Optimizing the Implementation of Small Modular Reactors into Ontario’s Future Energy Mix
Bibliographic record
Abstract
This paper performs a detailed analysis of the optimized Ontario power mix under impending load and emissions constraints with the consideration of small modular reactor (SMR) deployment.The target of minimizing the total cost of the 2055 power mix while retaining real-world energy requirements was achieved using a semidynamic, recursive linear optimization model with hourly time resolution for the accurate consideration of wind and photovoltaic variable renewable energy.Utilizing IBM's ILOG CPLEX Optimization Studio's Flow Control method, dynamic factors such as forecasted demand growth, increasing capacity installations, learning curve applications, and reactor refurbishment and decommissioning schedules were applied to the modeling scenarios.Optimized scenarios have demonstrated that SMR-based capacity should play a vital role in the provincial energy mix in order to minimize cost while meeting emissions reduction goals and responding to increasing demand.Simulations show ideal cost reductions when approximately one-third of generated energy is produced by SMRs in the future energy mix and that the absence of SMRs may lead to up to 29% higher spending.Additional cases have considered the benefits of early SMR investment and direct SMR-CANDU cost comparisons.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".