确定马来西亚光伏电动汽车充电站合适位置的标准需求分析

IF 0.8 Q3 MULTIDISCIPLINARY SCIENCES Malaysian Journal of Fundamental and Applied Sciences Pub Date : 2023-12-04 DOI:10.11113/mjfas.v19n6.3114
R. N. Farah, N. A. Syahirah, N. Misron, M. S. M. Azmi, N. S. A. Karim, N.M. Husin
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引用次数: 0

摘要

主要的问题是缺乏对光伏电动汽车充电站(PEVCS)的科学规划,该规划考虑了许多主要标准和子标准。PEVCS应战略性地放置在适当和理想的位置,以确保电动汽车(EV)用户可以在其行驶里程内到达站点。虽然马来西亚对太阳能的采用仍然很少,但马来西亚需要更快地在战略位置分配PEVCS。关于这个问题,本研究旨在确定在马来西亚分配PEVCS位置的合适标准。在本研究中,作为数据收集程序的一部分,为需求分析选择了177个子标准和6个主要标准项目中的52个,其中涉及12个受访者。结果显示,需求分析用于选择41个子标准,包括社会(8),经济(10),环境(7),技术(6),可达性(6)和邻近性(4)。对于未来的研究,建议使用李克特量表来分析需求分析的数据,同时计算平均值和标准差值。同时利用基于gis的MCDM方法,通过开发新的预测位置模型,在马来西亚分配理想的PEVCS位置。
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A Need Analysis of the Criteria Involved in Determining Suitable Locations for Photovoltaic Electric Vehicle Charging Stations in Malaysia
The main concern is a lack of scientific planning for photovoltaic electric vehicle charging stations (PEVCS) that considers numerous main criteria and sub-criteria. PEVCS should be strategically positioned in an appropriate and ideal location to ensure that electric vehicle (EV) users may reach the stations within their driving range. While the adoption of solar is still minimal in Malaysia, Malaysia needs to move faster to allocate the PEVCS at the strategic locations. Regarding this matter, this study aims to determine the suitable criteria for allocating the location of PEVCS in Malaysia. 52 out of 177 sub-criteria and six main criteria items were selected for the Need Analysis in this study as part of the data collecting procedure, which involved 12 respondents. The result revealed that the Need Analysis was used to choose 41 of the sub-criteria, including society (8), economics (10), environment (7), technology (6), accessibility (6), and proximity (4). For future studies, it is recommended to use Likert scales for analysing the data from the Need Analysis, along with calculating the mean and standard deviation values, while utilizing GIS-based MCDM methods to allocate ideal PEVCS locations in Malaysia through the development of a new prediction location model.
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CiteScore
1.40
自引率
0.00%
发文量
45
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