Bibliographic record
Abstract
Modern power systems are prudently designed and operated to their brim as allowed by policies and procedures. Many of these utilities have advanced transmission systems built over half a century. With considerable aging transmission asset, their up keep and renewal is very expensive. Probabilistic planning, though computationally cumbersome, is an approach that objectively compares economic risk from aging assets versus cost of upgrades. With a demand for such a generic tool amongst utilities, this thesis presents a probabilistic approach for transmission system expansion planning. The proposed method estimates potential economic losses from aging transmission system assets considering N-1 contingencies where N-1 contingencies represent operation of the transmission system after one element is removed due to fault. Thereafter, the thesis proposes a formulation that computes the best transmission system reinforcement plan to eliminate economic losses from all possible N-1 contingencies. Finally, tests on a sample 7-bus system and IEEE 118-bus system where potential economic losses from N-1 contingencies is compared with transmission system optimal expansion plan are presented. Test results reveal that in certain cases, there is economic merit to upgrade the system and benefit with from a robust transmission system. A 304-bus North American system was also tested and is reported.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".