Real‐time agent‐based control of plug‐in electric vehicles for voltage and thermal management of LV networks: formulation and HIL validation
Bibliographic record
Abstract
Ensuring a stable and reliable operation of current and future distribution networks represents a major challenge for system operators aggravated by the global proliferation of plug‐in electric vehicles (PEVs). While the introduction of a controlled charging process would be advantageous to minimise the impacts PEVs cause in the system, a suitable, efficient and ready to be implemented solution is still missing. The present work addresses this issue by proposing a smart charging management solution capable to simultaneously combat the main network impacts derived from the energy needs of the vehicles. This is done by means of an agent‐based hierarchical real‐time algorithm which combines a local decentralised nodal voltage management with a centralised thermal control conceived to minimise the impact upon participating users. The effectiveness of the proposed system is tested both using a simulation environment considering multiple PEV penetration levels and employing commercially available charging stations and cars through hardware‐in‐the‐loop simulations. The results reveal how all network violations are successfully attenuated by peak shaving the total aggregated charging demand and ensuring a correct system operation for all penetration scenarios while inflicting no impact on the participating users.
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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".