Charge Schedule Optimization and Infrastructure Planning for Solar-Integrated Electric Bus Transit Systems
Bibliographic record
Abstract
As urban transit systems transition towards electrification, using renewable energy sources (RES), such as solar, is essential to make them efficient and sustainable. However, the intermittent nature of renewables poses a challenge in deciding the solar panel requirements and battery energy storage system (BESS) capacity at charging locations. To address these challenges, we propose a two-stage multi-scenario model that considers seasonality in solar energy generation while incorporating temperature-based variations in bus energy consumption and dynamic time-of-use electricity prices. Specifically, we formulate the problem as a multi-scenario linear program (LP) where the first-stage long-term variables determine the charging station power capacity, BESS capacity, and the solar panel area at each charging location. The second-stage scenario-specific variables prescribe the energy transferred to buses directly from the grid or the BESS during layovers. We demonstrate the effectiveness of this framework using data from Durham Transit Network (Ontario) and Action Buses (Canberra), where bus schedules and charging locations are determined using a concurrent scheduler-based heuristic. Solar energy data is collected from the National Renewable Energy Laboratory (NREL) database. We solve the multi-scenario LP using Benders' decomposition, which performs better than the dual simplex method, especially when the number of scenarios is high. With solar energy production at select terminals, our model estimated a cost savings of 9.72% and 23.79% for the Durham and Canberra networks, respectively. Our results also show that the scenario-based schedule adapts better to seasonal variations than a schedule estimated from average input parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".