Bi-level coordinated optimization method integrating improved artificial fish swarm algorithm and hardware cost model for distribution network
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".