Green Hydrogen from Wind Energy for Long-Duration Energy Storage in Alberta
Bibliographic record
Abstract
With continued growth in global carbon emissions, much of the developed world is curtailing its dependence on fossil fuels. Broad decarbonization will not come from a single source, but rather a combination of policy, technology, and innovation. Wind power has seen significant development in Canada, having over 6,700 operational turbines installed over the last decade. Increased investment into wind energy has lead to improvements in the technology, with wind turbines roughly doubling in height since the early 1990’s. In recent years, hydrogen has seen a resurgence in popularity as a low carbon energy carrier. Pairing existing wind farms with energy storage, such as hydrogen, could increase their electricity revenue through price arbitrage, or generate new revenue through selling the hydrogen. This work examines the opportunity to generate green hydrogen in Alberta using a wind-hydrogen hybrid plant. To do so, Alberta’s electricity market operations were forecast to 2030 using commercial market simulation software, Aurora, including hourly prices and unit dispatch. To better simulate price spikes, electricity price perturbations, achieved through time series decomposition of historic price signals, are administered as random shocks to the hourly price forecasts. Wind-hydrogen hybrid plant characteristics, capacity and operating schedule, are optimized using linear programming. Results of this work show some potential for wind-generated green hydrogen to be cost competitive with steam-methane reforming in the next decade, with some scenarios achieving levelized cost of green hydrogen below $2/kg. Regardless of market conditions, optimal electrolyser sizing is contingent on achieving a capacity factor between 40% and 60%. At current costs, energy arbitrage through hydrogen fuel cells is not financially viable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".