Gas Network's Impact on Power System Voltage Security
Bibliographic record
Abstract
Due to the energy linkage between electricity and gas networks, assessing the voltage security of the electricity system without considering the practical constraints of both systems, may lead to unrealistic values of loading margins (LM). This work proposes a model for investigating the impact of gas networks on the voltage security of electric transmission networks. The overall objective is to maximize the LM of the electricity network while satisfying all relevant constraints in both gas and electricity networks such as hourly line pack of gas pipelines, reactive power capability limits of generators, and complementarity constraints representing the generators active/reactive power limits based on the capability curves, power flow equations at both current operation, and security limit points. Three (small, medium, and large) case studies are presented as the applications of the proposed model for LM maximization in power systems that are highly coupled with gas networks. The obtained results corroborate the impact of both gas and electrical networks operation constraints such as voltage and reactive power limits, nodal gas pressure limits, gas network loading as well as the line pack phenomenon of gas pipelines on the LM of power systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".