Analysis of Plug-in Hybrid Electrical Vehicle admission control in the smart grid
Bibliographic record
Abstract
In the power grid, efficient coordination among electricity generation, transmission, distribution and consumption processes call for integration of the advances in Information and Communication Technologies (ICT) to the physical components of the grid. The need for coordination and control becomes even more pronounced when the additional loads of the Plug-In Hybrid Electrical Vehicles (PHEVs) are considered. PHEVs are anticipated to be widely adopted in the following years, and this will increase the load on the power grid since the batteries of the PHEVs will be charged mostly from the grid supplied power. In this case, avoiding mismatch between generation and consumption is one aspect of the problem, whereas to avoid overloading the distribution system components, e.g. transformers, is another equally important challenge. In this paper, we consider an architecture where the status of the grid is monitored by the utility and translated into an amount of provisioned energy for each distribution system serviced by a substation. The substation employs an admission control mechanism for the PHEV charge demands based on the provisioned energy amount. We provide the theoretical analysis of this admission control scheme by calculating the blocking probability of the PHEV demands. We also propose a mechanism to reduce the load without increasing the blocking probability. We introduce an activity factor in the model and show that it can be used to reduce the load. We show by theoretical analysis and simulations that our PHEV admission control mechanism decreases the overall load in the system, and hence increases the resilience of the smart grid. Meanwhile, we show that load reduction can be implemented without increasing the blocking probability, thus customer satisfaction is not degraded.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".