Congestion-Driven Positioning of Grid Enhancing Technologies
Bibliographic record
Abstract
Transition towards low carbon electricity networks increases the bottlenecks in transmission networks which necessitates transmission network expansion. Yet, traditional transmission network expansion which relies on building new transmission lines is challenging due to the limited rights of way, long development lead times and capital intensive investment requirements. Grid enhancing technologies such as power flow control devices, and dynamic line rating are expected to play a crucial role in reducing transmission network congestion in low-carbon electricity networks. Nevertheless, there are limited decision-making tools which can assist the stakeholders to select the most appropriate grid enhancing technology for different locations of the network. In this paper, we examine the feasibility regions of bulk power systems to determine the influencing factors on congestion-driven positioning of grid enhancing technologies. Depth and duration of congestion are introduced as the determining factors for selecting the appropriate grid enhancing technology. It is demonstrated that these influencing factors can be monetized to justify the installation and deployment of the grid enhancing technology. The significance of the influencing factors are demonstrated by characterizing the feasibility region of a three-bus test 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".