Too Much, Too Soon? Exploring Trade-offs in Generation Expansion Planning
Bibliographic record
Abstract
In this paper, we compare and evaluate four model formulations for solving the Generation Expansion Planning (GEP) problem, including static and dynamic models and models, with and without the transmission network. Each formulation is evaluated on three key metrics at the optimal solution: (1) the percentage of load shedding, (2) the utilization rate of newly installed units, and (3) CPU time as a measure of computational complexity. The models are tested in two settings: a toy, two-node power system and three large benchmark systems (39 - 118 bus). The two-node system allows for detailed analysis of the solutions, while the larger systems enable evaluation of scalability and performance of each formulation. Results show that including the network constraints entirely reduces the potential load shedding in future network operation, while the dynamic models show a better utilization of newly installed units compared to static models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 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".