Long-Term Statistical Assessment of Frequency Regulation Reserves Policies in the Québec Interconnection
Bibliographic record
Abstract
Since 2003, Hydro-Québec Distribution has launched three calls for tenders to procure 3500 MW of wind power. This will bring the projected wind capacity in the Québec Interconnection to 4000 MW in 2015, for about 5% energy and 35% maximum hourly penetration rates. This paper presents two statistical approaches for assessing the additional frequency regulation reserves needed to reliably integrate 3000 MW of wind energy from 23 plants, for which minute/minute output time series were developed over an 11-year period and synchronized with historical load patterns. After description of a generalized dispatch-based approach for separating automatic generation control (AGC) and load-following components, a risk-based allocation method inspired from the BPA 2010 rate case is presented in detail, and then compared with the n × σ approach using the same data set. While the risk-based allocation forecasted AGC and load-following increasing by 1.8% and 20.6% of installed wind capacity, the n × σ criterion resulted in much lower incremental reserves requirements with only 0.4% and 6.8% increases of the AGC (n=4) and load-following (n=2), respectively. In a companion paper, the power grid operations-based simulation approach is presented and its results compared with those of this paper.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".