A power storage system planning model for the Wolfe Island wind farm
Bibliographic record
Abstract
As one of the alternatives to conventional energy sources, wind power is a fast-growing renewable technology. The intermittent characteristic of wind speed leads to unstable wind power production; therefore, a storage system is typically needed at wind power farms to stabilize the power output. This project aims to develop a power storage system planning model to optimize the power transfer between wind turbines and storage devices on an hourly basis to stabilize power output and maximize annual net earnings. The developed model utilizes the dynamic programming technique, and it is applied to a case study of the Wolfe Island Wind Farm in Ontario, Canada, for the year of 2020. Two scenarios with different power output tolerance considerations (i.e., tolerance ranges based on 6- and 8-hour subperiods) are analyzed to compare the fluctuations of power output and the total net earnings. The optimized number of storage devices is also discussed. The developed model can provide technical support for the design and operation of power storage systems at wind power farms.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".