Adaptive Optimal Management of EV Battery Distributed Energy for Concurrent Services to Transportation and Power Grid in a Fleet System Under Dynamic Service Pricing
Bibliographic record
Abstract
Deployment of electric vehicles (EVs) in a fleet system to deal with environmental issues has been at the center of attention over the past several years. While the battery of each EV offers small storage, hundreds of EVs collectively can offer large energy storage to serve a power grid. This article develops a model for a central controller in a fleet system that allows adaptive utilization of EV batteries distributed energy for concurrent services to the transportation and power grid. The optimization model integrates various slack variables and control parameters for managing real-time fare prices, adaptive energy, and reserve margin allocation, interaction with the grid operator, and meeting the fleet target revenue. The proposed model incorporates EV driver's input into the scheduling process to allow the driver to flexibly manage their battery capacities based on their availability and assessment of the transportation services demand. A dynamic pricing mechanism is developed for real-time calculation of fare rates to allow the EV fleet optimization problem to achieve a daily revenue target while limiting fare prices in a competitive market. Numerical results indicate that the model can manage several EVs for various services while enhancing the fleet financial metrics.
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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 | 0.001 |
| 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".