Smart rate control and demand balancing for electric vehicle charging
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
The anticipated high electric vehicle (EV) penetration motivates many research efforts to alleviate the potential associated grid impact. However, few works discuss the crucial issue: quality of service (QoS) degradation caused by competing for charging resources. This issue arises due to the limitation on power supply and charging space that charging stations can usually provide. Our work studies this issue and proposes an operational scheme that optimizes QoS for EV users while satisfying the stability of the power grid. The scheme consists of two levels. The lower level deals with charging rate control, for which we propose an efficient algorithm with provable QoS-optimal allocation of power supply to EVs. The upper level handles charging demand balancing, for which we design two approximation algorithms that schedule EVs to multiple charging stations. One algorithm is a 3-approximation with polynomial complexity; while the other is a (2 + e)-approximation using a fully polynomial time approximation scheme. Through extensive simulations based on realistic data traces and simulations tools, we demonstrate the efficiency and efficacy of our operational scheme and further provide interesting findings from in-depth analysis of the experimental results.
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) | 0.000 | 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 it