Optimizing the Integration of Direct Air Capture of Carbon Dioxide into the Canadian Electricity System
Bibliographic record
Abstract
Canada has committed to become a net-zero emissions country by 2050, enshrined in legislation in the Net-Zero Emissions Accountability Act. Federal government modeling from Environment and Climate Change Canada estimates that direct air capture (DAC) of CO2 could be required to meet this goal, extracting up to 201 Megatonnes (Mt) of CO2 from the atmosphere in 2050. However, these projections come from a low-fidelity model of DAC, which does not optimize its operation within the larger energy system. To obtain a more accurate understanding of the potential role of DAC in achieving the net-zero goal, this thesis develops a high-fidelity, mixed integer linear optimization model of liquid-solvent DAC that improve its flexibility and heat integration. This high-fidelity model is integrated into a larger energy system model, to study optimal investment and operating strategies for DAC. Various enablers of DAC deployment are also studied, including policy options like carbon pricing and subsidies. The DAC model is utilized in the context of the energy system of Alberta, Canada. Results show that DAC plays a minimal role in achieving medium-term net-zero goal in the electricity sector. The optimum deployment and operation of DAC is to remove only 1.4 MtCO2/year, which is less than 1% of cumulative emissions in the associated scenario. Solid material storage using silos in the DAC process was found to be more economical than either energy storage in batteries or hydrogen production in improving DAC’s flexibility. Using solids storage, DAC operation is optimized, reducing the required DAC capacity by 18% and paving the way for 8% and 1.8% more wind and solar capacity, respectively. This research finds that only a small amount of DAC is needed to achieve a net-zero electricity system. In addition, renewable energy is indispensable in the transition, and their deployment would benefit from a graduated carbon price, which increases periodically, as it is currently formulated in Canada. Furthermore, DAC process flexibility with solids storage is crucial in enabling cooperation with the energy system. These results contribute to the fields of industrial decarbonization and net zero energy system modeling, finding efficient operating and deployment strategies for DAC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".