Data-Clustering Based Method for Developing Battery Behaviour CDFs for BESS Grid Integration
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Bibliographic record
Abstract
The planning and operation of power systems necessitates accurate modeling of generation, demand and energy storage. This includes taking into account the stochastic nature of renewable resources, electricity prices and load profiles. This study presents an innovative approach to develop cumulative distribution functions (CDFs) for Li-ion battery dispatch during its charging and discharging states. The CDFs are obtained by executing an hourly dispatch model over a one-year time-frame utilizing actual zonal electricity price data of New York City (NYC) to determine the optimal hour-by-hour dispatches of the battery. This dispatch data are clustered by four distinct seasons and two different day-types (weekdays, weekends) to replicate the energy price profile. The battery charging, discharging and inactive states are derived from the clustered profiles, along with their respective time-frames. Thus, the CDFs are formulated for each season, day type and battery condition (charging or discharging) according to a probabilistic analysis conducted on an hourly basis.
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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 it