Impacts of Feeder Reconfiguration on Renewable Resources Allocation in Balanced and Unbalanced Distribution Systems
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Bibliographic record
Abstract
In this article, network reconfiguration and distributed generation allocation in distribution networks are dealt with simultaneously while imposing an objective of minimizing energy loss. The proposed method, which is based on a genetic algorithm, takes into consideration the uncertainty related to renewable distributed generation output power and the load variability. Three scenarios are assessed to analyze the superiority of the proposed method. In the first scenario, distributed generation units are allocated using the base configuration, followed by network reconfiguration. In the second scenario, distributed generations are allocated after network reconfiguration. In the third scenario, distributed generations are allocated simultaneously with network reconfiguration. The constraints involved include voltage limits, line current limits, and radial topology. Both balanced and unbalanced distribution systems are used as case studies.
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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 it