Evaluation of electric vehicle penetration in a residential sector under demand response considering both cost and convenience
Bibliographic record
Abstract
This paper proposes a residential load prediction model and an optimal control algorithm considering both electricity payment and waiting time to study impacts of electric vehicle (EV) penetration on the power system. EVs present both challenges (large electrical load) and opportunities (high efficiency and environmentally friendly). The proposed load prediction model simulates heterogeneous residential power consumption. A convex optimization model with real-time pricing (RTP) prediction is proposed to schedule EV charging to determine a tradeoff between electricity payment and waiting time. The dissatisfaction factor from delaying the EV charging, the EV penetration levels and flexibility of charging period are evaluated. The PAPR, standard deviation and electricity payment are significantly decreased by using the proposed optimal control model. Simulation results provide users a base line in which a “best” dissatisfaction factor value can be determined to find a trade-off. This study also shows that, although more and more controlled EV charging has the potential to improve the reliability of the power system, the restricted charging period at the residential sector can be a bottleneck when the EV penetration exceeds a certain level.
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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 | 0.001 | 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".