Integrating compressed air energy storage with wind energy system – A review
Bibliographic record
Abstract
- With an increasing capacity of wind energy globally, wind-driven Compressed Air Energy Storage (CAES) technology has gained significant momentum in recent years. However, unlike traditional CAES systems, a wind-driven CAES system operates with more frequent fluctuations due to the intermittent nature of wind power. Consequently, the design and operation of wind-driven CAES systems must address such a complex and dynamic behavior. Considering the growing interest in wind-driven CAES systems, a comprehensive and systematic review of the existing literature on their design and operational characteristics is appealing. Therefore, this study aims at filling this research gap by examining the existing literature on the configuration, sizing, and operation/scheduling of wind-driven CAES systems. This review also aims at highlighting the underlying assumptions and methodologies employed in previous studies on wind-driven CAES systems. Given the challenges faced by several CAES projects, which were discontinued due to geological and economic constraints, it is imperative to conduct comprehensive feasibility studies to support the development and implementation of wind-driven CAES systems. Additionally, there is a growing necessity to explore the feasibility of small-scale CAES systems, focusing on their potential to bolster energy security and resilience for small or remote communities in distributed energy systems. By examining the existing literature and highlighting the gaps in current research, a number of insights are provided serving as foundations for future investigations in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".