Green hydrogen energy storage to support microgrid Arctic communities on the pathway to 100% renewable energy
Bibliographic record
Abstract
Remote Arctic communities in Canada face significant challenges in transitioning from diesel-dependent energy systems to renewable alternatives. While wind, solar, and battery technologies enable renewable energy penetration (REP) up to approximately 60%, exceeding this level requires long-duration energy storage. This study evaluates the potential of green hydrogen energy storage to support Arctic microgrids on the pathway to 100% REP using an optimization model and a community case study. This evaluated the least-cost designs for REP targets from 50% to 100%, comparing scenarios with and without hydrogen energy storage. The analysis incorporated operational constraints including minimum load ratio, ramping limitations, and compression requirements, as well as a cost sensitivity analysis. Results demonstrate that hydrogen energy storage becomes economically attractive above 65% REP, providing substantial cost savings at high REP by reducing renewable generation over-sizing requirements. However, technological limitations, particularly minimum loading of fuel cells, prevent cost-effective achievement of 100% REP. Beyond economic benefits, the study identifies practical implementation challenges including harsh environmental conditions, logistical constraints, and local capacity. This work provides novel insights into hydrogen's potential role in Arctic microgrid decarbonization and identifies key technological and practical barriers that must be addressed for successful its implementation.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".