Semidefinite Relaxation of Optimal Power Flow for AC–DC Grids
Bibliographic record
Abstract
The proliferation of technologies operating on dc power has motivated the system planners toward integration of dc and ac grids. The optimal power flow (OPF) analysis is widely used to determine the economically efficient operating points of the power grids. The OPF problem in ac-dc grids is a non-convex optimization problem due to the nonlinear power flow equations and the operating constraints imposed by the ac-dc converters. In this paper, we study the OPF problem in ac-dc grids to address the non-convexity of the problem. The objective of the ac-dc OPF problem is to jointly minimize the generation cost and the losses on the lines and converters. The optimization problem is subject to the ac and dc power flow constraints, the limits of the voltages and line flows, and the operating limits of the converters. We use convex relaxation techniques and transform the problem to a semidefinite program. We derive a sufficient condition for zero relaxation gap to obtain the global optimal solution. Simulations are performed on an IEEE 118-bus test system connected to sample dc grids. We show that the zero relaxation gap condition holds for the case study and the global optimal solution can be obtained.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".