Comprehensive mixed‐integer linear programming model for distribution system reconfiguration considering DGs
Bibliographic record
Abstract
Distribution system reconfiguration (DSR) is a critical process that improves the power transfer efficiency and reduces the over‐all operational cost. There have been various methods for addressing the DSR problems. Recently, DSR problems formulated in mixed‐integer linear programming (MILP) has gained popularity as they generally can be solved by the state‐of‐the‐art commercially accessible linear programming solvers, and is able to solve the system with thousands of unknown variables within a reasonable time. However, in some MILP formulations, the distribution line losses are omitted in the nodal power injections for the sake of simplicity. This compromises the accuracy of the linearised model and contributes to the disparity between the MILP and the true non‐linear model. Hence, in this study, new formulations are introduced for embedding the expressions of line losses inside load flow equations so that the deviations between the modelled and exact losses notably reduce. Moreover, other novel formulations have also been presented for simultaneously optimising distributed generation (DG) locations and sizes, while at the same time considering various DG's modes of connection to the distribution grid. The validity and effectiveness of the proposed MILP model is tested on standard IEEE systems and actual distribution network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".