Optimal battery cycling strategies in workplaces with electric vehicle chargers, energy storage systems and renewable energy generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Efficient unit commitment strategy in a modern workplace facilitated with electric vehicle (EV) chargers and energy storage systems requires implementation of optimal battery cycling for both local storage system and electric vehicle batteries. In order to achieve this goal, it is necessary to address the battery health in the energy management strategies of commercial buildings. A fair battery cycling approach that could consider the interests of both parties in a workplace (including the system operator and the EV owners) requires access to detailed information on battery performance and degradation‐associated costs. In this study, a detailed investigation is carried out on the optimal battery cycling in a workplace that is facilitated with an EV charging station, energy storage system and renewable energy generation. This is carried out by employment of a tailored unit commitment model that can address the battery health for EVs, individually. This study illustrates how a business owner and the employees that own electric vehicles can benefit from bidirectional battery cycling in an equitable way without compromising their financial interests in the energy market.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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