Compressed air energy storage with waste heat export: An Alberta case study
Bibliographic record
Abstract
Interest in compressed air energy storage (CAES) technology has been renewed driven by the need to manage variability form rapidly growing wind and solar capacity. Distributed CAES (D-CAES) design aims to improve the efficiency of conventional CAES through locating the compressor near concentrated heating loads so capturing additional revenue through sales of compression waste heat. A pipeline transports compressed air to the storage facility and expander, co-located at some distance from the compressor. The economics of CAES are strongly dependant on electricity and gas markets in which they are embedded. As a case study, we evaluated the economics of two hypothetical merchant CAES and D-CAES facilities performing energy arbitrage in Alberta, Canada using market data from 2002 to 2011. The annual profit of the D-CAES plant was $1.3 million more on average at a distance of 50 km between the heat load and air storage sites. Superior economic and environmental performance of D-CAES led to a negative abatement cost of −$40/tCO2e. We performed a suite of sensitivity analyses to evaluate the impact of size of heat load, size of air storage, ratio of expander to compressor size, and length of pipeline on the economic feasibility of D-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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".