Impacts of Distribution-Level Joint Scheduling of Electric Vehicle Battery Charging and Swapping Stations on Reliability Improvement

Kursat Aktar A., Tascikaraoglu A., ERDİNÇ O., Guner S.

IEEE Transactions on Industry Applications, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1109/tia.2024.3416093
  • Journal Name: IEEE Transactions on Industry Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Batteries, Battery swapping station, Charging stations, Costs, Discharges (electric), electric vehicle, Electric vehicle charging, energy storage, Reactive power, smart grid, vehicle-to-grid, Vehicle-to-grid
  • Yıldız Technical University Affiliated: Yes


Recently, the rapid growth in electric vehicle (EV) sales and usage has brought significant attention to the required infrastructure, including EV charging stations, grid integration, maintenance facilities, and the supply chain for mechanical and electrical spare parts. Particularly in the electric grid where active power flow is managed with a delicate balance, the emergence of the management methods that provide EV users with a more convenient charging experience is highly regarded. This study explores the synchronized utilization of a Battery Charging Station (BCS) and Battery Swapping Station (BSS) through vehicle-to-grid (V2G), battery-to-grid (B2G), and swapping operations. The aim is to enhance the service quality for EV users and provide grid support during the peak energy demand periods. The performance of the devised constrained optimization algorithm is assessed across scenarios that involve a range of service and pricing options. The outputs reveal that the collaborative utilization of BCS and BSS offers advantages in terms of both economics and operations management. The analysis of different scenarios reveals that the designed algorithm results in a 42.1% cost-efficient operation when prioritizing economic benefits and a 37.4% increase in service provision when prioritizing more extensive service to EVs. Besides, compared to the base case, there is a substantial improvement in system reliability indices, ranging between 47% and 56%, along with load point indices reaching a significant enhancement of up to 68%.