Energy-centric flexibility management in power systems
Bibliographic record
Abstract
Currently, power system planning practices are undergoing various transformations in an attempt to integrate efficiently significant amounts of low-carbon power generation technologies. At the heart of this efficient integration lies the need to plan for and exploit the available flexibility in power systems. In the past, the emphasis was on planning the capacity (i.e., power) of operating reserve requirements, in the form of categorized reserve types. Such approach was suitable for traditional power systems exhibiting low variability and uncertainty. The concept of power system flexibility is emerging as a way to emphasize the need to also consider the ramping capability of operating reserve needed to accommodate high variability and uncertainty arising from renewable energy integration. Prior considerations of power system flexibility, however, are found to be inadequate to handle energy-limited power system resources like energy storage assets and demand response. Hence, this paper sets to consider systematically energy limitations of operating reserve by proposing energy-based operating reserve definitions. We demonstrate the benefits of the new reserve definitions, using a receding-horizon economic dispatch integrating energy storage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".