A Simplified Risk-Based Method for Short-Term Wind Power Commitment
Bibliographic record
Abstract
The installation of wind power systems is growing rapidly all over the world mainly due to increased environmental concerns regarding electricity generation and the perceived need to use renewable energy resources. The uncertain and intermittent nature of wind power has led to growing problems in integrating wind power in power systems as the wind power penetration continues to increase. One of the main challenges faced in power system operation with high wind penetration is to maintain the system reliability when committing an appropriate amount of power from a wind farm in the lead time considered. Commitment of wind power is a crucial task, which requires accurate wind power forecasting. Statistical methods employing time series model have been used to predict the short term wind power with reasonable accuracy. The short term (up to 4-6 hours) wind power is dependent upon the initial wind power and the wind site. Any future prediction contains a certain amount of risk. Short term wind power commitment is therefore also dependent upon the acceptable risk criterion, which is a managerial decision. This paper presents a conditional probabilistic method within a time series model to recognize the variability in wind, and to quantify the risk in wind power commitment during system operation. Complex methods that require significant amount of data are not readily applied in practical application. This paper presents a generalized and approximate risk based method that is relatively simple to apply, and therefore, should be useful to power system operators and wind farm owners to commit an appropriate amount of power in the next hour(s) based on the known initial wind power output.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".