Probabilistic estimation of the state of Electric Vehicles for smart grid applications in big data context

This paper presents a framework and methodology to estimate the possible states of Electric Vehicles (EVs) regarding their location and periods of connection in the grid. A Monte Carlo Simulation (MCS) is implemented to estimate the probability of occurrence of these states. The framework assumes the availability of Information and Communication Technology (ICT) technology and previous data records to obtain the probabilistic states. A case study is presented using a fleet of 15 EVs considering a smart grid environment. A high accuracy was obtained with 1 million iterations in MCS.