A Dual-Uncertainty Two-Stage Fractional Programming Model for Reginal Power Systems in the Province of Ontario, Canada
Bibliographic record
Abstract
This study proposed a dual-uncertainty two-stage fractional power system management (DUTSF-PSM) model to deal with uncertainties and dual objectives in the power management system of Ontario. This model integrates interval linear programming (ILP), chance-constrained programming (CCP), mixed-integer linear programming (MILP), and two-stage stochastic programming (TSP) methods into the framework of a linear fractional programming (LFP) model. Two-objective issues and capacity expansion schemes under multiple uncertainties can be addressed by the DUTSF-PSM model. Economic and environmental elements are considered in the objective function of the DUTSF-PSM model at the same time in order to get maximal system benefit with minimum environmental influence. This model can tackle effectively the tradeoff between the economic and environmental objectives. Through the DUTSF-PSM model for power systems in Ontario, the maximal system efficiency based on the least environmental influence under different levels of constraint-violation probabilities can be achieved. The results indicate that both hydroelectric and wind power have development potential when the economic and environmental factors are considered in the objective function at the same time. In addition, the results of factorial analyses reflected that the effect of CO2 emission of each power generation technology on the system revenue is most significant among the chosen three factors.
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 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 | 0.000 | 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 | 0.002 | 0.001 |
| Open science | 0.002 | 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".