Decarbonizing Alberta’s Oil Sands: Integrating Electrothermal Energy Storage and Renewable Energy to Reduce Greenhouse Gas Emissions from Steam Production
Bibliographic record
Abstract
Electrothermal energy storage (ETES) presents a promising pathway for decarbonizing steam production in Alberta’s oil sands. This study assesses the technical feasibility, emissions reduction potential, economic viability, and regulatory environment of ETES under five electricity supply scenarios, ranging from the current Alberta grid to 100% renewable energy. Scenario modelling shows that ETES is compatible with in-situ oil sands extraction and can reduce greenhouse gas emissions of steam-assisted gravity drainage operations by up to 91.8% when powered by 100% renewable electricity. Despite higher capital costs, ETES becomes cost-competitive when carbon credits under Alberta’s Technology Innovation and Emissions Reduction regulation and Canada’s Clean Fuel Regulations are stacked and applied. Renewable resource assessment shows sufficient wind and solar potential in key oil sands regions, supporting private-wire ETES configurations. However, policy gaps, particularly the lack of ETES-specific protocols and capped carbon pricing, constrain deployment. The findings support ETES as a technically and strategically viable industrial steam decarbonization solution, contingent on regulatory reforms and strategic integration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".