Scheduling of mobile charging stations with local renewable energy sources

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Aktar, AK (Author) Other; Tascikaraoglu, A (Author) Other; Catalao, JPS (Author) FEUP

Journal

Title: Sustainable Energy, Grids and Networks

Vol. 37

ISSN: 2352-4677

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00Z-KPR

DOI: 10.1016/j.segan.2023.101257

Abstract (EN): Due to the depletion of fossil resources and increasing environmental concerns, electric vehicles (EVs) have been attracting more attention at the last decade. Their extensive integration into the energy systems, however, has led to numerous operational and technological challenges, especially during their bulk charging. In this study, an optimization algorithm based on mixed integer linear programming is proposed to dispatch mobile charging stations (MCSs), which have emerged as both an alternative and supplement to permanent charging stations (PCSs). It is aimed to mitigate the number of EVs that cannot be charged in PCSs, due mostly to the limited charging unit capacity and prolonged waiting times, by using an MCS. Five determinative cases involving a combination of different operating and pricing mechanisms are evaluated. The results reveal that the use of the MCS provides both economic and operational benefits. In the best case determined according to the result of the comparisons with various pricing mechanisms, the MCS provides an operational improvement of 64.3 % compared to the case without the MCS. The quantity of EVs requesting service is 1074 in all cases, while 986 EVs are served in the case with the best results. Besides, a profit increase of 46 % is achieved for the cases in which dynamic pricing is applied. An important point to note is that with the incentive mechanism applied, there is a significant increase in the profit in Case 5, while the number of EVs served is 967. In case 4, without incentive mechanism, the number of EVs served is 968.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14