Pareto Optimality in Economic-Environmental Dispatch (EED) of Grid-Generators: A Case for Nigeria
Bibliographic record
Abstract
This paper presents a multi-objective approach to Economic-Environmental Dispatch (EED) problem for grid-generators in Nigeria, coded in General Algebraic Modeling System (GAMS). The EED problem is formulated as a nonlinear constrained multi-objective optimization problem for both economic and environmental considerations and for twenty-six (26) grid-generating plants. The analysis was done for three (3) scenarios and electricity demand and supply between 80GW and 1.4TW modeled for Nigeria by 2050. The results indicate over 60% reduction of emissions for electricity supply forecast for Nigeria with pareto optimality applied to generation mix. The results also demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of the multi-objective EED problem in one snapshot. The comparison with the classical techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem as a pathway to achieve sustainable electricity mix and solution in Nigeria by 2050.
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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 | 0.000 | 0.000 |
| 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".