Techno-economic opportunities for integration of renewable energy into the Saskatchewan energy system using EnergyPLAN
Bibliographic record
Abstract
Many renewable energy opportunities exist but scientific gaps remain on the techno-economic feasibility of a transition, especially when systems are considered holistically across all energy sectors. The purpose of this study is to investigate the efficacy of Smart Energy Systems using renewable energy compared to carbon capture and storage technologies. These decarbonization strategies are investigated through a case study in Saskatchewan, Canada. The study uses EnergyPLAN for modelling the energy system and considers the transportation, heating, industrial and electrical sectors. The analysis demonstrates that a Smart Energy System and renewable energy is feasible and preferred based on energy system efficiency, carbon dioxide emissions and costs. A transition has been shown to reduce annual carbon dioxide emissions by 52 % and total annualized system costs by 20 % with further reductions achieved by increasing the capacity for interprovincial trading. • A Smart Energy System in Saskatchewan would make contributions to decarbonization. • The existing Saskatchewan energy system can integrate additional renewable energy. • Interprovincial cooperation can further optimise the energy system. • A reduction in grid stabilizing units enhances renewable energy supply. • A Smart Energy System can reduce CO 2 emissions by 52 % and system costs by 20 %.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".