Long-Term Scheduling of Battery Storage Systems in Energy and Regulation Markets Considering Battery’s Lifespan
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Bibliographic record
Abstract
This paper presents a new method for scheduling of battery storage systems for participation in frequency regulation and energy markets, simultaneously. Unknown automatic generation control signal of regulation market is modeled through robust optimization. In addition, the complex effect of participation in regulation market on battery's lifespan is modeled through a dynamic procedure. For this purpose, a long-term optimization process is proposed in which, the short-term participation strategy defines battery's lifespan. In order to prevent fast depreciation of battery due to frequent and deep charges/discharges, a new limiting method is introduced here, which would be useful for participation in regulation market. The proposed long-term model is linearized by implementation of Benders' decomposition. Optimum values for limiting factors are determined in the master problem, while the daily operation strategies are decided by sub-problems. Finally, the applicability of the proposed method is investigated using an illustrative case study.
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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