Sharing private charging piles to develop electric vehicle charging and vehicle-to-grid services
Bibliographic record
Abstract
The increasing use of electric vehicles (EVs) has led to challenges in determining the most effective methods for charging their batteries. A potential solution to address this issue is the expansion of smart homes equipped with renewable energy sources, such as wind turbines and solar panels, to meet the growing demand for EV charging. By installing private charging piles (PCPs) in homes and enabling their sharing, both homes and EVs can benefit economically. Moreover, these PCPs can provide vehicle-to-grid services that can enhance the stability of the power system. This study develops a two-stage stochastic model to optimize the energy system of homes equipped with various types of distributed generation and PCPs. To address the role of uncertainty in parameters such as demands and renewable power generation, a sample average approximation (SAA) method is used to optimize the model. The SAA method is developed in a way that an autoregressive moving average (ARMA) model is employed to generate scenarios, and a fuzzy c-means (FCM) clustering algorithm is utilized to reduce the number of scenarios. This study utilizes data from the city of Calgary, Canada, to examine the application of the proposed model in a real-life setting. The results demonstrate that sharing PCPs benefits households by optimizing energy use, supports EV owners by making charging more convenient, and helps governments by reducing the need to build additional public charging stations.
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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 | 0.001 | 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".