Probabilistic evaluation and planning of power transmission system reliability
Bibliographic record
Abstract
Modern power systems are designed prudently and then operated according to the permitted equipment limits set out in standards, policies, and procedures. To meet the growing demand, utilities apply transmission expansion tools for planning and updating their transmission assets. However, due to their unpredictable nature, transmission contingencies cannot be easily integrated during the planning stage. Although computationally cumbersome, probabilistic planning is an approach that could enable an objective comparison of the economic risk associated with a contingency event versus the cost of upgrades. In the context of the development of a generic assessment tool for such a process that could be employed by utilities, this study presents a systematic approach for transmission system expansion planning that includes consideration of an N − 1 contingency. The proposed method provides an estimate of the potential economic losses that could occur due to contingencies related to transmission, which are quantified as the cost of expected energy not supplied. The study then introduces a proposed formulation that computes an optimal plan for transmission system reinforcement that will eliminate the economic losses associated with N − 1 contingencies. The results reveal that in specific cases, upgrading the system has economic merit as well as offering the benefits to be derived from a robust transmission 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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".