Distribution Factor Assessment for PEVs Charging Stations with V2G Capability in Power Systems
Bibliographic record
Abstract
Increased penetration level of plug-in electrical vehicles (PEVs), necessitates the need for more PEVs charging stations. However, by high penetration of charging stations, distribution power systems may have more challenges, such as high power loss and deteriorated voltage profile. So far, optimal sizing and sitting of charging stations have been utilized to improve the features of the power systems affected by charging stations. While, our studies show that distributed arrangements of charging stations can also play an important role. This role is more highlighted, when vehicle-to-grid (V2G) capability is also considered. To this aim, in this paper, considering total charging and discharging capacities of stations, a factor known as distribution factor is proposed. Then, the effect of different distribution factors, considering grid-to-vehicle (G2V) and V2G modes of PEVs, is discussed. Numerical results verify that distribution factor and hence distributed arrangements of charging stations can affect power loss and voltage profile of the host power system.
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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 | 0.003 |
| 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 | 0.001 | 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".