Impact of direct air capture process flexibility and response to ambient conditions in net-zero transition of the power grid
Bibliographic record
Abstract
Recent studies show that the cost of transitioning the power grid to a net-zero system could be reduced with the integration of direct air capture (DAC) of carbon dioxide as part of the portfolio of technologies. However, existing capacity expansion studies that model DAC assume that it has a constant capture rate, ignoring the ambient environmental conditions that are known to affect the DAC capture rate as well as geographical location. Furthermore, there are currently no studies that endogenously model DAC flexibility, especially the value of load-shifting in such a large-scale industrial process in capacity expansion optimization. This study develops a capacity expansion optimization model that integrates more realistic data on DAC’s response to ambient environmental conditions as well as DAC process flexibility. Results show that ignoring the impact of ambient environmental conditions leads to underestimation of the required cost, DAC capacity and renewable energy capacity to meet the net-zero goal. It is shown that when DAC capture rate data that has been computed as a function of ambient conditions is used, about 22.2%, 2.5% and 1.9% more DAC, wind and solar capacity, respectively, is required to meet net-zero requirements, relative to the often assumed 90% capture rate. Moreover, increasing the operational flexibility of DAC using material storage in silos was found to lower the cost of generation capacity expansion by lowering the DAC and renewable energy capacity needed to meet the net-zero target. These findings will be useful in improving the accuracy of net-zero transition plans that are focused on climate change mitigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".