为了实现“双碳”目标,助力新能源电动汽车的发展,需进行充电基础设施的先行规划。针对目前充电站的最初待选点的选址缺乏科学支撑的问题,本文拟构建公里级的充电站最初选址模型,为充电站的最初选址提供科学、精准的依据。以银川市为研...为了实现“双碳”目标,助力新能源电动汽车的发展,需进行充电基础设施的先行规划。针对目前充电站的最初待选点的选址缺乏科学支撑的问题,本文拟构建公里级的充电站最初选址模型,为充电站的最初选址提供科学、精准的依据。以银川市为研究区,采用网格划分法将研究区划分成公里级网格,选择对充电站分布影响较大的标准路网密度、各类POI点、GDP、人口密度等主要因素,基于空间稀疏主成分分析方法,构建充电站需求指数,并结合充电站的服务半径、现有充电站数量,构建充电站需求指数的修正模型,进而对拟新建充电站进行空间、数量上的分配,以此获取银川市每个网格上公里级的建议新建充电站数量。通过上述方法的分配结果表明:充电站选址分配区域集中在银川市庆兴区、金凤区与贺兰县、永宁县、西夏区交界处,可以弥补这些相对发达区域充电站的不足。本研究构建的公里级的充电站最初选址模型为后续充电站布局的优化提供了参考。In order to achieve the goal of “double-carbon” and help the development of new energy electric vehicles, the first planning of charging infrastructure must be carried out. In view of the lack of scientific support for the initial site selection of charging stations, this paper plans to build the initial site selection model of km-level charging stations to provide a scientific and accurate basis for the initial site selection of charging stations. Taking Yinchuan City as the study area, the meshing method is used to divide the study area into km-level grid, selecting standard road network density, various POI points, GDP, population density and other major factors that have a greater impact on the distribution of charging stations, based on space sparse principal component analysis, building charging station demand index, and according to the service radius and the number of existing charging stations, building charging station demand index correction model, and then the proposed new charging stations are allocated spatially and quantitatively, so as to obtain the number of proposed new charging stations at the km-level on each grid in Yinchuan City. The allocation results of the above method show that the site selection and distribution area of charging stations is concentrated at the junction of Qingxing, Jinfeng and Helan, Yongning and Xixia District of Yinchuan City, which makes up for the shortage of charging stations in these relatively developed areas. In this study, the initial site selection model of km-level charging station provides a reference for the subsequent optimization of charging station layout.展开更多
文摘为了实现“双碳”目标,助力新能源电动汽车的发展,需进行充电基础设施的先行规划。针对目前充电站的最初待选点的选址缺乏科学支撑的问题,本文拟构建公里级的充电站最初选址模型,为充电站的最初选址提供科学、精准的依据。以银川市为研究区,采用网格划分法将研究区划分成公里级网格,选择对充电站分布影响较大的标准路网密度、各类POI点、GDP、人口密度等主要因素,基于空间稀疏主成分分析方法,构建充电站需求指数,并结合充电站的服务半径、现有充电站数量,构建充电站需求指数的修正模型,进而对拟新建充电站进行空间、数量上的分配,以此获取银川市每个网格上公里级的建议新建充电站数量。通过上述方法的分配结果表明:充电站选址分配区域集中在银川市庆兴区、金凤区与贺兰县、永宁县、西夏区交界处,可以弥补这些相对发达区域充电站的不足。本研究构建的公里级的充电站最初选址模型为后续充电站布局的优化提供了参考。In order to achieve the goal of “double-carbon” and help the development of new energy electric vehicles, the first planning of charging infrastructure must be carried out. In view of the lack of scientific support for the initial site selection of charging stations, this paper plans to build the initial site selection model of km-level charging stations to provide a scientific and accurate basis for the initial site selection of charging stations. Taking Yinchuan City as the study area, the meshing method is used to divide the study area into km-level grid, selecting standard road network density, various POI points, GDP, population density and other major factors that have a greater impact on the distribution of charging stations, based on space sparse principal component analysis, building charging station demand index, and according to the service radius and the number of existing charging stations, building charging station demand index correction model, and then the proposed new charging stations are allocated spatially and quantitatively, so as to obtain the number of proposed new charging stations at the km-level on each grid in Yinchuan City. The allocation results of the above method show that the site selection and distribution area of charging stations is concentrated at the junction of Qingxing, Jinfeng and Helan, Yongning and Xixia District of Yinchuan City, which makes up for the shortage of charging stations in these relatively developed areas. In this study, the initial site selection model of km-level charging station provides a reference for the subsequent optimization of charging station layout.
