Distributed Continuous-Time Optimal Power Flow
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a distributed continuous-time optimal power flow (OPF) model, with DC power flow constraints, for a multi-area transmission network. The model exploits the unique properties of variational optimization, function space representation, and the alternating direction method of multipliers (ADMM) to enable continuous-time power exchange between adjacent areas. More specifically, the centralized multi-area OPF is formulated as a variational optimization problem with continuous-time load and decision variables (power generation, voltage phase angles, line/tieline power flows), which is then converted to a conventional optimization problem by projecting the load and decision trajectories into the Bernstein function space, and is decomposed to function space-based OPF sub-problems of individual areas using ADMM. The numerical results of implementing the proposed model on a synthesized three-area network indicate convergence to the centralized continuous-time OPF solution and showcase computational efficiency and the efficient sharing of ramping resources among areas.
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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) | 0.000 | 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) | 0.002 | 0.011 |
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