Optimal charging and discharging for EVs in a V2G participation under critical peak conditions
Bibliographic record
Abstract
Integrating electric vehicles (EVs) into the smart grid can support various services for the power grid through the vehicle‐to‐grid (V2G) system. In this study, the effects of critical peaks (CPs) on EVs’ charge/discharge process (CDP) while providing V2G services are critically investigated. A charge/discharge optimisation algorithm for EVs while considering varying charging costs and discharging incentives is proposed. Primarily, a probability model for the occurrence of CPs is formulated and incorporated in a time‐of‐use tariff plan. Considering the battery capacity loss in a CDP, an optimisation model is developed based on a non‐linear programming model. An optimisation algorithm is proposed to enhance the EVs’ CDP. The goal is to obtain the least possible charging cost per day while facilitating the V2G services, especially in case of CPs. The effects of CPs on the per day charging cost while considering real‐life scenarios are investigated. Furthermore, the dependence of the energy discharged by the EV on the number of estimated battery cycle life and the per day charging cost considering battery replacement is analysed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".