Coordinated Planning of Converter-Based DG Units and Soft Open Points Incorporating Active Management in Unbalanced Distribution Networks
Bibliographic record
Abstract
Soft open points (SOPs) can transfer active power between feeders and compensate reactive power. These features help increase the integration capacity of distributed generation (DG), but the installation location and capacity of SOPs will affect DG planning. In addition, the distribution networks are usually unbalanced due to asymmetric line parameters, unbalanced loads, and DG. Converter-based DG units and SOPs have individual phase active and reactive power regulating ability and provide unbalance compensation. The objective of this paper is to develop a coordinated planning model of converter-based DG units and SOPs in an unbalanced distribution network (UDN) to incorporate their individual phase power control abilities. The individual phase power control characteristics of DG converters and SOPs are first analyzed. A bi-level optimization model of converter-based DG units and SOP planning is then established, in which the upper-level problem minimizes the total cost of the UDN and the lower-level problem minimizes the power loss and voltage unbalance. The bi-level model is transformed into a single-level mixed integer second-order cone programming problem that can be efficiently solved by widely used commercial solvers. Finally, the proposed model is verified on IEEE 33-node and Taiwan Power Company systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".