CIGRE' Task Force C6.04.02: Developing benchmark models for integrating distributed energy resources
Bibliographic record
Abstract
Task Force C6.04.01 has been formed in order to elaborate recommendations for technical rules to be integrated in the national distribution grid codes and for the development of international standards, in order to keep a safe operation of power systems with reasonable and economically acceptable requirements for dispersed generation of various types. Emphasis has been placed on criteria that put unnecessary restrictions on DG penetration. The Task Force has produced a brochure [1] with the following contents:\n\n1.\tReview of the current connection criteria and protection practices applied in various countries for DGs, with special emphasis on the case of wind generators/wind farm integration.\n\n2.\tReview of existing international standards.\n\n3.\tDescription of simplified methods applied to DG connection in various countries\n\n4.\tIdentification of methods to meet the new requirements.\n\n5.\tFormulation of recommendations\n\n\n\nThe Task Force comprises 21 members from 15 countries contributing the experience from France, Spain, Italy, Germany, Austria, USA, Canada, Japan, Belgium, Greece, Portugal, Norway, the Netherlands, Croatia and Saudi Arabia.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".