An Evaluation of Electric Vehicle Penetration under Demand Response in a Multi-Agent Based Simulation
Bibliographic record
Abstract
This paper proposes an electric vehicle charging model and various control algorithms that are further incorporated into a multi-agent system to evaluate impacts of electric vehicle penetration on the power system. Electric vehicles have become increasingly popular due to the high costs of the operation of gas / diesel powered vehicles and the potential to reduce CO2emission. In this work, we propose the electric vehicle charging model and associated control algorithms to aggregate the electric vehicle load. Simulation results show that uncontrolled charging of electric vehicles can jeopardize the stability of the power system. In a worst-case scenario this can lead to an increase of peak demand by 53.2%, while by using appropriate scheduled charging the electric vehicles can have no contribution to the peak demand. Furthermore, scheduled charging dramatically reduces the standard deviation of the residential load (by up to 51%). Therefore, the aggregation of electric vehicle demand under an appropriate demand response control strategy has the potential to dramatically improve the stability of the power system with virtually no negative impacts. The proposed electric vehicle charging model and the associated scheduling 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".