Enhancing the large-scale electric power systems to meet future demands considering the sustainable technologies
Bibliographic record
Abstract
Electricity systems are currently expanding towards more efficient forms of production. Several expansionary strategies are being developed to cover increases in future electricity demand. Goals such as reducing greenhouse gas emissions, increasing the efficiency of operations, and achieving more equitable participation of the actors in charge of the investments are set. Following this premise, this paper presents a multi-objective model that helps in decision-making on the problem of expanding electricity generation. The model considers more realistic views than other works in the literature. The vast majority of the stakeholders in the studied field are satisfied with the present proposal. Investment costs, greenhouse emissions, and investment contribution rates are considered. Also, the actual procedures of the generation and transmission stages are rigorously studied. This means obtaining solutions that are closer to reality. The case study is the electricity system of Argentina. The results obtained indicate that the recommended solutions are the most convenient from all points of view. They constitute a mix of the generation with renewable and non-renewable technologies. The case study reveals emission reductions of up to 25% and it can be achieved that the most vulnerable social groups do not have to finance future system expansions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".