Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II

As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time EVs can reduce users' expense. Thus, schedule EV load requires multi-objective optimization. A diversi-ty-maximization non-dominated sorting genetic algorithm (DM-NSGA) -II is developed to perform by considering profile, cost, and battery degradation. Furthermore, a real-time locally optimal adopted utilizing flexible scale. The case study illustrates that proposed DM-NSGA-II prevent being trapped relatively limited region so as diversify results provide trade-off solutions decision makers. simulation analysis shows variable scale continuously involve present realtime rather than rely on forecasting data. more practical without loss accuracy.