A Novel Statistical Markov-based Approach for Modeling Charging Demand of Plug-in Electric Vehicles

The integration of a massive number of plug-in electric vehicles (PEVs) in power distribution infrastructure brings direct technical challenges to the network management in terms of planning, control and operation. In order to promote the penetration level of PEVs, it is important to understand the different PEV states as well as the PEV charging demand. This paper attempts to address such technical challenge to present a novel statistical modeling approach for PEV charging demand through adopting a Markov-based approach. Through the analysis of the available driving behavior statistics data, the PEV states can be mathematically formulated and derived. Through the adoption of Markov model and Monte Carlo simulation technique, the Markov transition probability matrix of PEVs can be obtained. As a result, the transition among different PEV states of individual PEVs can be explicitly described. A number of case studies are carried out to validate the effectiveness of the proposed modeling approach.