PV Utilization Analysis for a Canadian Small Arctic PV-Diesel Hybrid Microgrid
Bibliographic record
Abstract
Canadian remote arctic communities are mostly supplied electricity by diesel generators. The electricity price in these communities is high due mostly to the transportation cost of the diesel fuel to these remote locations. A large portion of the financial budget from the government or local community is allocated to cover the electricity cost. Diesel power plants are also major emitters of greenhouse gases (GHGs). The annual solar photovoltaic (PV) potential in the Canadian arctic region ranges from 850 to 1150 kWh/kWp. Therefore, a significant portion of the community energy requirement can be supplied by PV systems thus reducing diesel fuel consumption and associated GHG emissions. This paper presents the impact of PV integration on the system annual energy performance at various levels of PV penetration. The modelling of a typical small arctic PV-Diesel hybrid microgrid is addressed with the specifications of a small representative community. The PV utilization analysis establishes the low PV penetration regime up to 40% of peak load. Then two technical alternatives are presented that allow medium PV penetration up to 70% of peak load with associated PV energy contribution up to 23% of the community energy requirement.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".