Reliability evaluation of bulk power system considering compressed air energy storage
Bibliographic record
Abstract
The integration of large-scale energy storage systems (ESSs) have been identified as a viable option to mitigate the adverse effects of renewable energy sources (RES) on the power system operation and reliability. Currently, compressed air energy storage (CAES) is one of the two large-scale energy storage technologies with low capital and operational costs. This paper presents a method to integrate a new CAES reliability model in the bulk power system reliability evaluation and investigates quantitative benefits derived from the CAES. A state-duration sampling method is adopted for the reliability evaluation. A detailed reliability model of the CAES that considers its actual operating mechanism is first developed. Each system contingency state is then analyzed using a unit commitment (UC) method instead of hourly optimal power flow (OPF). This ensures that the inter-temporal constraints introduced by the CAES, such as its state-of-charge (SOC), are included in the analysis. Case studies are performed on a six-bus test system containing a wind farm and a CAES. The results indicate that the CAES can improve the overall reliability of the system. In particular, the reliability indices of the bus where the CAES is connected show the greatest improvement.
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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) | 0.000 | 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".