Wind power modelling and the determination of capacity credit in an electric power system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it