Stochastic Optimal Power Flow in Hybrid Power System Using Reduced-Discrete Point Estimation Method and Latin Hypercube Sampling
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Bibliographic record
Abstract
Stochastic nature of some input variables dictates the requisite of probabilistic analysis in power systems operation and planning. Wind generation is considered as a main source of intermittency in power systems due to the uncertain nature of wind speed. The proposed probabilistic optimal power-flow (POPF) method investigates spatial correlation among sources to attain more practical output distributions. The method established reduced-discrete point estimate method (RDPEM) along with the Latin hypercube sampling (LHS) in order to attain the stochastic characteristic of optimization’s outputs. Despite needing less computational effort, highly accurate results can be obtained, while there is no prerequisite for probability distribution of the input random variables. In order to more validate the efficiency of the proposed method, the Gram–Charlier (GC) expansion is used to compare the outputs’ cumulative distribution functions (CDFs) that are obtained from Monte Carlo (MC) with RDPEM methods. The performance and precision of the proposed solution are ascertained by comparison with those of Monte Carlo with discrete LHS (MCDLHS) in a hybrid IEEE 14-bus test system.
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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