Investigating Potential New Load Patterns on Distribution Transformers from Residential Electrification
Bibliographic record
Abstract
As nations set ambitious targets to phase out internal combustion engine (ICE) vehicles and electrify transport and household systems, it is anticipated that there will be a surge in demand for electric vehicles (EVs), heat pumps (HPs), and electric water heaters (EWHs). Utility providers must assess what this shift means for the existing grid. Although generation may be sufficient, attention must turn to local distribution, particularly individual street- level transformers. As EV chargers are installed and households replace gas systems with HPs and EWHs, the grid faces rising pressure. Many consumers prefer to charge vehicles at home in the evening, placing extra demand on local infrastructure. When several residents charge EVs while using water heating and cooling systems, transformer capacity may be exceeded. This study uses data from an Ontario utility to examine new loading scenarios as EVs, HPs, and EWHs are adopted in Canadian neighbourhoods. One transformer showed a maximum hourly load increase of 706.59% at 25% uptake and 1292.22% at 50%. Sustained high loads accelerate insulation ageing and raise the risk of early failure, with overloads possible when 11 EVs charge simultaneously at high speed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".