考虑充换电操作的新能源汽车换电站选址定容问题  

The location and capacity planning of new energy vehicle battery swapping stations with integrated charging and swapping operations

作　　者：王志远 郭贤 冉伦[1,2] 姚兆胜 WANG Zhiyuan;GUO Xian;RAN Lun;YAO Zhaosheng(School of Management,Beijing Institute of Technology,Beijing 100081,China;Digital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information Technology,Beijing Institute of Technology,Beijing 100081,China;Institute of Data and Information,Tsinghua Shenzhen International Graduate School,Tsinghua University,Shenzhen 518055,China;Tsinghua-Berkeley Shenzhen Institute,Tsinghua University,Shenzhen 518055,China)

机构地区：[1]北京理工大学管理学院,北京100081 [2]北京理工大学数字经济与政策智能工业和信息化部重点实验室,北京100081 [3]清华大学清华深圳国际研究生院数据与信息研究院,深圳518055 [4]清华大学清华-伯克利深圳学院,深圳518055

出　　处：《系统工程理论与实践》2024年第12期3963-3978,共16页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(72272014,91746210,72061127001)。

摘　　要：本文考虑了联合充换电操作的新能源汽车换电站的选址定容问题.换电站内部充换电操作是连接换电需求与换电站选址定容决策的关键一环,但以往研究都忽略了对该过程的详细刻画.本文将换电站内部充换电操作建模为多周期优化问题,并给出了该问题最优解的结构性质.在此基础上考虑换电需求的不确定性,将换电站内部运营与换电站选址定容决策结合起来,构建了分布式鲁棒优化模型和目标鲁棒式优化模型.针对模型中难求解的多阶段问题,本文利用线性决策准则近似求解两模型,并将融合辅助变量的升维技术扩展到考虑情景的多阶段鲁棒优化模型中,从理论上给出了升维前后模型之间的关系.最后进行数值实验,验证了本文所提模型和升维技术的有效性.This paper addresses the location and capacity planning of battery swapping stations of electric vehicles,combining the charging and swapping operations in the stations.The charging and swapping operations within the swapping station are a crucial link connecting the swapping demand with the decisions on station location and capacity planning.However,previous research has overlooked providing a detailed characterization of this process.This study models the internal operations of the swapping station as a multi-period optimization problem and provides insights into the structural properties of the optimal solution to this problem.Building upon this foundation, considering the uncertainty in swapping demands, we integrate the internaloperational aspects with the station location and capacity planning to construct a distributionallyrobust optimization model and a robust satisficing model. To deal with the hard multistageproblem in the model, we utilize the linear decision rule to approximately solve the two modelsand extend the lifting technique by incorporating auxiliary variables into multistage scenariowiserobust optimization models. The theoretical analysis establishes the relationship betweenthe models before and after lifting. Finally, numerical experiments are conducted to validate theeffectiveness of the proposed model and lifting techniques.

关 键 词：换电站选址定容 充换电操作 分布式鲁棒优化 目标鲁棒式优化 

分 类 号：O221.5[理学—运筹学与控制论]