AC Transmission Network Expansion Planning Using the Line-Wise Model for Representing Meshed Transmission Networks
Bibliographic record
Abstract
In this paper, a Line-Wise (LW) formulation of the AC Transmission Network Expansion Planning (AC-TNEP) problem for meshed transmission networks is proposed. The proposed formulation is then relaxed using a novel Quadratic Convex (QC) relaxation that can handle the mixed-integer nature of the problem and be solved efficiently using commercial solvers. The exact and relaxed formulations have been integrated into an algorithm for solving the AC-TNEP problem that has been introduced as an enhancement of an existing algorithm in the literature for obtaining feasible solutions of the AC-TNEP problem upon the use of convex relaxations to solve it. The AC-TNEP problem is solved for different scenarios that involve test cases ranging from 6 to 118 buses. The obtained solutions for the 6-bus to 46-bus test cases are shown to be globally optimal or reinforced to identical or better than the available solutions in the literature. Moreover, as distinguished from solutions in the literature, the proposed algorithm does not utilize approximations and heuristic constraints that are used to ease solving the AC-TNEP problem and were found to potentially lead to locally optimal solutions. Furthermore, the proposed algorithm is used to solve the AC-TNEP problem for the 87-bus and 118-bus test cases to establish its ability to solve for larger test cases. Extensions of the proposed algorithm to account for Reactive Power Planning (RPP) and dynamic planning are studied to further demonstrate its merits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".