Bibliographic record
Abstract
Electric vehicles are gaining more and more popularity due to low oil dependency and low emission. Their deep penetration will significantly benefit the environment, but meanwhile will cause two crucial consequences. First, electric vehicles introduce heavy load impact into the power grid by shifting energy demand from gasoline to electricity. The surging load will compromise the grid’s reliability and jeopardize its power supply quality. Second, charging stations become indispensable infrastructure to support large deployment of electric vehicles. The availability in public destinations comes with electric vehicles competing for both power supply and service points of charging stations. The competition degrades quality of service and thus can compromise the original intent of advocating electric vehicles. There are many research efforts addressing either of the two consequences above. Different with them, we consider both and jointly study quality of service for electric vehicle users and reliability of the power grid. We review recent developments on this topic in this article. In Chapter 1, we introduce the ecosystem of electric vehicles and discuss motivations for managing charging load. This chapter further presents a systematic solution framework for smart electric vehicle charging. The following chapters then study each block of the framework. Specially, in Chapter 2, we investigate charging rate control, which handles how to allocate power supply to electric vehicles within a charging station. In Chapter 3, we address electric vehicle demand response, which is how to make electric vehicles follow the power supply of charging stations and the power grid. In Chapter 4, we study electric vehicle scheduling, which copes with how to schedule electric vehicles to multiple charging points within a charging station. In Chapter 5, we discuss charging demand balancing, which deals with how to balance electric vehicles among multiple charging stations. In these chapters, we first present deployable algorithms and mechanisms that are designed for each framework blocks. Then, we evaluate the proposed approaches by two complementary ways. One way is leveraging theoretical analysis to demonstrate their performance guarantees, while the other is using extensive simulations based on realistic data traces and simulation tools. We also review studies that align with the corresponding framework blocks and consider additional dimensions and/or different optimization goals. Finally, in Chapter 6, we conclude the article with summaries of main ideas discussed in the previous chapters.
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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 | 0.001 |
| 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".