Demand side management by PHEV charging scheduling in residential areas
Bibliographic record
Abstract
During the last decade, Plug-in Hybrid Electric Vehicles (PHEVs) have become a part of modern transportation fleet, offering green alternatives to fossil fuel based transit system. Taking PHEVs great potentials into consideration, this transition can revolutionize transportation systems and push technological advancements further. However, in spite of plentiful economical and environmental advantages, new concerns are being brought up as PHEVs’ utilization rate increases. PHEV’s driving force is supplied by electricity. Hence, the built-in battery requires charging. Such newly introduced power demand, has raised alarming realizations for utility providers. Impacts of PHEVs on distribution networks, although have been proven to be noticeable, have not been thoroughly investigated for future years. In smart grid, the charging of PHEVs can be controlled to reduce the peak load, known as Demand-Side Management (DSM). In this work, we explore various DSM approaches accompanied by their effects on power consumption patterns. Moreover, Geometric Water-filling (GWF) method has been utilized to increase the accuracy of our proposed scheduling schemes. The main contribution of this work emerges by fusing consumer and utility provider concerns, resulting in our dual-target objective function. Such method allows us to alter the focal point between consumer and utility company satisfaction. Index Terms: Plug-in Hybrid Electric Vehicles, Demand-Side Management, Water-Filling
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".