a robust renewable energy source-oriented strategy for smart charging of plug-in electric vehicles considering diverse uncertainty resources

Nowadays, the notion of plug-in electric vehicle (pev) as a valuable tool of energy management has been extensively employed in smart distribution grids. the main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. moreover, the specific capability of renewable energy sources (ress) in terms of contribution in pev smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. however, the load demand, res generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. hence, a non-deterministic optimization model based on information gap decision theory (igdt) is proposed in this paper to specify a robust pev smart charging pattern. to solve the multi-objective proposed igdt-based pev smart charging (igdt-psc) model, the multi-objective version of particle swarm optimization (mopso) is utilized to define a set of pareto optimal solutions. furthermore, the final solution among the pareto solutions is selected by means of a linear fuzzy satisfaction rule. the simulation results for a test smart microgrid comprising a pev, a set of res units and a load demand verify the  effectiveness of the proposed igdt-psc model.