New tools for power system dynamic performance management
Why this work is in the frame
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Bibliographic record
Abstract
The application of market based approaches to power systems has, in general, resulted in the reduction of stability margins as profit maximization can lead to systems being operated in stressed conditions. As systems are operated closer to their limits, it is critical that the system is modeled appropriately and that control actions take into account stability margins. This paper reviews three recent tools for power system dynamic performance; first a probabilistic optimal power flow (P-OPF), which is used to incorporate uncertainty in system modeling; second, complementarity modeling is reviewed, as this approach allows for more appropriate modeling of how the system moves from stable equilibrium to unstable or loss of equilibrium. Finally, the normal boundary intersection method is presented. This method allows one to form the Pareto surface efficiently when considering a multi-objective optimization problem, such as a stability constrained optimal power flow
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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) | 0.000 | 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