Fuzzy security constraints for unit commitment with outages
Why this work is in the frame
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Bibliographic record
Abstract
Transmission systems of power systems face risk of emergency transmission contingency and line outages. Proper optimisation tools are required to schedule generation during emergency conditions and to be able to study the effect of the transmission line outages. This study proposes a successive mixed integer linear programming (MILP) method with fuzzy security constraints for solving the AC Security Constrained Unit Commitment (AC‐SCUC) challenge with transmission line outages. A linear formulation of AC‐SCUC challenge is created resulting in an MILP model. This MILP model is transformed into a robust fuzzy MILP model that overcomes infeasibility arising from line outages. This fuzzy MILP model is set up and solved successively using MILP technique whereas updating both continuous and integer variables making the proposed algorithm efficient. The proposed method is tested on 6‐bus, IEEE 57‐bus, and IEEE 118‐bus systems to demonstrate its capabilities and benefits.
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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