Optimal placement of fuses and switches in active distribution networks using value-based MINLP
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
Contingency conditions in distribution networks create financial losses for different parts of the system including electricity customers, electricity retailers, distributed generation (DG) units, etc. Therefore, protective device allocation methods have been introduced in recent years to enhance the reliability of the power system. In this study, a new formulation is proposed to find the optimal places of sectionalizing switches and fuses while taking the financial loss of both electricity customers and DG units into account. The current method has the flexibility to consider DG effect on any location of the network and its islanded operation in case of contingencies. Moreover, the uncertainty in load and renewable generation is taken into account using stochastic programming. The results demonstrate that the DG units and their financial loss can change the results of switch and fuse placement dramatically when there are no tie switches in the network. Furthermore, it is found that this method can decrease the total reliability costs by 3.86% when high penetration of DG units is introduced into a modified Roy Billinton test system (RBTS). The problem is modeled as a mixed-integer nonlinear (MINLP) formulation and is handled using BARON solver in GAMS environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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