A Factorial Fractional Chance-Constrained Programming Model for Regional Electricity Systems Management under GHG Emission Mitigation — A Case Study of Saskatchewan, Canada
Bibliographic record
Abstract
In this study, a factorial fractional chance-constrained programming model (FFCC) is developed for managing Saskatche- wan’s electricity systems under the pressure of greenhouse gas (GHG) emissions reduction. Through integrating multiple programming methods (i.e., linear fractional, mixed-integer linear, and chance-constrained) with factorial analysis into an optimization framework, FFCC could effectively (1) tackle multi-objective problems; (2) manage stochastic features of system parameters expressed as probability distributions and facilitate constraint-violation analysis; (3) reflect the impacts of various economic and environmental factors and their interactions on system response. Optimal electricity generation schemes, capacity expansion plans, and electricity import/export strate- gies under different policy scenarios and risk levels are explored with the objective of maximizing low-carbon power generation per unit of system cost. Results find that small modular nuclear reactor power would have the potential to replace fossil fuel-fired technologies and aid Saskatchewan in achieving net-zero carbon emissions by 2050. It is expected that the modelling results can support regional ef- forts in proposing effective power generation capacity expansion plans and relevant environmental policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".