7. Estimating the State of Charge of Plug-In Electric Vehicle Fleet Applying Monte Carlo Markov Chain

Modeling the drivers’ behavior, concerning their reaction with respect to charging management programs, is a key factor for the power system operators and plug-in electric vehicle (PEV) aggregators. In this regard, the state of charge (SOC) of PEV fleet is one of the important parameters that can affect the drivers’ behavior modeling and consequently the simulation results of planning and operation problems. In this chapter, Monte Carlo Markov Chain (MCMC) is applied to estimate the hourly SOC of PEV fleet in the day. MCMC, as the specific type of stochastic process, is a powerful method to analyze the scientific dataset and determine the probability distribution function of model parameters, by repeatedly applying the dataset. The dataset used in this study includes the real longitude and latitude of driving routes of PEVs in San Francisco, recorded in every 4-minute interval of the day. The position dataset is converted to the distances travelled by the PEVs, and then the hourly SOC of PEV fleet is determined applying the technical specifications of PEVs that include the initial SOC of PEV fleet, the energy consumption index of PEVs, and the capacity of PEVs’ batteries. After estimating the best-fit line of SOC of PEV fleet and the related confidence bands, the effects of problem parameters on the MCMC simulation results are studied.