Revolutionising underfrequency load shedding schemes - the E-REDES initiative
Bibliographic record
Abstract
Modernising and strengthening electric transmission and distribution systems is essential in a world increasingly reliant on electrification, distributed resources, and renewable energy integration. Underfrequency Load Shedding Schemes (UFLS) play a critical role in maintaining power system stability, especially during high demand or unexpected disruptions. E-REDES, the main Portuguese Distribution System Operator (DSO), in partnership with the Transmission System Operator (TSO), REN, is developing an automated UFLS approach to improve robustness, flexibility, and efficiency. The current manual UFLS process leads to inefficiencies, prompting the need for automatic calculations and monitoring to address energy transition challenges like distributed energy variability. E-REDES leverages Microsoft Azure Databricks to develop the new UFLS architecture, centralising data from multiple systems, including Geographic Information System (GIS), Outage Management System (OMS), and SCADA. This plan focuses on High Voltage (HV) to Medium Voltage (MV) Substations with frequency load-shedding systems. A rule-based algorithm introduces data consolidation, correlation techniques, and a multi-criteria approach for optimised load allocation. This initiative enhances operational efficiency and supports technical evolution, addressing new energy transition demands. The integration of advanced calculation and monitoring methodologies ensures a more responsive and adaptable system for managing power stability in an evolving energy landscape.
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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 | 0.003 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".