Evaluating and Optimizing Opportunity Fast-Charging Schedules in Transit Battery Electric Bus Networks

Public Transport Operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fastcharging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two Mixed Integer Linear Programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time-Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event-Optimization (DEO) discretizes the time and decisions into non-uniform slots based on arrival and departure events in the network. In addition to the DEO’s higher practicability, the comparative computational study carried out on the transit bus network in the city of Rotterdam shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared to two reference common-sense greedy strategies; First-In-First-Serve and Lowest-Charge-Highest-Priority.

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