Load Coincidence Factors for Robust Optimal Power Flow in Radial Distribution Networks
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".