Uncoordinated charging impacts of electric vehicles on electric distribution grids: Normal and fast charging comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plug-in electric vehicles (PEVs) have uncertain penetration in electric grids due to uncertainties in charging and discharging patterns. This uncertainty together with various driving habits makes it difficult to accurately assess the effects on local distribution network. Extra electrical loads due uncoordinated charging of electric vehicles have different impacts on the local distribution grid. This paper proposes a method to evaluate the impacts of uncoordinated PEVs charging on the distribution grid during peak period. Two PEVs charging scenarios are studied, including normal and fast charging. The impact analysis is evaluated in terms of voltage violations, power losses and line loading, which is implemented on a real distribution system in Canada. The results of the analysis indicate that there are significant impacts on distribution networks due to PEVs charging, which limits the accommodation of desired penetration levels of PEVs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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