Determination of an Accurate Dynamic Security Constraint with Applications in Market Dispatch
Bibliographic record
Abstract
This paper proposes the inclusion of transient stability constraints in generation dispatch algorithms used in electricity markets to ensure dynamic security. The proposed security constraint is derived by applying a non-linear surface fitting technique to a database generated off-line. The technique used has two key features that enables the derivation of an accurate transient stability constraint with a relatively short computing time compared to other comparable methods. One feature is that it uses a linear estimation technique to estimate a non-linear function by means of a non-linear transformation. The second important feature is that it employs an implicit technique to gain significant reduction in computing burden. The potential of the proposed method is demonstrated using the New England 39 bus system and a larger power system with 470 buses. The security constraint derived for the New England 39 bus system is used in an optimal power flow (OPF) program for market clearance. The locational marginal prices (LMP) obtained from the OPF are further analyzed to determine the component of LMP due to dynamic security
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".