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• Develop an agent-based model of electric vehicle (EV) and vehicle-to-grid (V2G) adoption. • The EV-V2G model can explicitly simulate social influences on the EV and V2G adoption. • Only 3.3% of survey participants were familiar with V2G. • V2G selling price and advertisement were not very influential to the diffusion of V2G. The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent's preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several "what-if" scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment. [ABSTRACT FROM AUTHOR]

• Scenario-specific electric vehicle behavioral model. • Higher accuracy in representing electric vehicles' movements. • Applicability to timid, aggressive and normal vehicles. • Ability to represent traffic oscillations of electric vehicles. Electric vehicles (EVs) are deemed to be a solution for reducing air pollution and greenhouse gas emissions. As a result, the market share has increased exponentially in recent years. Despite their distinct vehicle dynamics and characteristics, movement simulation models dedicated to EVs are yet to be developed. In this research, a micro-traffic flow model for EVs by considering their unique acceleration/deceleration characteristics is proposed to represent and simulate the movements of EVs in traffic flow, especially in congested traffic. Car-following pairs where second car is an EV were collected from Longpan mid road, Nanjing, China in March 2019 for model calibration and verification. The results show that the proposed EV behavior model outperforms traditional behavior models for both timid and aggressive drivers. In assessing the predictive power of the movement simulation models, we compare their performance for collected car-following pairs. The R-squared values indicate that the performance of the EV behavior model is similar to that of the asymmetric behavior model under free-flow conditions, but substantially better for congested scenarios. With this model, we can better understand and reproduce the trajectories and energy consumption of EVs in complex traffic flow scenarios, and especially in congested traffic. [ABSTRACT FROM AUTHOR]

• Two optimal scheduling models of electric vehicles (EVs) charging are proposed. • The degradation cost and parking fee are considered in the costs of two methods. • Recommendation model considers inconvenience of EV owners in the costs. • Recommendation model can help EV owners reduce charging time and save costs. • Performance of models are compared in different charging patterns and EV scales. The electric vehicle (EV) industry has developed rapidly in the past decade, and the number of EVs has surged. When large scale EVs are connected to the power grid, the peak load of the power grid surges during the rush time of EV charging. The uncoordinated large scale of EV charging is harmful to the operation of the power system. Hence, dynamic pricing mechanisms have been proposed that use cost reduction as the motivation for EV owners to alter certain charging decisions. In this study, two optimal scheduling models for the charging of EVs, basic scheduling and recommendation models, are proposed to achieve cost minimization for EV owners in response to dynamic pricing. The basic scheduling model schedules the charging and discharging behaviours of EVs based on the connection and disconnection times of the EVs to the power grid. The costs involved in this model include the charging cost, discharging reward, degradation cost of EV battery, and parking fee. Based on the basic scheduling model, the proposed recommendation model recommends that the EV owner leave earlier to save on the parking fee, which may inconvenience the EV owner. This inconvenience is further considered in the costs, by measuring the coefficient of inconvenience to represent the time sensitivity of the EV owner. Finally, the comparison results of charging and discharging behaviours and the corresponding costs based on the two optimal scheduling models are sent to the EV owners. This can help EV owners, who have different degrees of sensitivity to time, to make optimal charging decisions. Moreover, the recommendation model can improve the utilization efficiency of charging facilities and relieve the pressure on them in the scheduling process when EVs are connected to the power grid on a large scale. [ABSTRACT FROM AUTHOR]

• Developing an optimal investment model for EVs considering market uncertainty. • Deriving an optimal decision path of EV investment based on fuel price movement. • Economic advantages of EVs for consumers with more logged distances. • Even without subsidies, EVs are more cost-effective than ICEVs in the Korean market. • As fuel price gets more stabilized, the likelihood of adopting EVs should increase. Electric vehicles (EVs) are considered as an effective solution in achieving a low-carbon society. One of the major hurdles to EV diffusion is consumer ignorance. That is, consumers generally consider only the initial purchase price, not knowing the total life cycle cost in terms of long-term operational cost savings. The purpose of this study is to develop a consumer-based optimal electric vehicle investment model using total cost of ownership (TCO) which is the actual total cost paid by consumers from acquisition to disposal. Especially, this model includes the recent rapid technological advances in EVs and high volatility of oil prices. To quantify the economic value of the options of consumer's decision, we estimated the TCO of vehicles. Specifically, to provide practical results to consumers, we used the empirical data from the Korean automobile market to evaluate the economic value of TCO. Using the proposed investment model with empirical data, we derived an optimal decision path as a function of time, considering oil price uncertainty and technological advancements, by analyzing a binomial lattice model to identify the optimal timing of adoption. We found that even without subsidies, EVs are more cost-effective than internal combustion engine vehicles (ICEVs) at the current fuel price level, and are still cost-effective even if the current fuel price decreases 20% further in the Korean market. And as the fluctuation of fuel price becomes more stable, consumer preference to EV products should increase. [ABSTRACT FROM AUTHOR]