Wind power modelling and the determination of capacity credit in an electric power system
Bibliographic record
Abstract
Wind is an important energy source and is regarded as a valuable alternative to traditional electric power-generating sources. There is an increasing interest in the development and use of wind energy as a substitute for more conventional energy because of its high potential and minimum impact on the environment. Generating capacity from wind power behaves quite differently than that from more conventional generating sources, as the wind is highly variable and is both site and terrain specific. These conditions dictate the need to develop suitable models and procedures to assess the reliability implications associated with integrating wind power in electric power systems. This paper presents an approach to modelling wind power in generating-capacity reliability studies using an autoregressive moving average (ARMA) time series. The technique is illustrated by application to a representative test system using wind data from a site in Saskatchewan, Canada. The test system is used to illustrate the effect on the system risk of adding increasing amounts of wind capacity to a conventional generating system. The risk is assessed using the loss of load expectation and loss of energy expectation indices. The generating capacity credit attributable to wind power is expressed in terms of the increase in system peak load-carrying capability at the criterion risk level. These analyses are extended to consider multiple wind sites with dependent and independent wind regimes.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".