Combined Adequacy and Static Security Considerations in Transmission System Reinforcement
Bibliographic record
Abstract
There is growing interest in designing and applying deterministic techniques that include probabilistic considerations in order to assess increased system stress due to the restructured electricity environment. The overall reliability framework proposed in this paper incorporates the deterministic N-1 criterion in a probabilistic framework, and results in the joint inclusion of both adequacy and static security considerations in system planning. The combined framework is achieved using system well-being analysis and traditional adequacy assessment. System well-being analysis is used to quantify the degree of N-1 security and N-1 insecurity in terms of probabilities and frequencies. Traditional adequacy assessment is incorporated to quantify the magnitude of the severity and consequences associated with system failure. A sequential Monte Carlo simulation approach is utilized in this paper for bulk electric system reliability assessment. The combined adequacy and security framework presented in this paper can assist system planners to realize the overall benefits associated with a system reinforcement option based on the degree of adequacy and security, and therefore facilitate the decision making process. Possible reinforcement alternatives for a test system are examined using reliability cost/reliability worth considerations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".