Bi-Level Optimization Framework for Heavy-Duty Electric Truck Charging Station Design

被引:3
作者
Jackson, Derek [1 ]
Cao, Yue [1 ]
Beil, Ian [2 ]
机构
[1] Oregon State Univ, Sch Elect Engn & Comp Sci, Corvallis, OR 97331 USA
[2] Grid Edge Solut, Portland Gen Elect, Portland, OR USA
来源
2022 IEEE/AIAA TRANSPORTATION ELECTRIFICATION CONFERENCE AND ELECTRIC AIRCRAFT TECHNOLOGIES SYMPOSIUM (ITEC+EATS 2022) | 2022年
基金
美国国家科学基金会;
关键词
Electric Vehicles; Trucks; Charging Stations; Bi-level Optimization; Multi-Objective Optimization; DC Microgrid;
D O I
10.1109/ITEC53557.2022.9813815
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
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 9-port 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.
引用
收藏
页码:563 / 568
页数:6
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