Using Standard Deviation as a Measure of Increased Operational Reserve Requirement for Wind Power
Bibliographic record
Abstract
The variability inherent in wind power production will require increased flexibility in the power system, when a significant amount of load is covered with wind power. Standard deviation (σ) of variability in load and net load (load net of wind) has been used when estimating the effect of wind power on the short term reserves of the power system. This method is straightforward and easy to use when data on wind power and load exist. In this paper, the use of standard deviation as a measure of reserve requirement is studied. The confidence level given by ±3–6 times σ is compared to other means of deriving the extra reserve requirements over different operating time scales. Also taking into account the total variability of load and wind generation and only the unpredicted part of the variability of load and wind is compared. Using an exceedence level can provide an alternative approach to confidence level by standard deviation that provides the same level of risk. The results from US indicate that the number of σ that result in 99% exceedence in load following time scale is between 2.3–2.5 and the number of σ for 99.7% exceedence is 3.4. For regulation time scale the number of σ for 99.7 % exceedence is 5.6. The results from the Nordic countries indicate that the number of σ should be increased by 67–100% if better load predictability is taken into account (combining wind variability with load forecast errors).
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".