A ROOT Approach for Stochastic Energy Management in Electric Bus Transit Center with PV and ESS
Bibliographic record
Abstract
The development of alternatives for urban mobility and public transportation has been promoted by the increasing concerns on the environmental issues in the recent years. Along with the evolution of battery technology and charging/discharging control methodology, the diesel buses have been replaced with the electric buses (EBs) gradually. In order to satisfy the increasing charging demand of EBs, the photovoltaic (PV) and energy storage system (ESS) are usually installed on- site, which can not only guarantee sufficient power supply for the transit center, but also decrease the service charge for capacity from the distribution system operator (DSO). Besides, through involving the vehicle- to-grid (V2G) mechanism, the EBs parking in the transit center can also supply power to mitigate the peak load. Considering uncertainty of the office base load, PV generation, and the random availability of EBs, a stochastic energy management approach for such EB transit center is urgently needed. In this study, we focus on minimizing the total cost of the transit center taking into account the service charge for capacity, the energy consumption cost, the battery degradation cost of ESS and EBs, and the power generation revenue. A detailed model is built to investigate the energy management operation in the system as well as the corresponding cost. Then, we decompose the non-linear optimization problem into two stages: In the first stage, we aim to mitigate the target peak load through a modified robust optimization over time (ROOT) approach; Based on the optimal target peak load, the charging/discharging schedule is optimized in the second stage. The corresponding algorithm is developed and the performance of the proposed approach is evaluated in the case study based on the actual data obtained from FortisAlberta and St. Albert Transit, AB, Canada.
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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".