Microgrid and transportation electrification: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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 it