A Fast Solution Method for Stochastic Transmission Expansion Planning
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Bibliographic record
Abstract
Stochastic programming is a cost-effective approach to model the transmission expansion planning (TEP) considering the uncertainties of wind and load, which is known as stochastic TEP (STEP). The uncertainty can be accurately represented by a large number of scenarios, which need to be reduced to a relatively small number in order to shorten the computational time required by the STEP. The forward selection algorithm (FSA) is an accurate scenario reduction method which, however, is quite time consuming. An improved FSA (IFSA) is proposed in order to shorten the computational time. The STEP is a large-scale mixed-integer programming problem, and, therefore, is difficult to be solved directly. Benders decomposition algorithm is suitable to solve the STEP by decomposing it into master and multiple slave problems. The slave problems are nonlinear and thereby are difficult and time consuming to be solved. In this regard, a linearization method is proposed to solve the slave problems faster and to calculate the Lagrangian multipliers needed by the master problem. Two medium and a large datasets are used to demonstrate the efficiency of the IFSA and a 24-, a 300-, and a 2383-bus test systems are used to verify the efficiency of the linearization method.
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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 | 0.001 | 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 it