Power System Security in Market Clearing and Dispatch Mechanisms
Bibliographic record
Abstract
This paper presents and discusses some typical market clearing and dispatch mechanisms based on security-constrained (SC) optimal power flow (OPF) techniques that are currently being utilized in various jurisdictions throughout the world, concentrating on how system security is accounted for in these auction systems and the effect that this has on the system dispatch schedules and energy prices. The Ontario market is used as an example to illustrate the practical application of these security-constrained clearing and dispatch mechanisms. The need for a better representation of system security in the existent auction systems is also discussed, based on the fact that "unexpected" dispatch solutions, resulting from diverse bidding patterns and the mechanisms used to clear and dispatch the market, may render ineffective the predetermined system security limits typically used in most auction models, leading to insecure operating conditions and/or unnecessary high prices associated with an unrealistic modeling of system congestion. The latter is illustrated using a simple 6 bus test system, comparing the dispatch levels and prices, as well as the general system operating conditions, obtained from a SC-OPF auction mechanism with respect to those obtained using a voltage-stability-constrained technique, which better represents system 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".