A novel electric vehicles charging/discharging scheme with load management protocol
Bibliographic record
Abstract
In this paper the bidirectional power flow between electric vehicle (EV) and grid; Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G), is exploited to reduce the negative impact of the huge EV penetration on the current electric networks. We make profit from the unused electric power of EVs and we present an EV load management technique based on EV charging and EV discharging coordination. We propose two algorithms: the first one is the peak load management (PLM) used to schedule EVs for charging or discharging service according to the power demand with the timing and location where each EV need to be served, the second one is the guidance algorithm (GA) used to guide each EV to the appropriate EVSE in the way to reduce its waiting time to plugin. Those algorithms are evaluated while considering mobility of vehicles in an urban scenario and time-of-use-pricing (TOUP). Simulation results show the effectiveness of the proposed approach when considering realistic EVs and charging station characteristics and constraints.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".