Load Coincidence Factors for Robust Optimal Power Flow in Radial Distribution Networks
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Bibliographic record
Abstract
This paper presents an analytically tractable method to solve a robust optimal power flow (OPF) problem in radial distribution networks considering uncertainty in solar photo-voltaic (PV) generation and load demand. The proposed method optimizes PV curtailment limits and set-points of dispatchable distributed energy resources that are feasible for any realization of unknown-but-bounded uncertainty in available PV generation and load demand. A well-motivated set representation enabled by load coincidence factors bounds uncertainty in load demand. Subsequently, closed-form expressions derived for worst-case voltage deviations arising from uncertainty in available PV generation and load demand help to bypass the need to solve inner optimization problems typical of a robust OPF problem. It can then be reformulated into a single deterministic optimization problem that can be solved efficiently. Numerical case studies involving a modified CIGRE low-voltage test system demonstrate the effectiveness of the proposed method and the validity of the closed-form expressions for worst-case voltage deviations.
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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