Unit commitment – a fuzzy mixed integer Linear Programming solution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unit commitment (UC) of a large system is a complex puzzle with integer/continuous variables and numerous inter-temporal constraints. After deregulation, price offers submitted by GenCos are predominantly in the form of linear price quantity (PQ) pairs. A fuzzy UC formulation that uses price offers modeled as PQ pairs. This fuzzy linear optimisation formulation of UC is solved using a mixed integer linear programming (MILP) routine. In this formulation, start up cost is modelled using linear variables. The fuzzy formulation provides modeling flexibility, relaxation in constraint enforcement and allows the method to seek a practical solution. The use of MILP technique makes the proposed solution method rigorous and fast. The method is tested on a 24 h, 104-generator system demonstrating its speed and robustness gained by using the LP technique. A five-generator system is additionally used to create a see-through example demonstrating advantages of using the fuzzy optimisation model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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