CONGESTION MANAGEMENT AND LOOP FLOW CONTROL BY TRANSMISSION NETWORK RECONFIGURATION
Bibliographic record
Abstract
In this paper the problem of finding the optimal topological reconfiguration of a power transmission system is considered with the aim of providing a tool suited for congestion management. Network reconfiguration looks particularly appealing since it allows the relief of overloads by means of switching operations that may avoid generation or load curtailments. The techniques of corrective switching are profitably employed to formulate the problem of network reconfiguration for the purpose of congestion management. It is shown in the paper that the same optimization model employed in corrective switching can be used to solve the loop (or parallel) flow problem which occurs in today large interconnected systems. The solution of the resulting large-scale mixed-integer programming problem is carried out by a deterministic branch-and-bound algorithm included in the CPLEX optimization package. Tests were performed on the Italian network and on the European (UCTE) system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".