Real-Time Smart Charging of Electric Vehicles for Demand Charge Reduction at Non-Residential Sites
Bibliographic record
Abstract
Smart electric vehicle (EV) charging deals with increasing demand charges caused by EV load on EV supply equipment (EVSE) hosts. This paper proposes a real-time smart charging algorithm that can be integrated with commercial & industrial EVSE hosts through building energy management system or with utility back office through the advanced metering infrastructure. The proposed charging scheme implements a real-time water-filling algorithm able to reduce the peak demand and to prioritize EV charging based on the data of plugged-in EVs. The algorithm also accommodates utility and local demand response and load control signals for extensive peak shaving. Real-world EV charging data from different types of venues are used to develop and evaluate the smart charging scheme for demand charge reduction at medium & large general service locations. The results show that even at constrained venues such as large retails, monthly demand charges caused by EVs can be reduced by 20%-35% for 30% EV penetration level without depreciating EVs' charging demand.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".