Optimal Operation of Micro-Grids to Reduce Energy Production Costs and Environmental Pollution Using Ant Colony Optimization Algorithm (ACO)
Bibliographic record
Abstract
With increasing demand for electric energy and requesting higher quality by subscribers, the electric power industry has moved towards using new technologies. Many modern societies seek to use the systems of new energy management in order to reduce environmental pollutants and operational costs in electrical energy systems. Therefore, exploiting various resources of renewable energies, micro-grids can be considered as a significant tool to attain these objectives. According to the subject, Ant Colony Optimization algorithm (ACO) is used in this paper to optimize one micro-grid sample in order to reduce the cost of generating power and to reduce environmental pollution to increase reliability. The recommended algorithm has been done in two scenarios and each in two sections. In the first scenario, energy management will be conducted for all distributed generation resources and in the second scenario it is assumed that wind and solar products, produce their maximum power and energy management are conducted for the reminder elements and the results are compared with other optimization algorithms.
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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".