Inexact Credibility-Constrained Programming Approach for Electricity Planning in Ontario, Canada
Bibliographic record
Abstract
Under current changing climatic conditions, there has been a growing interest in green energy to mitigate carbon emissions. As such, proper management and planning of electricity are essential to mitigating climate change. This paper presents a hybrid inexact credibility constraint programming (ICCP) model for planning and optimization in the electricity sector for Ontario, Canada. The model considers the costs and emissions of electricity generated from six sources over a planning horizon of 30 years, minimizing system cost while meeting provincial emission goals. The ICCP method addresses uncertainties by transforming fuzzy variables into crisp equivalents with credibility levels, allowing decision-makers to address uncertainties in planning by tackling uncertainties as intervals through an interactive two-step algorithm. This model was applied to electricity planning in Ontario to address uncertainties due to demand predictions, technological advancements, and shifting energy consumption. The cap-and-trade program was compared to the federal carbon pricing backstop program, and the results over the planning horizon were similar. Through this model, expansion options to address future demands were also compared to minimize emissions while meeting electricity demand.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".