Grid expansion planning considering probabilistic production and congestion costs based on nodal effective load model
Bibliographic record
Abstract
This paper describes an alternative method for grid expansion planning considering construction cost, probabilistic production cost, congestion cost and outage cost of a grid at a composite power system derived from a simulation model. This model includes capacity limitation and uncertainties of the generators and transmission lines. It emphasizes also the two questions of “how should the uncertainties of system elements (generators, lines and transformers, etc.) be considered for longterm production and congestion costs assessment from the economic view point?” and “what is the reasonable reliability level when minimizing construction cost, probabilistic production cost, congestion cost and outage cost?”. This simulation methodology comes essentially from a nodal probabilistic production cost simulation model. This type of model is derived from a nodal equivalent load duration curve based on a nodal effective load model, and has been recently developed. In this paper, a heuristic scenario method choosing a least cost solution (scenario) among various scenario combined from candidate new lines is used for deciding the best solution. The characteristics and effectiveness of this simulation model are illustrated by several case studies using a test system.
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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 | 0.000 | 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 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".