Incorporating multi‐state unit models in composite system adequacy assessment
Bibliographic record
Abstract
Abstract Components are usually represented by a two state model in conventional generating capacity and composite generation and transmission system reliability studies. Multi‐state generating unit models create a significant increase in the number of generation contingency states and can result in a considerable increase in the overall solution time. In order to avoid this problem, the derated states are usually amalgamated with the totally forced out state to create the derating‐adjusted forced outage rate (DAFOR). This statistic is also known as the equivalent forced outage rate (EFOR). Studies have shown that modeling large generating units in generating capacity adequacy assessments using DAFOR can provide pessimistic appraisals. Many utilities therefore use multi‐state generating unit representations to assess generating capacity adequacy, in order to obtain more accurate appraisals. There is relatively little published material dealing with the effects of using multi‐state generating unit representations in composite system adequacy assessment. This paper illustrates these effects by application to the IEEE‐Reliability Test System (RTS). Load point and system indices for the test system are presented to illustrate the impact of incorporating multi‐state representations in composite system adequacy assessment. Attention is focused on the effects of model variations including how many derated states should be used in a multi‐state model to obtain a reasonably accurate appraisal. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".