Inclusion of Wind Generation Modeling into the Conventional Generation Adequacy Evaluation
Bibliographic record
Abstract
Wind energy has become a significant portion of power generation resources, consequently its variability and uncertainty introduces various challenges for both the operation and planning of power systems. One of the great challenges of integrating wind energy in power systems can be seen from the reliability assessment perspective. Indeed, there is an ongoing recognized need to study the contribution of wind generation to overall system reliability and to ensure the adequacy of generation capacity. With respect to the evaluation of the reliability of power systems incorporating wind energy, a variety of criteria and techniques have been developed over the years. This paper is dedicated to reviewing the literature pertaining to generating system adequacy assessment in general and with regard to wind energy in particular. This paper firstly reviews the concepts and related aspects of generating system adequacy assessment, it also includes detailed description of the involved elements and the available widely commonly-used techniques. Then, it discusses the main issues arising when implementing wind generation into the adequacy assessment of generating systems. Moreover, the paper surveys the previously reported works that have proposed to involve wind generation into adequacy assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".