Bi-level coordinated optimization method integrating improved artificial fish swarm algorithm and hardware cost model for distribution network
Why this work is in the frame
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Bibliographic record
Abstract
Traditional power flow optimization fails to account for the coupling between network loss and the cost of converters, and overlooks both transmission loss and distribution equipment loss. This paper proposes a bi-level coordinated optimization framework that integrates an improved artificial fish swarm algorithm (AFSA) and a hardware cost model to resolve this conflict. This framework has developed a two-layer model consisting of an X-Y layer optimal power model and a Z-layer optimal reconstruction model, which explicitly combines hardware costs and inverter losses, effectively resolving the conflict between minimizing control actions and reducing system losses. Furthermore, an enhanced AFSA featuring adaptive recombination behavior significantly improves resource utilization efficiency and reduces computation time. The results verified on the experimental distribution network platform show that, compared with traditional methods, the proposed approach reduces the total economic cost by 7.97 %, enhances the wind power consumption capacity by 12.42 %, and increases the average minimum voltage by 6.81 %, while maintaining a comparable level of transmission loss.
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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 it