E-REDES Solution to monitor and analyse underfrequency load shedding performance
Bibliographic record
Abstract
Underfrequency Load Shedding (UFLS) is a vital protection scheme to ensure power system stability and integrity, preventing a cascading drop in frequency and potential widespread blackout by automatically disconnecting progressive l oad steps in a timeframe of a few seconds. While UFLS is rarely triggered, the increased penetration of Renewable Energy Sources (RES) has decreased the power system inertia, resulting in the potential for more regular and greater amplitude underfrequency events. Furthermore, the intermittent nature of RES, alongside the fact these generation sources are mainly being integrated in to the distribution grid, creates problems in predicting the load associated with each UFLS step. Considering these challenges, E-REDES designed an algorithm and interface to monitor the UFLS scheme, allowing the simulation of each theoretical step operation for past and future load scenarios. The detailed approach enables a statistical analysis of UFLS scenarios, providing insights into key metrics such as assessing ENTSO-E targets and optimizing each step calculation criteria. This paper details the proposed methodology, the results of UFLS monitoring in Portugal and the major benefits derived from it. The authors believe it allows for a more flexible and proactive monitoring of the UFLS scheme, improving TSO/DSO coordination in this topic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".