The Potential for Hydrogen in Alberta: A Case Study of Combusting Hydrogen Gas in the Electricity Sector
Bibliographic record
Abstract
Electricity forms Alberta’s backbone, enabling progress, development, and an environment where businesses can thrive. Unlike other provinces with significant hydro resources, Alberta’s electricity industry is dominated by fossil fuels. One option to achieve lower carbon emissions is to replace natural gas with hydrogen as it does not emit any carbon dioxide when combusted. Moving to such a fuel will require technical compatibility, cost competitiveness, and the right incentive structure to encourage its use in the sector. Within this case study, I examine the current methods for producing hydrogen, discuss infrastructure compatibility, and then consider the current composition of Alberta’s large-scale electricity generators classified by make and model. For the most prevalent generators, I conducted a review of the potential technical capability in converting their fuel stocks to incorporate hydrogen as declared by the manufacturers and through similar conversion projects around the world. Furthermore, I analyze the marginal cost for the above generators to identify whether hydrogen is economic at current prices to incorporate into generator fuel mixtures. With current production methods and cost functions, hydrogen remains cost-prohibitive compared to its alternative of natural gas, whether for combustion turbines or steam boilers. Producing hydrogen from steam methane reforming would increase marginal fuel costs by 1.7 times compared to natural gas, and 1.36 times compared to coal. To successfully incentivize hydrogen, the carbon levy in Alberta would need to exceed $130 for production and carbon capture to become cost-effective given the natural gas prices over the last 10 years. If using steam methane reforming, carbon capture would be a required part of the hydrogen production process, else using hydrogen would be a more carbon-intensive option than using natural gas. Alternatively, as worldwide hydrogen production increases the sector may benefit from economies of scale from new technologies or methods making hydrogen a competitive option. This paper provides a study of the potential for hydrogen to power the electricity industry and further discusses some of the present and potential hurdles for policymakers to consider for hydrogen to become a successful fuel to power the sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".