Optimal Charging Control for Plug-in Electric Vehicles in Smart Microgrids Fueled by Renewable Energy Sources
Bibliographic record
Abstract
There is a growing interest in plug-in electric vehicles (EVs). Charging EVs from smart microgrids fueled by renewable energy resources is becoming a popular green approach. Although some works have been done on renewable energy sources and EVs in smart microgrids, the stochastic characteristics and the dynamic interplay between these two important green solutions should be carefully considered. Furthermore, one of the important performance metrics, service availability for EVs, is largely ignored in the existing works. In this article, we study the charging policies in smart microgrids with EVs and renewable energy sources. We analyze service availability under different charging policies. Based on the renewable energy sources states, battery states, and the number of charging EVs, an optimal charging policy is obtained to maximize the energy utilization with service availability constraints. We formulate the optimal charging problem as a stochastic decision process. Maximizing the energy utilization with service availability constraints is the objective in our model. Extensive simulation results are presented. It is illustrated that the proposed scheme can significantly improve the service availability for EVs in microgrids fueled by renewable energy sources.
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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 | 0.001 | 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".