Wind farms as reactive power ancillary service providers - technical and economic issues
Bibliographic record
Abstract
This paper examines the possibility of providing reactive power support to the grid from wind farms (WFs) as a part of the ancillary service provisions. Detailed analysis of the WF capability curve is carried out considering maximum hourly variation of wind power from the forecasted value. Different cost components are identified, and subsequently, a generalized reactive power cost model is developed for wind turbine generators that can help the independent system operator (ISO) in managing the system and the grid efficiently. Apart from the fixed cost and the cost of loss components, a new method is proposed to calculate the opportunity cost component for a WF considering hourly wind variations. The Cigre 32-bus test system is used to demonstrate a case study showing the implementation of the developed model in short-term system operations. A finding is that higher wind speed prediction errors (a site with high degree of wind fluctuations) may lead to increased payments to the WFs for this service, mainly due to the increased lost opportunity cost (LOC) component. In a demonstrated case, it is found that 2340 $/h is paid to the WF as the LOC payment only, when the wind prediction error is 0.5 per unit (p.u.), whereas 54 $/h is the expected total payment to the WF when the prediction error is 0.2 p.u. for its reactive power service.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".