Data-driven multi-objective optimization for electric vehicle charging infrastructure.
Data-driven multi-objective optimization for electric vehicle charging infrastructure.
复制标题
数据驱动的电动汽车充电基础设施多目标优化。
DOI：
10.1016/j.isci.2023.107737
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发表时间：
2023-10-20
期刊：
影响因子：
5.8
通讯作者：
Blythe, Phil
中科院分区：
文献类型：
--
作者：
Farhadi, Farzaneh;Wang, Shixiao;Palacin, Roberto;Blythe, Phil
以英国泰恩河畔纽卡斯尔的电动汽车（EV）充电基础设施为例，提出了一种结合仿真和多目标优化的数据驱动方法，有效地实现交通政策承诺。该方法利用了我们的行业合作伙伴奥雅纳集团有限公司开发的基线模拟模型，以估计2020年至2050年的电动汽车需求和数量。考虑了四种未来能源情景，并采用多目标优化方法来确定充电点的最佳类型，位置和数量，沿着相应的总资本和运营支出以及充电点运营时间。从数量上看，四种场景下不同类型充电点的比例变化较小，占充电点总数的3%范围内。最佳解决方案将优先考虑较慢的充电点，较快的充电点具有较小的部分，每个约为10%-13%。电动汽车充电基础设施规划的数据驱动优化方法未来电动汽车充电站的模拟和多目标优化相结合通过充电类型的更高多样性和更高的功率点降低成本电气工程;能源工程;能源资源
This paper presents a data-driven methodology combining simulation and multi-objective optimization to efficiently implement transportation policy commitments, using as a case study the electric vehicle (EV) charging infrastructure in Newcastle upon Tyne, United Kingdom. The methodology leverages a baseline simulation model developed by our industry partner, Arup Group Limited, to estimate EV demand and quantities from 2020 to 2050. Four future energy scenarios are considered, and a multi-objective optimization approach is employed to determine the optimal types, locations, and quantities of charging points, along with the corresponding total capital and operational expenditures and charging point operating hours. Quantitatively, the variations of the portions of different types of charging points for the four scenarios are relatively small and within 3% range of the total number of charging points. The optimal solutions put priority on the slower charging points, with faster charging points having smaller portions each around 10%–13%. Data-driven optimization method for electric vehicle charging infrastructure planning Combining simulation and multi-objective optimization for future EV charging stations Cost reduction with higher diversity in the charging types and higher power points Electrical engineering; Energy engineering; Energy Resources
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