Optimal Scheduling of Electric Vehicle Charging: A Study Case of Bantul Feeder 05 Distribution System

Abstract

The growing popularity of electric vehicles (EVs) has the potential to complicate distribution network operations. When a large number of electric vehicles are charging at the same time, the system load can significantly increase. This problem is exacerbated when charging is done concurrently in the evening, which coincides with peak load times. To prevent the increase in peak load and distribution operation stress, EV charging must be coordinated to achieve financial and technical objectives. This study seeks to evaluate the impact of financially driven EV charging scheduling algorithms. The contribution of this study is that the scheduling algorithm considers EV usage behavior based on real data as well as considers the state-of-charge (SoC) target set by EV owners. The proposed algorithm seeks to minimize the total charging cost incurred by EV owners using mixed-integer linear programming (MILP). The impact of the coordinated charging scheduling on the system demand profile and real distribution system operation metrics are also evaluated. The simulation result tested on the Bantul Feeder 05 system demonstrates that coordinated charging can reduce the charging costs by 57.3%. Furthermore, the peak load is reduced by 5.2% while also improving the load factor by 3.5% as compared to uncoordinated scheduling. Based on the power flow simulation, the proposed algorithm can reduce distribution transformer loading by 0.5% and improve voltage quality by 0.1% during peak load. This demonstrates that coordinated EV charging benefits not only the EV users but also the distribution system operator by preventing system operation issues.