Mathematical methods of combining deterministic/probabilistic criteria in short-term generating reserve scheduling
Bibliographic record
Abstract
Probabilistic approaches generally base the design and operating constraints on the criterion that the risk of certain events must not exceed preselected limits. Many utilities still prefer to use deterministic techniques owing to the difficulty in interpreting a numerical risk index and the lack of sufficient information provided by a single index. This is especially true in the power system operating domain as existing probabilistic risk indices do not provide any assessment of the capacity reserve available during the course of system operation. A practical way to overcome these difficulties is to embed deterministic considerations into the probabilistic framework in the form of system well-being analysis. Incorporating this framework in system operation overcomes some of the difficulties in interpreting the risk index and also provides the system operator with important information on the degree of system well-being. The intent of this paper is to present the basic mathematical principles of short-term generating reserve scheduling utilizing the well-being framework. The evaluation process is illustrated using an educational test system, designated as the RBTS.
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".