Modified and Augmented Nodal Analysis-based Optimal Power Flow
Bibliographic record
Abstract
The optimal power flow (OPF) problem focuses on the operational efficiency of electric power systems. Modern grids include a variety of devices that are often omitted in traditional OPF formulations, namely, different types of generators, transformers, or power electronics-based devices. The modified and augmented nodal analysis (MANA) extends the traditional nodal analysis by expressing circuit equations in a generic sparse matrix representation. The MANA approach can seamlessly accommodate constraints from various types of devices commonly encountered in modern networks. This, in turn, allows their straightforward inclusion as constraints in OPF by modelling them through MANA using their current and voltage equations. This work proposes a new OPF formulation based on MANA and the corresponding power flow constraints, which we refer to as MANA-OPF. The non-convex MANA-OPF is solved using a nonlinear solver, namely, IPOPT in Julia. The formulation is tested on several test cases. The results are compared to a standard approach used by PowerModels.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".