Hydrogen microgrids to facilitate the clean energy transition in remote, northern communities
Bibliographic record
Abstract
Most remote and northern communities rely on diesel for their electrical and thermal energy needs. Communities and governments are working toward diesel exit strategies, but the role of hydrogen technologies has not been explored. These could serve both electrical and thermal demand, reduce emissions, and enhance energy security and community ownership. Here, we determine the installed capacities, costs, hydrogen storage needs, and water resource requirements of hydrogen microgrids across a large, diverse sample of communities. We also compare the cost of hydrogen microgrids to that of diesel microgrids. Our results optimize resource deployment, demonstrate how sub-components must operate to serve both demand types, and yield insights on storage and resource needs. We find that hydrogen microgrids are cheaper, in levelized cost terms, than diesel systems in 28 of 37 communities investigated; if wind power capital costs escalate to CAD 20,000/kW, as recently seen in one project, only 3 of the 37 communities net hydrogen microgrids that are cheaper than diesel variants. Hydrogen storage plays a large role in maintaining reliability and reducing cost—both it and water needs are modest. The former can be met with current technologies. • Remote and northern communities rely on diesel for electricity and heat. • Governments and communities are pursuing low-carbon “diesel exit” strategies. • An optimization model of renewable+hydrogen microgrids is applied to 37 communities. • Extent of investment depends on population, climate, and quality of wind resources. • These microgrids are often cheaper than diesel, making them a viable option.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".