Multi-objective Load Scheduling in a Smart Grid Environment

Smart grid is a remarkable development for managing the existing grids more efficiently. This paper deals with an integration of distributed energy resources and plug-in electric vehicles (PEVs) into an existing grid. There are significant impacts due to PEVs in the existing grid. However they also bring negative impacts to the grid if they are not coordinated properly. The continuous varying load and voltage fluctuations caused by their disordered charging behaviour can be detrimental to the grid. In order to overcome them, an intelligent load-scheduling strategy is applied in this paper. A multi-objective optimization strategy based on non-dominated sorting genetic algorithm (NSGA-II) is used in this paper to minimize two contradicting objective functions such as voltage deviation at buses and the total line loss simultaneously. The applied method is tested on IEEE 17-bus test system. Simulation results show the superiority of the applied method.