Identifying optimal geographic locations for hybrid concentrated solar biomass (HCSB) power plants in Alberta and Ontario, Canada
Bibliographic record
Abstract
Solar thermal and biomass hybridization combine two energy sources that complement one another seasonally and diurnally, overcoming their respective disadvantages. In the current research, the feasibility of locating a new medium capacity (5–150 Mwe) hybrid concentrated solar biomass power plant is investigated in Alberta and Ontario. To address the above-mentioned goal, three critical criteria are considered to find the most suitable region. The amount of direct normal irradiation and the biomass feedstock in 50 km and 100 km radii are analyzed in different regions in Ontario and Alberta. The third factor is the distance to the substations. According to the results, although it is difficult to justify developing a hybrid concentrated solar biomass power plant in Ontario, at least five locations in Alberta meet the aforementioned criteria. By concentrating on substations located near natural gas power plants, Calgary, with four natural gas power plants, can be regarded as the most suitable region. According to the findings, we identified that by establishing a 100 Mwe hybrid concentrated solar biomass power plant that requires 359,478 tons of forestry biomass and costs between 3.7 and 4.9 mCAD/Mwe, about 5% of Calgary’s total electricity consumption would be met while reducing CO2 emissions by 32 tons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".