Utility-scale energy storage system for managing EV load in connected distribution circuits
Bibliographic record
Abstract
Increased penetration of electric vehicles (EVs) may exacerbate the residential peaks in distribution circuits due to the coincidence of the residential and EV peak loads. Meanwhile, the residential circuits are under-utilized during off-peak hours and are potentially overloaded during peak hours. Therefore, in this study, the deployment of utility-scale battery energy storage systems (BESSs) at strategic locations is proposed to mitigate the overloading of adjacent circuits. The BESS can absorb power from two connected circuits during off-peak intervals, thus enhancing the circuit utilization. In addition, it can inject power to both circuits during their respective peak hours, thus avoiding network overloading. To this end, an optimization model is developed to determine the optimal sizes of BESSs considering EV loads. Estimation of the EV load is carried out using the EV data, travel patterns of EV drivers, and charger types based on Edmonton, Canada, local data. Simulation results have shown the efficacy of BESSs in reducing the peak-to-off-peak load difference in residential circuits.
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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 | 0.000 |
| 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 | 0.001 | 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".