Stochastic Approach for Evaluating the Operation of Electric Power Distribution Systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a stochastic approach to evaluate the performance of electric power distribution systems (EPDS). The daily load and generation curves at each node are generated in a correlated and random manner for each day of the week. These curves, spanning seven days, constitute a sample within the Monte Carlo Simulation (MCS) and form a synthetic representation of a week. This data from the synthetic week is then utilized to calculate power flows, yielding the metrics of interest for each day: energy losses and nodal voltage violations. These daily metrics are aggregated over a year, considering the distribution of days within that year. Once the MCS converges, statistical values for annual voltage violations and losses are derived. The methodology was applied to a 23-node distribution system with capacitor banks, voltage regulators, and distributed energy resources. The findings indicate that employing the MCS while considering the correlation between nodal power demands and generation offers a practical approach to studying EPDS operation. Disregarding this correlation results in underestimating operational indicators within the grid.
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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