Multi‐priority queuing for electric vehicles charging at public supply stations with price variation
Bibliographic record
Abstract
Abstract As electric vehicles (EVs) become more popular, public charging stations for such vehicles will become common. Because the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the charging waiting time. To achieve this goal, we propose two models where vehicles communicate beforehand with the grid to convey information about their charging need and location. In the first model, we develop a mathematical formalism for handling requests for charging vehicles at public charging station based on queuing theory. The second model extends the first one by considering priority queues with two EV classes, high and low, and a cut‐off service discipline. Both models are evaluated while considering mobility of vehicles in an urban scenario and time‐of‐use pricing. Finally, we propose two algorithms for directing vehicles to charging stations in a way to minimize either their waiting time to plug‐in or their waiting time to charge completion. Simulation results show the effectiveness of the proposed approaches when considering both real EV and charging station characteristics and constraints. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".