A Semidefinite Optimization-based Branch-and-Bound Algorithm for Several\n Reactive Optimal Power Flow Problems
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Bibliographic record
Abstract
The Reactive Optimal Power Flow (ROPF) problem consists in computing an\noptimal power generation dispatch for an alternating current transmission\nnetwork that respects power flow equations and operational constraints. Some\nmeans of action on the voltage are modelled in the ROPF problem such as the\npossible activation of shunts, which implies discrete variables. The ROPF\nproblem belongs to the class of nonconvex MINLPs (Mixed-Integer Nonlinear\nProblems), which are NP-hard problems. In this paper, we solve three new\nvariants of the ROPF problem by using a semidefinite optimization-based\nBranch-and-Bound algorithm. We present results on MATPOWER instances and we\nshow that this method can solve to global optimality most instances. On the\ninstances not solved to optimality, our algorithm is able to find solutions\nwith a value better than the ones obtained by a rounding algorithm. We also\ndemonstrate that applying an appropriate clique merging algorithm can\nsignificantly speed up the resolution of semidefinite relaxations of ROPF large\ninstances.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it