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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".