Fast SDP Relaxation of the Optimal Power Flow Using the Line-Wise Model for Representing Meshed Transmission Networks
Bibliographic record
Abstract
In this paper, we propose a novel Semidefinite Programming (SDP) relaxation of the Optimal Power Flow (OPF) problem. The proposed formulation utilizes the Line-Wise Model (LWM) to represent meshed transmission networks. This allows the constraints of the proposed formulation to mostly depend on diagonal elements of the voltages' Hermitian${\bm{W}}$matrix in contrast to the Bus Injection Model (BIM) where constraints heavily utilize its off-diagonal elements. Test cases with bus sizes of 3 to 9241 were considered and chordal sparsity was exploited for them. Obtained results show that the proposed SDP-LW OPF formulation manages to provide solutions of similar or better quality for most test cases that belong to the Typical and Congested (TYP and API) Operating conditions. Furthermore, the proposed SDP-LW OPF formulation manages to reduce the solver's computational time for most of large-scale test cases with reductions up to 80.298%. Results analysis shows that reductions in the solver's computational time upon using the proposed SDP-LW OPF formulation are affected by its ability to significantly reduce constraints with off-diagonal elements of the${\bm{W}}$matrix without drastically increasing constraints with diagonal elements of the${\bm{W}}$matrix. Furthermore, the percentage of large sized cliques is found to affect the obtained reductions through its influence over the number of needed linking constraints for relating the elements of the${\bm{W}}$matrices of decomposed cliques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".