated scheduling algorithm can be embedded into a home energy management system or a smart charger.