The role of long-term hydrogen storage in decarbonizing remote communities in Canada: An optimization framework with economic, environmental and social objectives
Bibliographic record
Abstract
Many small Canadian communities lack access to electricity grids, relying instead on costly and polluting diesel generators, despite the local availability of renewable energies like solar and wind. The intermittent nature of these sources limits reliable power supply; thus, hydrogen is proposed as a cost-effective and eco-friendly long-term energy storage solution. However, it remains uncertain whether hydrogen storage can significantly contribute to a 100% renewable energy system (100RES), given the diverse characteristics of these communities. Additionally, the potential for fully renewable infrastructure to reduce costs, mitigate adverse environmental impacts, and enhance social impact is still unclear. A multi-period optimization model that balances economic, environmental, and social objectives to determine the optimal configuration of 100RESs for isolated communities is introduced and utilized to evaluate hydrogen as an energy storage solution to seasonal fluctuations. By identifying the best combinations of technologies tailored to local conditions and priorities, this study offers valuable insights for policymakers, supporting the transition to sustainable energy and achieving national climate goals. The results demonstrate that hydrogen could serve as an excellent long-term energy storage option to address energy shortages during the winter. Different combinations and sizes of energy generation and storage technologies are selected based on the characteristics of each community. For instance, a community in the northern territories with high wind speeds, low solar radiation, extremely low temperatures, and limited biomass resources should optimally rely on wind turbines to meet 80.7% of its total energy demand, resulting in a 62.0% cost reduction and a 49.5% decrease in environmental impact compared to the existing diesel-based system. By 2050, all communities are projected to reduce energy costs per capita, with northern territories achieving 33% and coastal areas achieving 55% cost reductions, eventually leading to the utilization of hydrogen as the main energy storage medium. • Develops a tri-objective model for 100% renewable energy in remote communities • Hydrogen storage is key for seasonal balancing in all modelled communities • Optimal renewable mix varies based on local climate, resources, and geography • By 2050, renewables reduce per capita energy costs by 33%–55% across communities • Findings support clean energy policies for remote areas in Canada and worldwide.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".