Allocation of Ontario’s Surplus Electricity to Different Power-to-Gas Applications
Bibliographic record
Abstract
Power-to-Gas (PtG) is a potential means of managing intermittent and weather-dependent renewable energies to create a storable chemical energy form. Power-to-Gas is not only a storage technology; its role can be extended to many other applications including energy distribution, transportation, and industrial use. This study quantifies the hydrogen volumes upon utilizing Ontario, Canada’s surplus electricity baseload and explores the allocation of the hydrogen produced to four Power-to-Gas pathways in terms of economic and environmental benefits, focusing on the following Power-to-Gas pathways: Power-to-Gas to mobility fuel, Power-to-Gas to industry, Power-to-Gas to natural gas pipelines for use as hydrogen-enriched natural gas, and Power-to-Gas to renewable natural gas (i.e., Methanation). The study shows that the Power-to-Gas to mobility fuel pathway has the potential to be implemented. Utilization of hydrogen for refueling light-duty vehicles is a profitable business case with an average positive net present value of $4.5 billions, five years payback time, and 20% internal rate of return. Moreover, this PtG pathway promises a potential 2,215,916 tonnes of CO2 reduction from road travel.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".