A Linear Chance-Constrained Mixed-Integer Programming Model for Optimizing Regional Electric Power Systems under Carbon Constraints
Bibliographic record
Abstract
In view of increasing population size and energy consumption, greenhouse gas (GHG) emissions are increasing and are one of the main causes of climate change. The regional electric power system is one of the main sources of carbon emissions, so there is an urgent need to optimize the regional electric power system to meet the Paris Agreement's long-term temperature goal. Therefore, this study provided a linear chance-constrained mixed-integer programming (LCMI) model with the objective of maximizing the total system profit and applying it to the regional electric power system. Chance-constrained programming and mixed-integer programming were integrated into the LCMI model to address input uncertainties. Including five commonly used power generation technologies, namely coal-fired, natural gas-fired, hydropower, wind power, and solar power, the model can provide the optimal electricity generation schemes and capacity expansion plans for different technologies at the regional level to meet the end-user’s needs while meeting the carbon dioxide emission targets under different risk levels. The outcomes of the research will offer decision-makers a framework for optimizing conventional regional electric power systems for their long-term sustainability in environmental and economic development.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".