电动汽车充电基础设施的数据驱动多目标优化

Data-driven multi-objective optimization for electric vehicle charging infrastructure.

作者信息

Farhadi Farzaneh, Wang Shixiao, Palacin Roberto, Blythe Phil

机构信息

School of Engineering, Newcastle University, Stephenson Building, Newcastle upon Tyne NE1 7RU, UK.

School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne NE4 5TG, UK.

出版信息

iScience. 2023 Aug 31;26(10):107737. doi: 10.1016/j.isci.2023.107737. eCollection 2023 Oct 20.

DOI:10.1016/j.isci.2023.107737

PMID:37720110

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10502409/

Abstract

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%.

摘要

本文提出了一种结合模拟和多目标优化的数据驱动方法，以有效落实交通政策承诺，案例研究对象是英国泰恩河畔纽卡斯尔的电动汽车（EV）充电基础设施。该方法利用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，估算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，以及相应的总资本和运营支出及充电点运营时间。从数量上看，四种情景下不同类型充电点所占比例的变化相对较小，在充电点总数的3%范围内。最优解决方案优先考虑较慢的充电点，较快的充电点所占比例较小，各约为10%-13%。