Regional Electric-Power Systems Planning and Carbon Dioxide Emissions Management under Uncertainty
Bibliographic record
Abstract
In this study, an interval two-stage integer programming model is formulated for planning electric-power systems and managing carbon dioxide (CO2) emissions under uncertainty. The developed model can reflect dynamic, interactive, and uncertain characteristics of regional energy systems. Besides, the model can be used for answering questions related to types, times, demands and mitigations of energy systems planning practices, with the objective of minimizing system cost over a long-time planning horizon. The developed model is also applied to a case study of planning CO2-emission mitigation for an electric-power system that involves fossil-fueled and renewable energy sources. Solutions can help generate electricity-generation schemes and capacity-expansion plans under different CO2-mitigation options and electricity-demand levels. Different CO2-emission management policies corresponding to different renewable energy development plans are analyzed. A high system cost will increase renewable energy supply and reduce CO2 emission, while a desire for a low cost will run into risks of a high energy deficiency and a high CO2 emission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".