Feasibility Evaluation of a Hybrid, Deep Geothermal Plant in Remote Northern Areas of Canada
Bibliographic record
Abstract
Summary Geothermal plants, whether stand-alone or hybrid, are clean and sustainable technologies able to provide the stable baseload energy (heat and power) required by remote, rural, or urban areas. This research evaluates the technical and economic feasibility of using a hybrid, deep geothermal plant to supply the power requirements of Fort Liard (FL), an Indigenous, remote and off-grid community in the Northwest Territories (NT), Canada, which heavily relies on fossil fuel. The designed power plant has two deep wells (for extraction and injection of brine), a geothermal one-stage binary cycle system, solar and wind systems, plus a battery bank and a backup diesel facility. The results demonstrate that the hybrid, deep geothermal plant can provide stable baseload power to fulfill FL’s yearly energy requirements over a 30-year lifetime by scheduling to run only the wind and solar systems for six months (April to September) when there are favourable weather conditions and operate the wind, solar, and geothermal systems together for the rest of the year. The cost of energy (COE) for the designed power plant is ∼0.35 CAD$/kWh, while FL’s current diesel-reliant installation, excluding the northern diesel subsidy, incurs a COE of ∼0.70 CAD$/kWh, which is double. The economic evaluation also indicates that the period of return on investment (ROI) for the hybrid, deep geothermal plant is calculated to be ∼18 years.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".