Probabilistic OPF using linear fuzzy relation
Why this work is in the frame
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Bibliographic record
Abstract
Optimal Power Flow (OPF) is a very important tool for planning and analysis of power systems. In the recent times, uncertain renewable energy is being integrated into power systems in a large scale. Appropriate modeling of renewables in OPF requires using stochastic models. Using stochastic models of renewables in OPF is numerically and algorithmically challenging due to the complexity of stochastic models and nonlinear nature of bus power balance equations. Hitherto, Monte Carlo Simulation technique and Cumulant technique have been proposed, but they are not computationally viable for large systems. In this paper, we propose the use of linear fuzzy relation technique to relate stochastic models of dependent variables of OPF formulation in terms of control variables that include power output of renewables. This fuzzy relation uses Hessian matrix of the LaGrangian of the OPF formulation at optimal solution point. The method is tested on a 6-bus system and results are reported. One can intuitively see that this method can be easily extended to larger systems.
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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