Security-Constrained Unit Commitment With Uncertain Wind Generation: The Loadability Set Approach
Bibliographic record
Abstract
We present a novel approach to the security-constrained unit commitment (SCUC) with uncertain wind power generation. The goal is to solve the problem considering multiple stochastic wind power scenarios but while significantly reducing the computational burden associated with the calculation of the reserve deployment for each scenario. The method is called reduced SCUC or R-SCUC and is based on the notion of loadability set, that is, the set of residual demand scenarios that can be met by the transmission and reserve capability of a given power system at any specific hour. The key is to project all feasible generation and demand vectors onto the demand space and reformulate the SCUC within this loadability set rather than on the larger set of generation and demand. The accuracy and performance of R-SCUC were gauged and compared to SCUC via a three-region multi-unit system and by the IEEE 24-bus reliability test system with multiple units. Simulations support the accuracy and superior computational performance of R-SCUC.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".