Assessment of Transmission System Component Criticality in the De-Regulated Electricity Market
Bibliographic record
Abstract
This paper describes a probabilistic approach for assessing the criticality of bulk transmission system components in the de-regulated electricity market. The proposed method is based on the analysis performed by the Hydro One probabilistic composite system evaluation program and use of a simplified reliability model for the transmission system network. The method accounts for random failures of system generators and transmission system components, transmission system component ratings, system load profile and generator bids during a specific period of time. The assessment method uses a performance criterion based on the total system energy cost in ranking transmission components in terms of their importance to the over all system performance. Sensitivity analysis is performed to determine the impacts of changes in some system parameters on the ranking of transmission components. The proposed method will enable power system planners and operators to identify the most critical transmission system components with regard to system reliability or system operating cost or both. Having identified the most critical components of the system, the next step would be to develop action plans that address individual component reliability and capability. Discussion on the individual component action plans is beyond the scope of this paper. The proposed assessment method is illustrated using the IEEE Reliability Test System.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".