Spatiotemporal model for estimating electric vehicles adopters. (15th September 2019)

• Record Type: Journal Article
• Authors: Rodrigues, João L.; Bolognesi, Hugo M.; Melo, Joel D.; Heymann, Fabian; Soares, F.J.
• Is Part Of: Energy. Volume 183(2019)
• Page Start: 788
• Page End: 802
• Publication Date: 2019-09-15
• Subjects: Sustainable city planning -- Geographical information systems -- Spatial regression -- Electric vehicle adopters; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.06.117
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The use of fossil fuel vehicles is one of the factors responsible for the degradation of air quality in urban areas. In order to reduce levels of air pollution in metropolitan areas, several countries have encouraged the use of electric vehicles in the cities. However, due to the high investment costs in this class of vehicles, it is expected that the spatial distribution of electric vehicles' adopters will be heterogeneous. The additional charging power required by electric vehicles' batteries can change operation and expansion planning of power distribution utilities. In addition, urban planning agencies should analyze the most suitable locations for the construction of electric vehicle recharging stations. Thus, in order to provide information for the planning of electric mobility services in the city, this paper presents a spatiotemporal model for estimating the rate of electric vehicles' adopters per subareas. Results are spatial databases that can be viewed in geographic information systems to observe regions with greater expectancy of residential electric vehicle adopters. These outcomes can help utilities to develop new services that ground on the rising availability of electric mobility in urban zones. Highlights: The proposed methodology estimates the rate of EVs' adopters per subareas. The urban spatial interaction for the possibility of purchasing EVs are characterized. The spatial distribution of the purchase of EVs in the first years of their penetration is estimated. The result of the proposal can be processed by any agency's GIS involved.