Infrastructure enabled and electrified automation: Charging facility planning for cleaner smart mobility
Infrastructure enabled and electrified automation: Charging facility planning for cleaner smart mobility
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DOI：
10.1016/j.trd.2021.103079
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Bahar Azin;X. Yang;Nikola Marković;Mingxi Liu
中科院分区：
文献类型：
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作者：
Bahar Azin;X. Yang;Nikola Marković;Mingxi Liu
由于更高的能源效率和更低的排放，电动汽车（EV）已经成为开发清洁移动系统的有吸引力的交通工具。此外，许多未来的自动驾驶汽车（AV）可以电气化。因此，现有市场将经历自动电动汽车（AEV）的急剧增长。对于基础设施启用自动化（IEA），充电设施规划需要适应不断增长的AEV充电需求。规划过程还必须考虑它们对电网的影响。提出了一种电力-运输耦合网络的综合需求覆盖优化模型。该模型旨在确定AEV充电站的候选位置，这些位置将满足交通网络中最大的充电需求，考虑到AEV中即将到来的技术也将影响可能影响充电系统的充电行为。此外，在每个充电站的电网限制被认为是最小的电力成本的网络。将该模型应用于犹他州的公路网，以确定最佳的充电站位置。
Due to higher energy efficiency and lower emissions, electric vehicles (EVs) have become attractive transportation means in developing cleaner mobility systems. Moreover, many future automated vehicles (AV) can be electrified. Hence, existing market will experience a drastic growth in automated electric vehicles (AEVs). For infrastructure enabled automation (IEA), charging facility planning is required to accommodate the increasing AEV charging demand. The planning process must also account for their impact on the power grid. This study presents an integrated demand coverage optimization model over a coupled power-transportation (CPT) network. This model aims to pinpoint candidate locations of AEV charging stations that would serve the most charging demand in the transportation network, considering the upcoming technologies in AEV also will affect the charging behavior that can influence the charging system. Besides, power grid limitations at each charging station are considered for the minimal power cost of the network. The developed model is applied to Utah state road network to determine the optimal charging station locations.