Smart Residential Electric Vehicle Charging Control via Multi-Objective Deep Reinforcement Learning
Bibliographic record
Abstract
In recent years, there has been a rapid increase in the adoption of electric vehicles (EVs). This is mainly due to the increasing concern about greenhouse gas emissions and climate change. In Canada alone, $\mathbf{1 8 5, 0 0 0}$ new zero-emission vehicles were registered in 2023, an increase of $49 \%$ from 2022. This surge in the adoption of electric vehicles has intensified the demand for charging electric vehicles, placing additional pressure on the electrical grid, particularly during peak hours. In densely populated areas, the trend towards simultaneous charging further strains the infrastructure, which may cause significant upgrade costs. To our knowledge, electric vehicle supply equipment can automatically adjust charging schedules based on electricity prices, grid conditions, and user requirements, but this has not been well investigated. To address these needs, this work proposes a smart residential electric vehicle charging control system based on multi-objective deep reinforcement learning using the soft actor-critic algorithm. The proposed solution can schedule the EV charging rate based on user requests and several other related factors. Our proposed method succeeds in reducing energy costs, carbon emissions, and peak residential load while ensuring that EVs are adequately charged.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".