Machine Learning-Based Control of Electric Vehicle Charging for Practical Distribution Systems With Solar Generation
Bibliographic record
Abstract
The adoption of Electric Vehicles (EVs) and solar Photovoltaic (PV) generation by households is rapidly and significantly increasing. Utilities are facing the challenge of efficiently managing EV and PV resources to help mitigate the undesirable effects on grid operation. Existing approaches to solve these issues depend on accurate but hard to predict behavior of EVs and PVs, detailed knowledge of customers, and grid infrastructure, all of which complicate the effective deployment of these resources. Motivated by these practical challenges and in collaboration with industry partners working on addressing these issues, this paper proposes a two-level data-driven smart controller for EV charging in distribution systems. The controller is modeled as a Deep Reinforcement Learning (DRL) agent, which coordinates the charging rates of multiple EVs connected to a realistic residential feeder with high penetration of PV generation. The first level coordinates the aggregated EV load at distribution Medium Voltage (MV) level to provide Demand Response (DR) services; at the Low Voltage (LV) level it aims to maximize the EVs’ state of charge at departure while avoiding the overloading of the MV/LV distribution transformers. The controller is verified through simulations on an actual utility grid facing the aforementioned challenges, demonstrating the effectiveness and practicality of the proposed DRL-based smart charging approach.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".