Research on Optimal Layout Strategy of Electric Vehicle Charging Network by Considering Land Resource and Policy Constraints

2019 2nd International Conference of Green Buildings and Environmental Management

生态环境科学

Ding, Ning^1 ; Yan, Hui^1 ; Jiang, Linru^2 ; Zhang, Jian^1 ; Han, Wenqi^3

Department of LoV Platform Center (Research Center), State Grid Electric Vehicle Service Company, Xicheng District, Beijing

100032, China^1

Department of Power Consumption and Energy Efficiency, China Electric Power Research Institute, Haidian District, Beijing

100192, China^2

Department of Electrical Engineering, Northeast Electric Power University, Jilin

132012, China^3

关键词: Chinese Government;    Cost of investment;    Electric vehicle charging;    Environmental problems;    Fossil energy consumption;    Operation and maintenance;    Policy constraints;    Utilization of resources;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/310/3/032034/pdf
DOI  :  10.1088/1755-1315/310/3/032034

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

With the increasing demand of people for transportation, the number of automobiles in cities continues to increase, which leads to haze weather, PM2.5 pollution and other environmental problems. In order to reduce fossil energy consumption and carbon emissions, the development of energy-saving and environmentally friendly transportation tools has become the focus of attention. In this paper, the electric vehicle industry has developed rapidly, the sales market has begun to take shape, and the Chinese government has given policy support such as purchase subsidies. However, the layout of charging network of regional electric vehicles represented by residential areas is not perfect, which leads to the low satisfaction of users' charging demand, the insufficient utilization of resources and the high cost of charging station network. In this paper, a single objective non-linear programming model for minimizing the cost of investment, operation and maintenance of charging stations with resource and policy constraints is established. In the optimization of the model, combined with the advantages of particle swarm optimization and genetic algorithm, the hybrid algorithm of particle swarm optimization and genetic algorithm is selected to obtain the optimal solution of the model. Taking a residential area of 10.5 km2 as an example, the optimized charging station and its service scope are obtained, the location and capacity of charging station are determined, and the accuracy and validity of the model are verified.