Quantifying the impact of PEV charging loads on the reliability performance of generation systems
Bibliographic record
Abstract
Plug-in electric vehicle (PEV) charging load represents a large and uncontrollable load that behaves far differently from a conventional load. This paper presents a methodology for evaluating the adequacy of the power capacity of systems that include PEV charging loads. A probabilistic analytical approach has been employed using an IEEE reliability test system. The PEV charging load is modeled based on the National Household Travel Survey and on currently available market data pertaining to PEV type and charging level. Also presented is the effect on the adequacy indices of each PEV load parameter, specifically penetration level, PEV type, and charging level. A further case study was conducted in order to evaluate the impact of the current time-of-use tariff in response to the expected increase in power demand due to the massive deployment of PEVs. The results show that the addition of PEVs significantly affects the generation reliability, and that higher charging levels and PEVs with greater battery capacity create a severe risk to generation reliability. Investigation of solutions that maintain reliability indices is therefore required.
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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".