Vehicle-Directed Smart Charging Strategies to Mitigate the Effect of Long-Range EV Charging on Distribution Transformer Aging
Why this work is in the frame
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Bibliographic record
Abstract
The recent introduction of affordable long-range electric vehicles (EVs) has the potential to trigger more widespread adoption of EVs with higher charging needs. Increased EV charging can have a detrimental effect on the distribution grid, especially by causing accelerated aging of transformers. Although many EV smart-charging strategies have been proposed to mitigate this problem, centralized and distributed smart-charging strategies require numerous new infrastructure components and, thus, take time and money to implement. This article proposes the term vehicle-directed smart charging to describe strategies that individual EVs can use to charge in a more intelligent way and, thus, lessen grid impact. This article proposes a new vehicle-directed smart charging concept, random-in-window (RIW), which has fixed-rate and variable-rate variants. The RIW strategy allows for random charging start times within a specific time window after the residential peak load has reduced. The RIW strategies are compared to other strategies using the real-world logged driving data from 150 drivers for one week using long- and short-range EV models. A transformer aging model indicates that the RIW strategies are approximately as good as a fully controlled centralized smart-charging algorithm at EV penetration rates up to 60% for long-range EVs and 70% for short-range EVs.
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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 | 0.001 |
| 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 it