Sensitivity‐based relaxation and decomposition method to dynamic reactive power optimisation considering DGs in active distribution networks
Bibliographic record
Abstract
With the development of active distribution networks, new challenges such as overvoltage and power loss become critical. The reactive power optimisation serves as a voltage control measure to minimise the total transmission loss by coordinating the continuous and discrete reactive power compensators while guaranteeing the specific physical and operating constraints. To address the daily operating times of discrete control variables, the dynamic reactive power optimisation (DRPO) is set up to minimise total energy loss over several time periods when considering the inter‐temporal constraints. However, DRPO is in fact a large‐scale mixed integer non‐linear non‐convex programming that is difficult to solve. Therefore, second‐order cones are employed to relax the non‐convex power flow equations to obtain a mixed integer second order cone programming model. Furthermore, a sensitivity‐based relaxation and decomposition method is proposed to further improve the computational performance. Solution quality and computational performance are compared with traditional methods on IEEE‐33, 123 and 615‐bus systems as well as two real‐world distribution networks in China. The Results demonstrate that the fast performance and effectiveness of the proposed technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".