The Viability of Green Hydrogen for Electric Power Generation: Evaluating Current Practicability and Future Demand
Bibliographic record
Abstract
This study investigates the feasibility of green hydrogen as an alternative to natural gas for power generation. In doing so, it contributes to the broader discourse surrounding hydrogen’s potential role in the transition of the energy sector. Our case study is of Ontario, Canada, where natural gas serves as the sole remaining carbon-emitting energy source for the generation of electricity. Through this, we present a practical reference and methodology that energy planners, policymakers, and researchers can use to analyze fuel consumption patterns and their costs. Our research involves estimating the volume of hydrogen required to support the conversion of natural gas-powered turbines. At present, electrical power in Ontario generated by natural gas may reach 39 TWh annually by 2035. Our findings suggest that the cost of hydrogen to generate that volume of electricity will range between USD 1.8 billion and USD 23.2 billion, contingent upon turbine efficiency and fluctuations in hydrogen prices. Moreover, if hydrogen prices remain elevated (up to USD 8/kg), the annual premium for hydrogen-generated electricity compared to natural gas could reach USD 20.604 billion, a significant deterrence for energy planners in Ontario from adopting hydrogen at scale. Thus, the added costs of hydrogen, along with challenges related to infrastructure requirements, safety, and technological considerations, render a potential transition to hydrogen, and to green hydrogen specifically, a complex undertaking. Ultimately, insights derived here enhance understanding of hydrogen’s potential within the context of power generation and may be applicable to other regions considering similar transitions toward hydrogen-based energy systems.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".