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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".