Electric bus charging station location selection problem with slow and fast charging

• Published in: Applied energy Vol. 382; p. 125242
• Main Authors: Gkiotsalitis, Konstantinos, Rizopoulos, Dimitrios, Merakou, Marilena, Iliopoulou, Christina, Liu, Tao, Cats, Oded
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2025
• Subjects: Charging station location selection; Charging time slots; decision support systems; Electric buses; energy; linear programming; metropolitan areas; Minimum deadheading; Mixed-integer programming; public transportation; Electric buses; Charging time slots; Minimum deadheading; Mixed-integer programming; Charging station location selection
• ISSN: 0306-2619
• DOI: 10.1016/j.apenergy.2024.125242

To facilitate the shift from conventional to electric buses, the required charging infrastructure must be deployed. This study models the charging station location selection problem for fixed-line public transport services consisting of electric buses. The model considers the deadheading time of electric buses between the final stop of their trip and the locations of the potential charging stations with the objective of minimizing vehicle running costs. The problem is solved at a strategic level; therefore, several parameters of day-to-day operations, such as deadheading distances, are included as aggregate data considering their average values. In addition, it considers different charger types (slow and fast), which are subject to a day-ahead scheduling of the charging sessions of the buses. The developed model is a mixed-integer nonlinear program, which is reformulated as a mixed-integer linear program and can be solved efficiently for large networks with more than 1940 bus trips and 336 charging installation options. The model is applied in the Athens metropolitan area, demonstrating its potential as a decision support tool for selecting charging station locations and charger types in large public transport networks. •Development of a charging station location selection model with fast and slow chargers.•Incorporation of multi-use charging stations in the modeling process.•Model reformulation to a mixed-integer linear program that can be solved to global optimality.•Proof of NP-hardness and computational complexity analysis.•Model implementation using data from the Athens metropolitan area.