Reliability and Environmental Benefits with Market Operation of Compressed Air Energy Storage in a Wind Integrated Power System
Bibliographic record
Abstract
Energy storage systems are receiving considerable attention as potential means to exploit the benefits from extensive renewable energy growth in electric power systems by absorbing the variability of these intermittent generation sources. This paper focuses on the compressed air energy storage (CAES) which has high potential for grid-scale application. A hybrid approach is proposed which embeds a Monte-Carlo simulation (MCS) method in an analytical technique to develop a suitable reliability model of the CAES. The MCS technique is used to sequentially model the state of charge incorporating the important dependent variables. The analytical technique employs a period analysis utilizing suitable sub-periods to maintain the diurnal and seasonal correlation of the renewable resource, system load and the state of charge of the CAES and quantitatively assess the system adequacy and wind energy usage. The CAES model incorporates diurnal energy arbitrage for profit making. The proposed model is applied to a test system to investigate the economic and reliability benefits of CAES as well as its contribution in facilitating wind integration during different operating scenarios. The conclusions drawn from the study results provide valuable information to help utilities and policy makers in arriving at effective and efficient policies for planning and operation of large-scale energy storage, such as the CAES.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".