Three-Stage Distribution Feeder Control Considering Four-Quadrant EV Chargers
Bibliographic record
Abstract
With the increased penetration of electric vehicle (EV) chargers in distribution systems, there is a need to understand and minimize their impact on medium-voltage (MV) and low-voltage (LV) networks. Thus, this paper proposes a three-stage algorithm to coordinate the operation of four-quadrant EV chargers with other volt/var control devices in MV and LV distribution feeders. The first stage operates on a day-ahead basis and defines the load tap changer and capacitor schedules while minimizing the peak load associated with EVs in the distribution system. The second and third stages update their operation every five minutes, to fairly allocate the aggregated and individual EV loads in the MV an LV feeders, respectively, while minimizing active power losses and voltage deviations. The proposed technique is applied to CIGRE's North-American MV and LV benchmark systems to demonstrate its ability to properly allocate EV loads, and improve distribution system performance in terms of losses and voltage deviations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".