A multi period community energy system optimization model for Arctic and Northern communities considering both thermal and electric loads
Bibliographic record
Abstract
Abstract Remote communities across the Arctic continue to rely heavily on fossil fuels for energy sources, which is environmentally damaging and decreases energy security in these regions. Energy system planning models can help communities transition to renewable energy and displace the need for imported diesel. Many studies have developed generation expansion planning (GEP) models to make staged investment decisions. However, few models integrate both electricity and thermal energy needs. In this work, space heating via surface geothermal water is integrated directly into a mixed integer linear programming GEP optimization model, validated on a case study in Pilgrim Hot Springs, Alaska. It was found that a renewably-powered system consisting of wind, solar, and battery storage units was economically superior to a system with diesel, saving over $3457 per year in annualized costs. An analysis of generator capacity lumpiness revealed that a purely renewables system with a hypothetical 20 kW wind turbine (WT) could meet the energy needs of the community for an annualized cost of $13 525, a 24% decrease when compared to a system using a commercially available 100 kW WT. The recommended system met the expressed preferences of the case study community in achieving diesel independence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".