Communication-based Plug-In Hybrid Electrical Vehicle load management in the smart grid
Bibliographic record
Abstract
New services and applications that employ the advances in the Information and Communication Technologies (ICT) to the electrical power grid are rapidly emerging and consequently the traditional power grid is evolving into a smart grid. In the smart grid, communication among the supplier controlled generation units, utility administered transmission and distribution system and the consumer devices is providing new opportunities for improving the resilience and the efficiency of the grid. Resilience is a significant issue due to increasing demand, and in contrast, diminishing fossil fuels. Moreover, in the near future, resilience is expected to become a more significant concern especially due to the additional loads of the Plug-In Hybrid Electrical Vehicles (PHEVs). PHEVs are expected be widely adopted as passenger cars and as commercial vehicle fleets since they have low carbon emissions and low operating costs. On the other hand, their load on the power grid should be managed so that they do not cause failures. In this paper, we propose the Communication-based PHEV Load Management (Co-PLaM) scheme to control the load of the PHEVs. In our scheme, utilities provision a certain amount of energy for each distribution system based on the predicted supply level. The provisioned energy is communicated to the Substation Control Center (SCC) where each charging request is either accepted or rejected based on the utility set limits. Then, these decisions are sent to the smart charging stations through a Wireless Mesh Network (WMN) that uses IEEE 802.11s. In this paper, we simulate the Co-PLaM scheme and also mathematically analyze the blocking probability of the system. We show the performance of WMN in terms of delivery ratio, delay and jitter. Furthermore, we provide the blocking results and show the required additional capacity to supply all the PHEV loads without causing grid failures.
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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 | 0.000 | 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 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".