Benefits of Employing an On-line Security Limit Derivation Tool in Electricity Markets
Why this work is in the frame
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Bibliographic record
Abstract
Security limits in both re-structured and vertically integrated power systems are usually derived based on a limited number of off-line system studies using a previously defined portfolio of demand and generation scenarios, which may incur risk in real-time operation as well as driving high electricity prices. This work addresses the existing challenges facing independent system operators to provide reliable, competitively priced electricity to meet demand. An on-line security limit derivation (OLSLD) tool is suggested to improve both the real-time system security and the market efficiency; existing challenges from both technical and business process point of view in employing such a tool are discussed. As a benchmark system, the Ontario's electricity market is used in this paper to demonstrate the existing security requirements and potential gains to the market in employing OLSLD tool along with recommendations and guidelines in a successful implementation.
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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