Acceleration of umbrella constraint discovery in generation scheduling problems
Bibliographic record
Abstract
Summary form only given. Security-constrained optimal power flow (SCOPF) and security-constrained unit commitment (SCUC) problems are necessary tools to system operators for operational planning and near-to-real-time operation. The solution time of these problems are challenging mainly due to their inherent large size. Previous studies have shown that relatively few of those problems' constraints serve to enclose their feasible set of solutions. Therefore, the constraints that do not contribute to the feasible set of solutions could be discarded to decrease the size of these problems and their associated solution times. Umbrella constraint discovery (UCD) has been proposed to identify and rule out redundant constraints in dc-SCOPF problems. In this paper, we propose an improvement over the original UCD formulation that exploits the structure of its parent SCOPF problem. This new partial UCD approach can lead to significant speed-ups in terms of UCD solution time and size. Based on the encouraging results for partial UCD on SCOPF, we apply the technique on SCUC. Alike in SCOPF, partial UCD can efficiently strip out redundant (i.e. non-umbrella) constraints off SCUC. We find, however, that because of its structure, SCUC has a much lower proportion of non-umbrella constraints in comparison to SCOPF.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".