Clustering-based EV suitability analysis for grid support services
Bibliographic record
Abstract
The literature extensively discusses the benefits of electric vehicles (EVs) for grid support services. However, not all EVs are suitable for these services due to variations in service requirements (duration and frequency) and EV driver behavior (available energy, parking duration, and battery degradation). This study proposes a three-step EV classification approach to assist aggregators in selecting appropriate EVs for specific services. First, using data from the National Household Travel Survey and a commercial EV database, various EV parameters are estimated, including available energy for grid support services, the duration of parking at home and at work, and the battery degradation factor. Second, the K-means clustering method is applied to categorize EVs based on each parameter, chosen for its superior performance and lower complexity compared to other clustering methods. Finally, suitability indices are proposed for each service, taking into account the service requirements and EV parameters of different clusters. Each EV is then ranked to help the aggregators select the best-suited EVs for each service. The performance of the proposed method is evaluated for two ancillary services (frequency regulation and ramping) and two operating reserve services (contingency spinning and supplemental reserves). Simulation results demonstrate that more EVs are suitable for home services due to longer parking hours, while those with low parking duration are unsuitable for workplace services, despite low degradation scores. Additionally, the proposed method consistently shows higher poolable energy compared to the random selection method, with differences reaching 56 kW (10.1%) for 10 EVs, 383 kW (26.8%) for 25 EVs, and 895 kW (31.2%) for 50 EVs. • A classification approach is proposed to help aggregators select suitable EVs. • EVs are clustered based on vehicle parameters and service needs. • Suitability indices are formulated for each EV for various services. • Evaluation of two ancillary and two operating reserve services is performed. • Achieving higher poolable energy is possible at homes compared to workplaces.
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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 | 0.001 |
| 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".