Study on Economic, Rapid and Environmental Power Dispatch Based on Fuzzy Multi-objective Optimization
Bibliographic record
Abstract
Environmental awareness and the recent environmental protecting policies have extremely spurred many electric utilities to regulate their practices to account for the emission impacts. One way to accomplish this is by reformulating the traditional Economic Load Dispatch (ELD) module merely with a view to minimal coal consumption of fossil fired units. This paper presents a triple-objective ELD model which consists of the minimal coal consumption, the best time for the unit commitment response and the economic emission index. The rapid/economic/environmental dispatch problem is a multi-objective non-linear optimization problem with constraints. The fuzzy theory is adopted to convert the multi-objective problem into the single-objective problem. The problem is been tackled through dynamic programming algorithm and the approach is tested on a four-unit system to illustrate the analysis process in present analysis. Results simulated through MATLAB show that the approach has great potential in handling multi-objective optimization problem.
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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".