AC Multi-Stage Transmission Network Expansion Planning considering a Multi-Voltage Approach
Bibliographic record
Abstract
Long-term transmission network expansion planning aims to determine where, when and which types of equipment should be installed over a period of time, in order to meet the electric market needs with certain specifications of quality in services at the lowest possible cost. Until now, several methods have been proposed to solve the Static Transmission Network Expansion Planning (STNEP) problem, considering a multi-voltage approach using the DC load flow, however, these solutions may not be feasible when the AC model is used for the operational problem. In this paper a multi-stage model based on the mathematical formulation of the AC load flow is solved, considering a multi-voltage approach, power losses and reactive power compensation. The AC multi-stage transmission network expansion planing problem with multi-voltage approach (MTNEP-MV) was solved by the hybrid meta-heuristic, Differential Evolution (DE) and Continuous Population-Based Incremental Learning (PBILc) algorithm. To evaluate the proposed mathematical formulation Garver 6-bus system was used. The results show that raising the transmission system voltage and considering the MTNEP-MV problem, less transmission lines are required, and also power losses and reactive power compensation needs, are reduced.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".