DEVELOPMENT OF ELECTRIC ROAD TRANSPORT: SIMULATION MODELLING

被引:1
作者
Katalevsky, D. Yu [1 ]
Gareev, T. R. [2 ]
机构
[1] Skolkovo Inst Sci & Technol, 100 Novaya St, Skolkovo 143025, Russia
[2] Russian Presidential Acad Natl Econ & Publ Adm RA, 82 Vernadskogo Pr, Moscow 119571, Russia
关键词
simulation modelling; system dynamics; electric transport; electric vehicles; charging stations infrastructure; region; demand forecasting; demand stimulation; Bass model; AnyLogic; VEHICLE; MARKET; INCENTIVES; DIFFUSION; DYNAMICS;
D O I
10.5922/2079-8555-2020-2-8
中图分类号
K9 [地理];
学科分类号
0705 ;
摘要
Electric transport is rapidly gaining popularity across the world. It is an example o f technological advancement that has multiple consequences for regional economies, both in terms of the adaptation of production, transport and energy systems and their spatial optimization. The experience of leading economic regions, including countries of the Baltic Sea region, shows that electric transport can potentially substitute traditional transport technologies. Based on an authentic model of system dynamics, the authors propose a new approach to simulation modelling of the dissemination o f electric vehicles in a given region. The proposed model allows the authors to take into account the key systemic feedback loops between the pool of electric vehicles and the charging infrastructure. In the absence of data required for the econometric methods of demand forecasting, the proposed model can be used for the identification of policies stimulating the consumer demand for electric vehicles in regions and facilitating the development of the electric transport infrastructure. The proposed model has been tested using real and simulated data for the Kaliningrad region, which due to its specific geographical location, is a convenient test-bed for developing simulation models of a regional scale. The proposed simulation model was built via the AnyLogic software. The authors explored the capacity of the model, its assumptions, further development and application. The proposed approach to demand forecasting can be further applied for building hybrid models that include elements of agent modelling and spatial optimization.
引用
收藏
页码:118 / 139
页数:22
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