EVs for frequency regulation: cost benefit analysis in a smart grid environment
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
Vehicle‐to‐grid systems facilitate efficient and reliable integration of electric vehicle (EV) into the smart grid. This integration helps provide various services such as peak load levelling, frequency regulation (FR) and other ancillary services that provide notable benefits to utilities. In addition to the benefits to the utilities, EV owners may also benefit from providing these ancillary services to the grid. In this study, a comprehensive assessment of the economic benefits of using EVs to support FR service to the power grid is developed. The limitations for providing such services to the grid are evaluated. The number of the charge and discharge cycles of the EV battery is estimated based on a realistic semi‐logarithmic model. Finally, the estimates are used to calculate the battery degradation cost for providing FR and estimate the safe amount of power that EVs can supply with adequate consideration for daily driving requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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