Neighborhood level network aware electric vehicle charging management with mixed control strategy
Bibliographic record
Abstract
With the fast development of the electric vehicle (EV) technology and the pressing environment conditions. It is expected that EVs will grow rapidly in the near feature. However, the broad adoption of EVs will produce a high power demand on the power grid. Smart charging control of the EVs could help to relieve the possible negative influences on the power grid. Many researchers have investigated possible algorithms for EV charging control. Considering customers' willingness, this paper proposes a mixed charging control framework. The proposed control framework is aimed to minimize the charging cost and satisfy the customers' charging freedom requirements. A user satisfaction determining method is also proposed which can help to evaluate user satisfactions for different control algorithms. Within the proposed framework, both the benefits for the utility companies and the customers are considered. The optimization framework is evaluated with the data from a local utility company. Simulation results show the efficiency of the proposed framework. The framework can also be further used to evaluate the penetration for a certain neighborhood level network.
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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".