Fast Heuristics for Transmission-Line Switching
Bibliographic record
Abstract
The optimal transmission switching (OTS) problem, a mixed-integer program (MIP), has been proposed as a way to choose lines to take out of service to reduce generation costs. One impediment to the use of OTS in practice is the very long computing time to solve it. This paper proposes two heuristics which rely on a line-ranking parameter that is based on the optimal solution to the ordinary dc optimal power flow problem, a linear program (LP). One heuristic solves a sequence of LPs, removing one line at a time, and the other heuristic solves a sequence of MIPs, removing one line at a time, and each MIP has far fewer binary variables (for switching the lines out of service) than the original MIP. The proposed heuristics are tested on 118-bus and 662-bus systems, and compared with the most common previous heuristic in the literature, which solves a sequence of MIPs, removing one line at a time, with each MIP having all binary variables, i.e., one for each line. Both heuristics are much faster than the previous heuristic from the literature. In almost all cases tested, both proposed heuristics find cost reductions that are approximately as large as the previous heuristic. The computing time reductions are so great that OTS may now be practical with respect to the computing time issue.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".