Vehicle-Directed Smart Charging Strategies to Mitigate the Effect of Long-Range EV Charging on Distribution Transformer Aging
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".