Sensitivity‐based relaxation and decomposition method to dynamic reactive power optimisation considering DGs in active distribution networks
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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