GreenBroker: Optimal Electric Vehicle Park-and-Charge Control via Vehicle-to-Infrastructure Communication
Bibliographic record
Abstract
The increasing market share of electric vehicles (EVs) makes charging facilities indispensable infrastructure for integrating EVs into the future smart grid. The promising facility called park-and-charge station was recently proposed. Existing studies on park-and-charge station mainly focus on managing the charging distribution of onsite EVs, ignoring the impacts of offsite EVs in the region. In this paper, we fill this gap by leveraging the emerging vehicle-to-infrastructure (V2I) communication technique to manage the charging schedule of both onsite and offsite EVs. Specifically, we design a park-and-charge management system, GreenBroker, which allows park-and-charge stations to control the arriving rate by sending real-time prices to EVs via V2I communications, and to control the charging rate via real-time electricity state. We develop a two-timescale stochastic optimization model, maximizing the revenue of park-and-charge stations while ensuring a finite charging delay of EV users. We derive the worst-case charging delay of EVs and show that it provides an [O(1/V), O(V)] tradeoff between the revenue of charging stations and the worst-case delay of EV users. We also demonstrate the efficacy of GreenBroker via trace-data simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".