Data-Driven Analysis of Electric Vehicle Charging Impact on Power Distribution Systems
Bibliographic record
Abstract
Electric vehicles (EVs) are becoming increasingly essential for reducing greenhouse gas emissions and promoting a more sustainable transportation sector. However, EV adoption also introduces a significant electrical load. This paper analyzes real-world EV charging data alongside household electrical load data to evaluate the impact of EV charging on power distribution systems. The EV charging data were collected from a pilot program in Saskatchewan, Canada, while the household load data were obtained through advanced metering infrastructure (AMI) in the same province. The pilot program categorized participants into three groups: Open Choice, Targeted Charging, and Peak Avoidance. The Targeted Charging and Peak Avoidance groups received incentives to shift their charging behavior and reduce peak demand. Our analysis shows that such incentives are highly effective in reducing peak demand—for example, the Targeted Charging group achieved a 51% reduction. However, simulation results indicate that incentivized charging can overload transformers in low-voltage power distribution systems. The study shows that the evaluated power distribution system can handle 2 EVs per house under normal conditions. However, under extreme conditions (e.g., hot days), the existing power infrastructure cannot handle 1 EV per house, even with incentives. In such cases, an optimized charging strategy is necessary. The study also indicates that the voltage sag is not the bottleneck of the EV penetration. This study provides a foundational reference for utilities to design smart EV charging programs, assess their impacts, and plan for future infrastructure needs.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".