Probabilistic Optimal Power Flow Applications to Electricity Markets
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the comparison of two solution methods for probabilistic optimal power flow problems; namely, the two-point estimate method (2PEM) and the cumulant method (CM). The goal of the P-OPF problem is to determine the probability distributions for all random variables in the problem. In this paper, bus loading and generators' supply power bids are considered as uncertain or probabilistic parameters in a P-OPF problem. Due to their importance in the context of electricity markets, special attention is paid to the uncertainty in locational marginal prices (LMPs) that results from uncertain behavior of market players. The proposed methods are tested on a modified version of the Matpower 30-bus system to demonstrate the capabilities of both approaches. Solution methodologies are compared in terms of accuracy and computational burden. Results are compared against those obtained from 10,000 sample Monte Carlo simulations (MCS). The proposed methods show high accuracy levels and are computationally significantly faster than an MCS approach
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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 | 0.001 |
| 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