Exploiting PHEV to Augment Power System Reliability
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
Environmental concerns with gasoline vehicles have led to increased attention to electric vehicles in recent years. Plug-in hybrid electric vehicles (PHEV) use both electricity and gasoline to propel the vehicle, and is being recognized as a potential alternative to conventional vehicles. PHEVs offer opportunity to use electric energy generated by renewable resources and significantly reduce greenhouse gas emissions. The electric energy requirement of PHEV can, however, cause negative impacts on the power system reliability, especially when the size of a PHEV fleet is relatively large. This paper presents the development of a probabilistic model considering the driving distance, charging times, charging locations, battery state of charge, and charging requirements of a PHEV. A methodology using hybrid analytical and Monte Carlo simulation approach is presented to evaluate the reliability of a power system integrated with PHEVs, considering the important PHEV characteristics, charging scenarios, and power system parameters. Studies are presented on the IEEE-reliability test system to illustrate the impact of PHEV penetration in a power system. Based on the study results, the methods of augmenting system reliability through controlled PHEV charging are presented in this paper.
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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