A stochastic distribution operations framework to study the impact of PEV charging loads
Bibliographic record
Abstract
In this paper, an extensive study on plug-in electric vehicle (PEV) driving characteristics, charging behavior and their impact on the utility is presented. The primary challenge in investigating the effects of PEV charging loads on the distribution system is taking care of the uncertainties. A detailed study on National Household Travel Survey (NHTS) data is carried out and the PEV charging behavior is modeled. Thereafter, a stochastic optimization model is proposed considering different PEV charging scenarios with their associated probabilities and the impact on system load, feeder loss and voltage deviation are studied. A Distribution Optimal Power Flow (DOPF) model with various objectives such as feeder loss minimization, energy drawn minimization and PEV charging cost minimization subject to feeder operational constraints including PEV charging within a 33-bus balanced distribution system is presented. In the uncontrolled charging case, the worst case scenarios are discussed. The proposed smart charging model provides with the optimal charging schedules which result in flattening the load profile.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".