Probabilistic integrated framework for <scp>AC</scp> / <scp>DC</scp> transmission congestion management considering system expansion, demand response, and renewable energy sources and load uncertainties
Why this work is in the frame
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Bibliographic record
Abstract
Congestion management (CM) is one of the most crucial tasks in power system operation and planning, which has become more challenging in recent years due to the growth of renewable energy sources (RESs) and flexible loads. This paper presents an integrated framework that simultaneously employs different methods, including re-scheduling, transmission expansion, and demand response programs (DRPs), to manage the AC/DC transmission congestion. The uncertainty associated with remote wind/solar farms and load demand is taken into account and is modelled using the probabilistic point estimate method. To provide a comprehensive analysis, three different management approaches taking both planning and operation phases into account are considered in this study. In the context of CM, the first management approach considered is to minimize the overall system cost including both investment and operational costs (cost-efficient approach). The second approach is to minimize the overall active power losses (energy-efficient approach). The last one is to make a trade-off between these two approaches (cost-/energy-efficient approach) by simultaneously minimizing system investment cost and operational loss as a multi-objective optimization problem. The effectiveness of the proposed framework is evaluated on IEEE two-area RTS-96 (MRTS) network using an AC/DC power flow tool.
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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 | 0.001 |
| 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 it