Generation and Transmission Network Expansion Co-Planning Models: A Comprehensive Review
Bibliographic record
Abstract
In the realm of energy infrastructure planning, Generation Expansion Planning (GEP) stands as a critical component, aimed at determining the optimal generation unit capacities, resource combinations, locations, and commissioning schedules. In parallel, Transmission Network Expansion Planning (TNEP) is intended to identify the future requirements for transmission infrastructure, ensuring an efficient and reliable energy supply. While GEP and TNEP share a common goal of equipping generation and transmission networks to meet future demands, traditional approaches tend to address these aspects separately or sequentially, neglecting their inherent interdependence. To address this limitation, co-optimization models have been developed to integrate Generation and Transmission Network Expansion Planning (GTNEP) in a unified approach. In this work, a comprehensive review of the existing literature on GTNEP models is conducted. The objective of this review is to classify and identify gaps and opportunities for further research, spanning both centralized and liberalized electrical markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".