Joint optimization of location and topology of multi-terminal soft open point in distribution networks
Bibliographic record
Abstract
The popularization of renewable energy has led to problems including excessive current and voltage violations in distribution networks. Soft open point (SOP) enables real-time continuous active and reactive power regulation to alleviate these problems. However, how many terminals of a SOP should be set, and which feeders should be interconnected with these terminals is a crucial issue. To address this issue and fully utilize the performance of SOP, this paper conducts a comparative study of SOP with different topologies. First, a nonlinear programming (NLP) model to reveal the effect of multi-terminal SOP (MTSOP) in minimizing system losses and voltage deviation is developed. Second, to facilitate the solution, the NLP model is transformed into a second-order cone programming (SOCP) model based on cone relaxation. Finally, validation on the IEEE 33-, 69- and 141-node systems is conducted. MTSOP can reduce the total losses of IEEE 33-, 69- and 141-node systems by up to 23.54 %, 37.98 %, and 28.90 %, respectively. Although SOPs with a large number of terminals have excellent performance, they are difficult to gain an advantage in feasibility. Therefore, it is not necessarily better to have more terminals in an MTSOP which should be determined based on the characteristics of distribution networks.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".