Developing, implementing and testing up and down regulation to provide AGC from a 10 MW wind farm during varying wind conditions
Bibliographic record
Abstract
With increasing levels of electrical energy generated by intermittent sources such as wind turbines, their participation in grid ancillary services is becoming a necessity. Typically, all generated energy from variable generators is absorbed by the electric grid and balancing is left to traditional generators. Wind turbine technology has matured to the level where a large wind generator is capable of providing ancillary services such as up- and down-active power regulation (secondary frequency regulation). The up-regulation capacity of a variable generator is constrained primarily by external factors such as the prevailing wind speed in the case of a wind turbine. This work uses the Wind Energy Institute of Canada's (WEICan) 10 MW Wind R&D Park (Type 5 generators) in Prince Edward Island, Canada, to test and evaluate a simple algorithm to provide up- and down-regulation services from a wind park. The developed algorithm uses a 10-minute averaged wind speed to estimate the available park generation potential. A fixed power curtailment is applied to provide room for up-regulation. An historical, external AGC signal is then applied to the wind park's active power set-point and the resulting park performance is evaluated. Results of the 4.5 hour test prove the technical capability of the wind farm in participating in the regulation market. A performance score of 64 % was calculated according to the PJM method, averaged across the test duration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".