Network Reconfiguration for the Optimal Operation of Smart Distribution Systems
Bibliographic record
Abstract
This paper introduces a day-ahead network-reconfiguration model for smart distribution systems (DSs) in the presence of renewable distributed generators (DGs) and battery energy storage systems (BESSs). The proposed model aims to determine the optimal day-ahead operational schedule that minimizes two objective functions: the operating cost and the voltage deviations. The minimization of the voltage deviations will result in improvements in the next-day voltage profiles. The operational schedule obtained by the proposed model includes the network reconfiguration schedule, the BESS charging/discharging schedule, and the generation schedule of the dispatchable DGs. The proposed model takes into account the day-ahead forecasted variations in load demands and renewable DGs. The model also considers the maximum number of switching operations for each controlled switch in the network. The proposed model has been tested using a case study of a 33-bus smart DS that included different types of energy resources. The efficacy of the proposed model has been confirmed through a comparison between the model results and the base-case results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".