Day-ahead dispatch of distribution feeders considering temporal uncertainties of PEVs
Bibliographic record
Abstract
This paper presents an approach to dispatch taps of Load Tap Changing (LTC) transformers, switched capacitors and Plug-in Electric Vehicle (PEV) charging in distribution feeders to minimize feeder daily peak demand, using a nonparametric Bootstrap technique, an alternative to Monte Carlo Simulations (MCS), to account for the PEV charging temporal uncertainties. From an initial sample of independent observations generated using a deterministic Genetic Algorithm (GA)-based optimization framework, Bootstrap samples are generated, which yield an estimate of the mean daily system peak demand, and the hourly tap, capacitor and PEV charging schedules. The proposed technique is applied to a distribution feeder model of an actual primary feeder in Ontario, considering a significant PEV charging penetration level. The results for an actual distribution feeder show the feasibility of the proposed approach, with a significant reduction of computational burden with respect to an MCS approach while still using a global search technique, which yields adequate tap and capacitor daily schedules for a Local Distribution Company (LDC) that properly accounts for PEV charging uncertainties.
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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".