Developing, implementing and testing up and down regulation to provide AGC from a 10 MW wind farm during varying wind conditions
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
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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