Optimal scheduling algorithm for charging electric vehicle in a residential sector under demand response
Bibliographic record
Abstract
This paper proposes an electric vehicle charging model and an optimal control algorithm to predict and evaluate impacts of electric vehicle penetration on the power system. Electric vehicles have become increasingly popular due to their highly efficient use of energy and their potential to reduce CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions. The proposed electric vehicle charging model simulates an individual electric vehicle's load profile by capturing various characteristics of a Lithium-Ion battery such as charging demand, the state of charge and potential driving patterns. The optimal control algorithm of scheduling electric vehicle charging is formulated as a convex optimization problem under real-time pricing to minimize the electricity payments of the user. Simulation results show that uncontrolled electric vehicle charging can jeopardize the stability of the power system while scheduled charging has no contribution to the peak demand. Furthermore, scheduled charging dramatically reduces the peak to average power ratio and electricity payment of users. The proposed electric vehicle charging model can be used to study charging patterns in a simulation environment and the optimal control algorithm can be embedded into a home energy management system or a smart charger.
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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".