Techno-Economic Assessment of the Need for Bulk Energy Storage in Low-Carbon Electricity Systems With a Focus on Compressed Air Storage (CAES)
Bibliographic record
Abstract
Increasing electrification of the economy while decarbonizing the electricity supply is among the most effective strategies for cutting greenhouse gas (GHG) emissions in order to abate climate change. This thesis offers insights into the role of bulk energy storage (BES) systems to cut GHG emissions from the electricity sector.\nWind and solar energies can supply large volumes of low-carbon electricity. Nevertheless, large penetration of these resources poses serious reliability concerns to the grid, mainly because of their intermittency. This thesis evaluates the performance of BES systems – especially compressed air energy storage (CAES) technology – for integration of wind energy from engineering and economic aspects.\nAnalytical thermodynamic analysis of Distributed CAES (D CAES) and Adiabatic CAES (A CAES) suggest high roundtrip storage efficiencies (~80% and 70%) compared to conventional CAES (~50%). Using hydrogen to fuel CAES plants – instead of natural gas – yields a low overall efficiency (~35%), despite its negligible GHG emissions.\nThe techno-economic study of D CAES shows that exporting compression heat to low-temperature loads (e.g. space heating) can enhance both the economic and emissions performance of compressed air storage plants. A case study for Alberta, Canada reveals that the abatement cost of replacing a conventional CAES with D CAES plant practicing electricity arbitrage can be negative (-$40 per tCO2e, when the heat load is 50 km away from the air storage site).\nA green-field simulation finds that reducing the capital cost of BES – even drastically below current levels – does not substantially impact the cost of low-carbon electricity. At a 70% reduction in the GHG emissions intensity of the grid, gas turbines remain three times more cost-efficient in managing the wind variability compared to BES (in the best case and with a 15-minute resolution). Wind and solar thus, do not need to wait for availability of cheap BES systems to cost-effectively decarbonize the grid. The prospects of A CAES seem to be stronger compared to other BES systems due to its low energy-specific capital cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".