A Three-Layer Stochastic Energy Management Approach for Electric Bus Transit Centers With PV and Energy Storage Systems
Bibliographic record
Abstract
Along with the increasing electric bus (EB) penetration, the impact of the extensive charging load on the distribution system has been aggravating. To mitigate such impact, energy storage systems (ESSs) and photovoltaic (PV) are usually installed in the EB transit centers (EBTCs). In this article, a three-layer stochastic energy management approach is proposed for EBTCs to reduce the operation cost while maintaining local voltage quality. In the first layer, a modified robust optimization over time (ROOT) approach is developed to obtain the charging/discharging margin with minimum EBTC operation cost. In the second layer, the voltage regulation impact on the local voltage quality is estimated through power flow analysis considering voltage fluctuation and line loss minimization. In the third layer, the charging/discharging strategy is optimized with dynamic programming based on a modified greedy algorithm. The performance of the proposed approach is evaluated in a case study based on the IEEE 123-bus test feeder and the real operation data obtained from St. Albert Transit in Alberta, Canada. The results indicate that the proposed approach can not only minimize the EBTC operation cost but also well maintain the local voltage quality, in comparison with existing energy management approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".