Analysis of lifecycle GHG emissions reduction in grid-connected water electrolysers: The role of dynamic operating strategies and emission factors
Bibliographic record
Abstract
Low-carbon hydrogen production through water electrolysis offers a significant opportunity to mitigate lifecycle greenhouse gas (GHG) emissions, particularly when incorporating often overlooked dynamic grid emission factors (EFs). This study introduces innovative dynamic operating strategies that surpass traditional static approaches in reducing emissions. Using a 20 MW proton exchange membrane (PEM) electrolyser system, we examine configurations powered by grid electricity alone or hybridized with on-site wind energy in Ontario, Canada. Continuous full-load operation yields a hydrogen carbon intensity (CI) of 4.52 kg CO 2e /kg H 2 for grid-only case. However, implementing proposed dynamic strategies, while maintaining a minimum of 5000 full load hours (FLHs), reduces the CI to as low as 3.37 kg CO 2e /kg H 2 . Hybrid setups with wind integration further decrease GHG emissions, achieving CI values down to the range of 0.22–3.17 kg CO 2e /kg H 2 . These findings underscore the potential impact of dynamic operating strategies and consideration of grid dynamic EFs in reducing emissions of electrolytic hydrogen production compared to constant full-load operations, offering a promising path for low-carbon hydrogen production and enhancing sustainability of global hydrogen energy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".