A Bi-Level Polyhedral-Based MILP Model for Expansion Planning of Active Distribution Networks Incorporating Distributed Generation
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel mixed-integer linear programming (MILP) model for the expansion planning of active distribution networks, which not only is able to accurately reflect the fundamental characteristics of the problem, but also provides the opportunity to find its optimal solution in a computationally efficient manner. This model is able to jointly expand both the network assets (feeders and substations) and distributed generators (DGs) while minimizing the investment and operation costs and taking all the necessary physical and technical constraints into account. A highly accurate linearization method based on polyhedral approximation is utilized to eliminate the nonlinearities of AC power flow equations and obtain the proposed MILP model. Furthermore, a bi-level approach is also proposed to accelerate the solution process and reduce the computation time. This solution approach is comprised of a pre-solution level in which a simplified MILP model is solved to find a near-optimal initial solution for the expansion planning problem, and a main solution level in which the proposed accurate MILP model is solved considering the already found initial solution. Finally, a 24-node distribution system is used to verify the effectiveness of the proposed planning methodology.
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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