Flexibility chart 2.0: An accessible visual tool to evaluate flexibility resources in power systems
Bibliographic record
Abstract
Various aspects of power system flexibility are evaluated within the multi-country study framework of IEA Wind Task 25. Grid components and actions which have been adopted for enhancing flexibility in different areas, countries, regions are addressed, as well as how Transmission System Operators, Independent System Operators, Utilities intend to manage variable generation in their operating strategies. A visual assessment to evaluate the diversity of flexibility sources, called a “flexibility chart”, is further developed to illustrate several flexibility parameters (e.g., hydropower, pumped hydro, gas turbine, combined heat and power, interconnection and battery) in a polygonal radar (fan-shaped) chart. This enhanced version of the Flexibility Chart is an “at-a-glance” and “easy-to-understand” tool to show how to estimate the potential of flexibility resources in a given country or area, and is accessible for non-technical experts. The Flexibility Chart 2.0 is also a useful tool to compare the past and future flexibility of a system. Comparing the historical change of flexibility resources may not only be helpful to discuss energy policy in regions with high installed variable renewable generation, but also to contribute to the discussion in other regions where renewables have not been widely adopted yet.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.012 |
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".