Electric vehicle demand estimation and charging station allocation using urban informatics
Electric vehicle demand estimation and charging station allocation using urban informatics
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DOI：
10.1016/j.trd.2022.103264
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Zhiyan Yi;X. Liu;R. Wei
中科院分区：
文献类型：
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作者：
Zhiyan Yi;X. Liu;R. Wei
本文提出了一种新的数据驱动的方法来优化电动汽车（EV）公共充电。我们将研究区域转化为有向图，将其划分为离散的网格。基于出行起点-目的地（OD）和社会维度特征，提出了一种改进的地理PageRank（MGPR）模型来估计电动汽车充电需求，并将其与真实充电数据进行了对比验证，将结果输入容量约束最大覆盖选址问题（CMCLP）模型，以最大化公共充电站的利用率来优化公共充电站的空间布局.结果表明，MGPR可以有效地量化电动汽车充电需求与令人满意的精度。基于CMCLP模型的电动汽车充电站优化可以弥补电动汽车需求与现有充电站分配之间的空间不匹配。所开发的方法框架具有高度的可推广性，可以扩展到其他地区的电动汽车充电需求估计和最佳充电基础设施选址。
This paper performs a novel data-driven approach to optimize electric vehicle (EV) public charging. We translate the study area into a directed graph by partitioning it into discrete grids. A modified geographical PageRank (MGPR) model is developed to estimate EV charging demand, built upon trip origin–destination (OD) and social dimension features, and validated against real-world charging data. The results are fed into the capacitated maximal coverage location problem (CMCLP) model to optimize the spatial layout of public charging stations by maximizing their utilization. It is shown that MGPR can effectively quantify the EV charging demand with satisfactory accuracy. Optimized EV charging stations based on the CMCLP model can remedy the spatial mismatch between the EV demand and the existing charging station allocations. The developed methodological framework is highly generalizable and can be extended to other regions for EV charging demand estimation and optimal charging infrastructure siting.