Bibliographic record
Abstract
Rising uncertainty in power systems due to different system and operational requirement has led to increasing risks in the system operation. There are growing concerns with the widely used methods, such as the N-1 criterion, to determine operating reserve requirement during unit commitment, and the economic load dispatch method to allocate regulating margin to respond to disturbances. These deterministic methods do not consider the stochastic nature of power systems and are often inadequate to maintain the required operating reliability. This thesis introduces a comprehensive operating risk index, designated as the committed generators’ response risk (CGRR) that can be used to maintain a specified level of operating reliability. An analytical probabilistic method to evaluate the CGRR is presented and validated using a Monte Carlo simulation technique. An application of the new index and the methodology is illustrated using the IEEE RTS system. The evaluation of CGRR provides a comprehensive operating risk of the scheduled generation until further assistance is available to the system and, therefore, helps operators in decision making for unit commitment and dispatch of the generating units to meet the projected load in the short future time. There is an increasing trend of wind energy integration to the existing power system for its environmental benefits. But the wind power can create more challenges to the modern power systems due to possible wind disturbances. To appropriately quantify the wind variability, a short term wind power disturbance model is proposed by utilizing conditional probability approach. The information on operating risk of a wind connected power system can help operators to act prudently while operating a power system in a reliable manner. The developed CGRR based operating strategies can be used to continuously track the system risk level and take necessary actions before the undesirable consequences occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".