Three-phase distribution OPF in smart grids: Optimality versus computational burden
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Existing Mixed Integer Non-linear Programming (MINLP) solution methods and commercially available solvers lack computational efficiency and robustness in solving three-phase Distribution Optimal Power Flow (DOPF) programs, given the large number of continuous and integer variables encountered in practical sized systems. A heuristic approach to solve this problem was proposed by the authors, in which a compromise is made on optimality in order to reduce the computational burden. In the present work, a Genetic Algorithm (GA) based method is applied to determine the optimal solution to the three-phase DOPF problem, and is compared with the heuristic solution in terms of both optimality and computational burden. Two distribution feeders, namely, the IEEE 13-node feeder and a practical feeder from Hydro One are used for these comparisons. The results show that the GA-based method yields superior solutions in terms of optimality but at a rather large computational cost, making it unsuitable for practical implementation. The heuristic method is shown to yield solutions reasonably close to the global optima at a significantly reduced computational burden, demonstrating that the heuristic solution method has the potential to improve distribution system operation in practical real-time applications.
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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