Distribution System Optimization Based on a Linear Power-Flow Formulation
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Bibliographic record
Abstract
In this paper, a framework for distribution system optimization is proposed. In this framework, different control variables, such as switchable capacitors, voltage regulators, and system configuration can be optimally determined to satisfy objectives, such as loss minimization and voltage profile improvement. Linearized power-flow equations are used in the optimization, and the problem is formulated as mixed-integer quadratic programming (MIQP), which has a guaranteed optimal solution. Existing efficient solution algorithms developed for MIQP problems facilitate the application of the proposed framework. System operational constraints, such as feeder ampacities, voltage drops, radiality, and the number of switching actions are considered in the model. The performance of the proposed framework is demonstrated using a variety of distribution test systems.
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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