Microgrid and transportation electrification: A review
Bibliographic record
Abstract
An increasing concern over environmental impacts of fossil fuels and sustainability of energy resources is leading to significant changes in the electric power and transportation systems. Decentralized power generation and transportation electrification, in particular, are emerging as some of the most effective and promising tools in addressing these concerns. In this article, we conduct a integrated review of microgrid and transportation electrification to emphasize their inevitable interdependency in future developments. We summarize control strategies for microgrid and electric vehicle (EV) technology and discuss the challenges arising from the introduction of EVs into electric grid systems. We also analyze the current electric power market. To improve the grid's efficiency, we study the possibility of EVs performing vehicle-to-grid (V2G) service in order to entirely or partially replace some inefficient components of the current power market. Finally, two future research topics are proposed. First, a smart plug-and-play control approach for the integration of EVs into microgrids is described. Second, an adaptive charging strategy is proposed for the use in microgrids. This could mostly alleviate the stress to medium voltage distribution transformer of a region with a large EV population, without compromising the charging performance and EV owner's interests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".