Optimisation of Location and Size for Distributed Generation in Unbalanced Grids
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a distributed generation installation procedure that can be applied to balanced and unbalanced distribution grids. This is achieved through metaheuristic optimisation and by modelling the grid using the three-phase grid model rather than the one-line diagram model. Both the power loss in the lines and the voltage deviation from the nominal voltage are minimised using the multi-objective genetic algorithm. This installation procedure facilitates grid planning for the distribution system operator. In particular, various possible Pareto optimal solutions can be chosen, which all lead to significant improvements in the performance of the grid. When applied to the unbalanced IEEE37 grid, this method achieves an installation capacity of 3 MW while significantly reducing power loss in the lines by 83.7%, and voltage deviation from the nominal by 90.6%.
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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