Multi-objective scheduling of electric vehicles considering wind and demand uncertainties

Abstract

In this paper, operation cost and emission in the presence of Electric vehicles (EVs), conventional power plants and renewable energy sources have been evaluated. Also demand and wind speed uncertainties using scenario tree is considered and applied to model. In EVs scheduling problem, drive patterns, electricity prices and batteries have most effect on results. In order to have a real and accurate analysis, it is necessary to get on real drive patterns and electricity prices properly. Several kinds of EVs are introduced which every kind has specific battery. Different cases for EVs and renewable energy sources in a 33 bus distribution test system are considered. Simulation model consist of cost and emission functions that should be minimized. According to nonlinear power flow constraints, multi-objective function is a MINLP problem. GAMS has been selected for solving economic /environmental problem. The ε-constraint method has been used to solve multi-objective optimization problem. Also, the output of this method is pareto optimal solutions. Finally, using membership function, the best solution is selected. The results demonstrate that system operator and EVs owners can benefit from proper charge and discharge.