Stochastic Transmission Expansion Planning Considering Uncertain Dynamic Thermal Rating of Overhead Lines
Bibliographic record
Abstract
Dynamic thermal rating (DTR) is an important smart grid technology that can bring considerable economic benefits. One of the most important benefits of DTR is to postpone new investment. This paper proposes a novel stochastic transmission expansion planning (STEP) model considering the DTR of overhead lines. The objective function of the STEP model includes operational costs and the investment costs of new line construction and DTR systems installation. The model can determine where to build new lines and install DTR systems. The model cannot only realize the benefits that occur when the DTR is higher than the static thermal rating (STR) but also avoid overload risk, i.e., the power flow on a line being larger than the line's real capacity, caused by the DTR being lower than the STR. The model can consider both the voltage magnitude and phase angle of each bus. The model is linearized and therefore can be effectively solved by a Benders decomposition method. Furthermore, a new way of scenario reduction is proposed to obtain a better set of reduced scenarios. The effectiveness of the model is verified on a modified IEEE reliability test system and a modified IEEE 300-bus system.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".