Development of An Interval Chance-Constrained Mixed-Integer Linear Programming Model for Electric Power System Planning — A Case Study for the Province of Alberta, Canada
Bibliographic record
Abstract
Reducing carbon emissions from power systems is essential for meeting increasingly stringent decarbonization require- ments while maintaining reliable electricity supply and economic performance. This study develops an interval chance-constrained mixed-integer linear programming (ICM) model to maximize total system profit and support capacity expansion and generation planning under uncertainty. The proposed ICM framework integrates mixed-integer programming, chance-constrained programming, and interval linear programming to represent both risk preferences and interval-type uncertainties in key inputs, and it considers eight planned power generation technologies. The model generates optimal technology-specific capacity expansion plans and electricity generation strategies that satisfy end-user demand while complying with carbon dioxide (CO2) emission targets under three risk levels. The approach is demonstrated through a provincial-scale case study in Alberta, Canada, where uncertainties and risks are quantified and trade-offs among multiple system criteria are examined. Results indicate that the share of installed capacity for small modular reactors (SMRs) will increase rapidly, clean energy generation will rise to 66% of total electricity production, and total CO2 emissions will decrease by approximately 34%. The proposed framework provides decision-makers with a practical tool for optimizing provincial power systems and advancing long-term environmental and economic sustainability.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".