Smart Charging of PEVs Penetrating Into Residential Distribution Systems
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Bibliographic record
Abstract
This paper presents a novel modeling framework for the analysis of Plug-in Electric Vehicle (PEV) charging in unbalanced, residential, distribution systems. A Smart Distribution Power Flow (SDPF) framework is proposed to determine the controlled or smart charging schedules and hence address the shortcomings of uncontrolled charging. The effect of peak-demand constraint imposed by the Local Distribution Company (LDC) is also studied within the SDPF framework for the smart charging scenarios. Uncontrolled versus smart charging schemes are compared for various scenarios, from both the customer's and the LDC's perspective. Various objective functions, such as energy drawn by the LDC, total feeder losses, total cost of energy drawn by LDC and total cost of PEV charging are considered. Studies are carried out considering two sample systems i.e., the IEEE 13-node test feeder and a real distribution feeder. Analyses are also presented considering a probabilistic representation of the initial state of charge (SOC) and start time of charging for various scenarios to take into account the difference in customers' driving patterns. The results show that uncontrolled charging of PEVs results in increased peak demand, low node voltage levels, and increased feeder current magnitudes. On the other hand, the SDPF framework provides very satisfactory operating schedules for the overall system including smart PEV charging.
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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