• Energy Research

Abstract This paper investigates the potential expansion and impacts of Electric Vehicle (EV) market in Beijing, China at the micro level with an agent-based integrated urban model (SelfSim-EV), considering the interactions, feedbacks and dynamics found in the complex urban system. Specifically, a calibrated and validated SelfSim-EV Beijing model was firstly used to simulate how the EV market might expand in the context of urban evolution from 2016 to 2020, based on which the potential impacts of EV market expansion on the environment, power grid system and transportation infrastructures were assessed at the multiple resolutions. The results suggest that 1) the adoption rate of Battery Electric Vehicle (BEV) increases over the period, whereas the rate of Plug-in Hybrid Electric Vehicle (PHEV) almost remains the same; Furthermore, the so-called neighbour effects appear to influence the uptake of BEVs, based on the spatial analyses of the residential locations of BEV owners; 2) the EV market expansion could eventually benefit the environment, as evident from the slight decrease in the amounts of HC, CO and CO2 emissions after 2017; 3) Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system; 4) the EV market expansion could influence several EV-related transport facilities, including parking lots, refuelling stations, and charging posts at parking lots, in terms of quantity, layout and usage. These results are expected to be useful for different EV-related stakeholders, such as local authorities and manufacturers, to shape polices and invest in technologies and infrastructures for EVs.

Water and energy consumptions in the residential sector are highly correlated. A better understanding of the correlation would help save both water and energy, for example, through technological innovations, management and policies. Recently, there is an increasing need for a higher spatiotemporal resolution in the analysis and modelling of water-energy demand, as the results would be more useful for policy analysis and infrastructure planning in both water and energy systems. In response, this paper developed an agent-based spatiotemporal integrated approach to simulate the water-energy consumption of each household or person agent in second throughout a whole day, considering the influences of out-of-home activities (e.g., work and shopping) on in-home activities (e.g., bathing, cooking and cleaning). The integrated approach was tested in the capital of China, Beijing. The temporal results suggested that the 24-hour distributions of water and related energy consumptions were quite similar, and the water-energy consumptions were highly correlated (with a Pearson correlation coefficient of 0.89); The spatial results suggested that people living in the central districts and the central areas of the outer districts tended to consume more water and related energy, and also the water-energy correlation varies across space. Such spatially and temporally explicit results are expected to be useful for policy making (e.g., time-of-use tariffs) and infrastructure planning and optimization in both water and energy sectors.

Policy is an influential factor to the purchase and usage of Electric Vehicles (EVs). This paper is focused on the license plate lottery policy, a typical vehicle purchase restriction in Beijing, China. An agent-based spatial integrated urban model, SelfSim-EV, is employed to investigate how the policy may influence the uptake of EVs over time at the individual level. Two types of “what-if” scenario were set up to explore how the methods to allocate the vehicle purchase permits and the number of permits might influence the EV market expansion from 2016 to 2020. The results suggested that 1) both the allocation methods and the number of purchase permits could heavily influence the uptake of EVs and further its impacts on vehicular emissions, energy consumption and urban infrastructures; 2) compared to the baseline, both scenarios got significantly different spatial distributions of vehicle owners, transport facilities, vehicular emissions and charging demand at the multiple resolutions; 3) SelfSim-EV was found as a useful tool to quantify the nonlinear relationships between the increase of EV purchasers and the demand for transport facilities and electricity, and also to capture some unexpected results coming out from the interactions in the complex dynamic urban system.

The market penetration rate of electric vehicle (EV) is on the rise globally. However, the use behaviors of private EVs have not been well understood, in part due to the lack of proper datasets. This paper used a unique dataset containing trajectories of over 76,000 private EVs (accounting for 68% of the private EV fleet) in Beijing to uncover trip, parking and charging patterns of private EVs, so as to better inform policy making and infrastructure planning for different EV-related stakeholders, including planners, vehicle manufacturers, and power grid and infrastructure companies. We conducted both statistical and spatiotemporal analyses. In terms of statistical patterns, most of the EV trip distances (over 71%) were shorter than 15 km. Also, most of parking events (around 76%) lasted for less than 1 h. From a spatial perspective, the densities of trip Origins and Destinations (ODs), parking events and charging events in the central districts tended to be much higher than those of the other districts. Furthermore, the number of intra-district trips tended to be much higher than the number of inter-district trips. In terms of temporal trip patterns, there were two peak periods on working days: a morning peak period from 7 to 9 AM, and an afternoon peak period from 5 to 7 PM; On non-working days, there was only one peak period from 9 AM to 5 PM; while the temporal charging patterns on working and non-working days had a similar trend: most of EV drivers got their EVs charged overnight. Finally, we demonstrated how to apply the observed statistical and spatiotemporal patterns into policy making (i.e., time-of-use tariff) and infrastructure planning (i.e., deployment of normal charging posts, enroute fast charging stations and vehicle-to-grid enabled infrastructures).

An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling.

Abstract The pathways to the uptake of Electric Vehicles (EVs) may vary across cities and regions. This paper investigated characteristics and attitudes of early EV adopters from three different classes of Chinese cities, namely Beijing, Wuhan and Shijiazhuang, which were defined as the upper, middle and lower classes of cities, respectively. A questionnaire survey was conducted in 2017 in the three study areas separately, targeting at actual EV adopters. In total, 1119 samples were collected. Discrete choice models were developed to relate decisions and attitudes of EV adopters to their sociodemographic characteristics and the class of a city which they were from. The results suggested that the class of a city was a statistically significant factor to several of the decisions and attitudes, including the reason for choosing EVs, actual payment made for EVs owned, preferences towards EVs and demand for public charging infrastructure. Specifically, the early adopters from the lower class of city tended to pay less for purchasing EVs. Also, they tended to agree on that EVs were generally better than Conventional Vehicles (CVs) given that their purchase costs were the same. As a result, over 80% of them would still purchase an EV given that no financial incentives were provided. Furthemore, those early EV adopters from the lower class of city tended to accept a lower density of charging stations. Finally, the potential applications of the empirical findings in policy making and infrastructure planning were discussed.

Abstract Technology innovations are expected to overcome several barriers to the uptake of Electric Vehicles (EVs). This paper explored the role of battery and charging technologies in the diffusion of EVs. Specifically, four groups of “what-if” scenario in Beijing were set up to assess the potential impacts of battery cost (i.e., EV price), battery capacity (i.e., driving range), battery swap stations and fast charging posts on the expansion of EV market. An agent-based spatial integrated model (SelfSim-EV) was used to simulate how vehicle consumers might respond to these technological innovations over time. The results suggested that 1) Plug-in Hybrid Electric Vehicle (PHEV) became competitive when its sale price decreased over time at a yearly rate of 8%, due to the decrease in battery cost; 2) Increasing the driving range of Battery Electric Vehicle (BEV) had little influence on the total number of vehicle purchasers, but did increase electricity consumption; 3) Deploying fast charging infrastructures, i.e., battery swap stations and fast charging posts, had little influence on the uptake of EVs at the macro level, suggesting that fast charging facilities might not be necessary at the early stage of EV development.