Scenario generation for electric vehicles' uncertain behavior in a smart city environment. (15th September 2016) - Soares, João; Borges, Nuno; Fotouhi Ghazvini, Mohammad Ali; Vale, Zita; de Moura Oliveira, P.B.
Abstract: This paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location. Highlights: New concept/framework in smart cities context to estimate the states of electric vehicles and energy demand. Monte Carlo Simulation and fuzzy logic probabilistic approach to support the envisaged concept. A day-ahead and an hour-ahead stochastic scenarios generation to support the smart grid's operational decisions.