重型电动汽车充电站设计的双层优化框架

Derek Jackson, Yue Cao, I. Beil
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引用次数: 1

摘要

重型商用电动汽车(HDEV)充电站,如货运卡车,必须处理大的峰值电力需求。安装现场储能系统可以减少高峰充电需求,避免昂贵和超大的公用事业管理配电设备。为了保证充电基础设施的优化设计,需要考虑储能规模和电网设备额定值之间的权衡。在电力电子变流器最优尺寸和实际功率损耗模型约束下,提出了一种双层多目标优化框架来发现帕累托最优设计。在这些考虑下,双层方法将充电站优化分解为一个系统级问题和多个变流器级问题,可以大大简化设计过程。利用基于行业的HDEV到达时间和充电条件,这种双层方法被用于一个9端口充电站。由此产生的帕累托前沿展示了设备尺寸权衡,这对于知情的充电基础设施开发决策是必要的。将双层优化帕累托前沿与传统的固定效率转化器模型的帕累托前沿进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bi-Level Optimization Framework for Heavy-Duty Electric Truck Charging Station Design
Heavy-duty commercial electric vehicle (HDEV) charging stations, such as for freight trucks, must handle large peak power demands. Installing on-site energy storage can reduce the peak charging demand to avoid expensive and oversized utility-managed distribution equipment. To ensure optimal design of charging infrastructure, the trade-off between energy storage size and grid equipment ratings should be considered. This paper presents a bi-level multi-objective optimization framework to discover Pareto optimal designs, under the constraint of optimally sized power electronic converters and realistic power loss models. Under these considerations, the bi-level approach can greatly simplify the design process by breaking up charging station optimization into a system-level problem and multiple converter-level problems. Using industry-based HDEV arrival times and charging conditions, this bi-level approach is demonstrated for a 9port charging station. The resulting Pareto front showcases equipment sizing trade-offs that are necessary for informed charging infrastructure development decisions. The bi-level optimization Pareto front is compared the Pareto fronts of traditional, fixed efficiency converter models.
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