Electric bus fleet charging management: A robust optimisation framework addressing battery ageing, time-of-use tariffs, and energy consumption uncertainty
Bibliographic record
Abstract
The large-scale adoption of electric buses offers sustainable and reliable transportation, but it poses challenges in designing appropriate charging strategies to accommodate the operation requirements of the fleets. The optimisation of those strategies is crucial to avoid disrupting daily operations due to insufficient energy for trips, aiming to minimise operational costs and grid overload because of coincident peak demand. This work introduces a robust optimisation model to provide solutions accounting for uncertainties in energy consumption, enabling operators to establish cost-effective and resilient charging plans. The model includes features such as battery ageing, time-of-use tariffs, vehicle-to-grid (V2G), and operational constraints. We propose a reformulation approach to solve the model and deal with its computational complexity. An illustrative case study is conducted using real-world data from a mid-sized city in Portugal. The findings indicate significant cost reductions through coordinated charging, with deterministic and robust models achieving 37 % and 12 % reductions, respectively, compared to a business-as-usual charging scenario. Further, V2G activities generate additional revenue, also emphasizing the importance of considering degradation costs to reduce battery capacity fade. Additionally, the effectiveness of the robust approach in addressing energy consumption uncertainty is demonstrated, offering operators a flexible method to adapt to various operational contexts and improve bus transportation service reliability. • Smart charging strategy considers tariffs, battery ageing, and energy trading. • Robust model enables optimal bus charging under energy consumption uncertainty. • Results indicate a reduction in total costs of up to 12 % for a robust strategy. • Efficient power management handles peak charging effectively. • V2G activities generate revenue, although limited by degradation costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".