A dual-interval fractional energy systems optimization for Nova Scotia, Canada
Bibliographic record
Abstract
In real-world energy systems , uncertainties are diverse and complex. Simply turning multi-objective energy planning into a single objective often does not work. This approach relies on subjective or unrealistic assumptions and also overlooks the trade-offs among economic cost, energy efficiency, and environmental performance. In this study, we developed a dual-interval fractional energy systems programming (DFEP) method to optimize energy systems under multiple objectives and uncertainties, particularly in the form of interval- and dual-interval-fractional representations. The DFEP framework integrated mixed-integer linear programming with fractional and dual-interval programming. This allowed it to handle uncertain parameters expressed as dual-interval boundaries and trade-offs between costs and benefits. This method was applied to Nova Scotia's energy system in Canada. The goal was to support energy planning under greenhouse gas (GHG) emission policies. Eight scenarios were analyzed to evaluate electricity generation , primary energy consumption , facilities expansion, and imported electricity. The results suggested that renewable energy and imported electricity can effectively replace fossil fuels , leading to a significant reduction in GHG emissions. A higher share of clean electricity generation and subsequent decline in fossil fuel use demonstrated the feasibility of achieving zero GHG emissions in electricity generation by 2030. Additionally, current coal-fired and oil-fired plant capacities were sufficient to meet future energy demand. These findings provide valuable insights and robust support for decision-makers in developing sustainable energy policies and achieving GHG mitigation targets.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".