Impact Assessment of Electric Vehicles Charging on the Loss of Life of Distribution Transformers
Bibliographic record
Abstract
Electrification of the transport sector is an essential component of the energy transition. However, the arrival of large numbers of Electric Vehicles (EVs) may negatively impact grid components. Expressly, steep peak demands from the uncoordinated charging of EVs will stress distribution transformers. This situation is aggravated depending on the ambient temperature since high temperatures reduce the transformer's operating range, and very low temperatures increase the energy demand of the EVs. In this context, this paper proposes an impact assessment methodology to consider the temperature effects jointly on EVs' demand and transformers' loss of life. Firstly, a time-series modeling process characterizes the EV charging profile and its dependency on ambient temperature. Secondly, a transformer's expected loss of life is computed as a function of its hotspot temperature, according to the IEC 60076–7 standard. Experiment results on actual data show an increase of 36% in EVs' energy demand as ambient temperature drops from 25°C to -15°C. The cold temperatures are likely to increase the transformers' operating range and help them to support longer peak demands. Thus, results suggest that transformers' degradation is significantly higher in summer sessions, more than one hundred times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".