Summary Table
The summary table contains the Literature name and Results of the referred literature.
| No. | Literature | Results |
|---|---|---|
| 1 | Helveston (2023) | The reproduction of the examples can be accessed here. |
| 2 | Tarroja and Hittinger (2021) | Both smart charging and V2G are proved to be valuable in terms of achieving GHG emission reduction. V2G saves much more because it helps saving on stationary energy storage. |
| 3 | Brand, Israeli, and Ngwe (2023) | OpenAI API can help generate automatic workflow in customer preference generation and data collection. GPT’s simulation is proved to be realistic in demand curve, annual income, past purchase, and diminishing marginal utility. |
| 4 | Parsons et al. (2014) | Incentives of V2G should be increased to hedge inconvenience of car usage and people’s unawareness of the profit they can make. The incentives can either be based on power contributions, or as a form of upfront EV discounts. |
| 5 | Bailey et al. (2023) | EV owners significantly adjust their charging behavior in response to financial incentives, but revert to original habits once incentives are removed, while moral suasion has no noticeable impact. |
| 6 | Wong et al. (2023) | Although some people support smart charging even without incentives, monetary rewards are still important for a larger amount of people. However, monetary rewards undergo a diminishing marginal utility (with a cap as well). Assured battery levels and alternative transportation solution will encourage people’s willingness to smart charging. On the contrary, penalties for taking back charging control will hurt the willingness, especially for EV owners. |
| 7 | Tarroja et al. (2016) | Pros: V2G is more efficient in utilizing renewable energy sources and lead to reduced GHG emissions. It reduces the required capacity of stationary energy storage. Cons: V2G is not good at grid stability due to unpredictable customer travel patterns, and it highly depends on charging infrastructure. The benefits of V2G can be varied based on the policymakers’ aims. |
| 8 | Sovacool, Axsen, and Kempton (2017) | Vehicle-Grid Integration (VGI) has significant benefits but faces a range of challenges in terms of technical, financial, behavioral, and cultural. An interdisciplinary and sociotechnical approach is preferred in order to successfully implement and gain acceptance for VGI systems. |
| 9 | Veselovsky et al. (2023) | Primary findings: without any intentional direction, the estimated prevalence of LLM use was 30%. However, if there are literal limits or copy-pasting limits, the usage was decreased by a half. Secondary analyses: LLM produces high-quality but homogeneous responses, which may harm future LLM training. |
| 10 | Matthew D. Dean and Kockelman (2024) | Program Adoption: 25.7% of participants refuse controlled charging of any forms. 8.7% would control by themselves. 25.4% need more info before making decisions. 37.0% supports smart charging. Logit Models: Multinomial logit model reveals that BEV owners and those with wholesale power prices are more willing to adopt smart charging. Ordered logit models reveal the one-time and annual cash back coefficients. |
| 11 | Matthew D. Dean and Kockelman (2023) | Younger users (age <34) have higher WTP to V2L and V2H. Households with more vehicles tend to use them more frequently. |
| 12 | Constance | Prof Contance Crozier has conducted a smart charging survey in 2022 for European users. |
| 13 | Khezri, Steen, and Anh Tuan (2024) | Swedish EV drivers are more interested in the V2H application than in V2G. Range anxiety deals more concern than battery degradation. |
| 14 | Kühne and Zindel (2020) | Social media is an efficient new approach of survey recruitment. Meta (Facebook & Instagram) is a very good application of it. The reason is simply that people want their voice being heard. |
| 15 | Neundorf and Öztürk (2022) | It’s important to apply incentives in web-based surveys to efficiently recruit more participants, but the type of incentive should be carefully considered. |
| 16 | Philip and Whitehead (2024) | A good set of smart charging incentives and guaranteed driving range will surely increase customer willingness. |
| 17 | Hoogland et al. (2023) | Federal tax credit is vital for both PEV lessees and buyers. |
| 18 | Heuveln et al. (2021) | Among the 20 interviews to the Dutch EV drivers, some of whom used V2G before, most of them support V2G. Positive effects are financial compensation, transparent communication and reliable control of the system. Negatives effects are range anxiety, discomfort experienced while participating and battery degradation. Demographics data were collected and only conceptual model was used. There is no simulation or mathematical modeling. |
| 19 | Kubli (2022) | A choice experiment was conducted in Switzerland on 202 current and potential EV drivers for their willingness to smart charging in varied location, duration, cost, and moderation, where cost showed major influence. The study found that high willingness should be reinforeced by sufficient compensation, and sweet home bias exists. This study is too generic and failed to account for a specific location and grid. The sample size of 202 with only 9% being real EV owners is not convincing enough. |
| 20 | Huang et al. (2021) | A study on Dutch BEV owners revealed that willingness to particiapte in a V2G program increases if BEVs can be quickly recharged, making access to a level-2 charger essential for the program. 99% of the respondents claimed to have driven a BEV, but their total sample was only 157 respondents. |