Security‐constrained transmission expansion planning using linear sensitivity factors
Bibliographic record
Abstract
Formulating power flow equations with linear sensitivity factors (LSFs) reduces the number of variables and constraints, and consequently, the computational burden of power systems’ optimisation problems. This study proposes a transformative, computationally efficient model for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of LSFs to formulate an optimisation. LSFs allow to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Thus, the proposed model includes less number of variables and constraints compared with the classical angle‐based model. These features significantly reduce computational costs of TEP and enhance its scalability, especially for large‐scale systems. Load and generation uncertainties are modelled using a data‐driven approach, and N − 1 security criteria are taken into account to ensure system security. All equations under normal and N − 1 conditions are considered using data of the complete network graph. Simulation results show that the proposed model provides the same results as the conventional angle‐based model while being much faster (more than 58% based on the authors’ case studies) and computationally more efficient.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".