Considering Load-Carrying Capability and Wind Speed Correlation of WECS in Generation Adequacy Assessment
Bibliographic record
Abstract
Wind power is an intermittent energy source that behaves quite differently from conventional energy sources. The reliability impact of this highly variable energy source is an important aspect that needs to be assessed as wind power penetration becomes increasingly significant. Generation adequacy assessment including wind energy conversion systems (WECS) at multiple locations is described in this paper. Effective load-carrying capabilities (ELCC) obtained using the loss of load expectation (LOLE) and the loss of load frequency (LOLF) for a power system containing WECS are illustrated and compared. The results show that ELCC obtained using the LOLF and obtained using the LOLE for WECS can be considerably different, while they are similar for a conventional generating unit. The impact on the system reliability indices of wind speed correlation between two wind farms is also examined. The studies show that the degree of wind speed correlation between two wind farms has a considerable impact on the resulting reliability indices. The sequential Monte Carlo simulation approach is used as this methodology can facilitate a time series modeling of wind speeds, and also provides accurate frequency and duration assessments. An autoregressive moving average time series model is used in this study to simulate hourly wind speeds
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".