Quantifying the benefits of extending electric vehicle charging deadlines with solar generation
Bibliographic record
Abstract
Significant cost reduction in recent years has made solar power an economically competitive power source in many regions today. In view of this, and the widespread introduction of electric vehicles (EVs) to the mass market, we study public EV charging stations with on-site solar generation that are backed up by conventional power from the grid. Since the carbon footprint of conventional power is higher than solar power, charging deadlines can critically affect the total solar energy available to charging stations and therefore the overall carbon footprint of the charging service. In this paper, we propose a method to quantify how much carbon footprint can be reduced as a function of the charging deadline by describing a performance-guaranteed fair power allocation algorithm in a public charging station. This enables us to study the three-way tradeoff between the charging deadline, the utility of EV owners, and the carbon footprint of EV charging. We find that our algorithm makes nearly optimal use of available green energy, while still guaranteeing that solar charging performs no worse than grid charging.
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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".