A New Approach for Estimating Frequency Variations Due to Smart Grid Functions
Bibliographic record
Abstract
This article presents a new approach for estimating the frequency variations (Df) due to applying smart grid functions on a load bus. The proposed approach is based on constructing a ZIP model for the power demands at a load bus that is controlled by smart grid functions.The constructed ZIP model provides a relationship between the active and reactive power demands and the frequency at any load bus. This relationship can be formulated as a set of nonlinear equations, which can be numerically solved for Df. The load-model approach is implemented for performance evaluation using load buses in the IEEE 30-bus power system under different loading levels. Performance results show that the proposed approach has a simple implementation, and can provide an accurate estimation of frequency variations due to slow and small changes load power demands. Furthermore, performance results reveal the insensitivity of the load-model approach to load power demands and/or seasonal changes in load power demands.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".