中断情景下EV充电站与多类型充电桩联合布局优化  

The Combination of EV Charging Station and Multi-Type Charging Piles under the Interruption Scenario

作　　者：范志强 师冉冉 梁宁宁 李姗姗 FAN Zhiqiang;SHI Ranran;LIANG Ningning;LI Shanshan(School of Business Administration Research Center of Energy Economy,Henan Polytechnic University,Jiaozuo Henan 454003;School of Finance and Economics Administration,Henan Polytechnic University,Jiaozuo Henan 454003,China)

机构地区：[1]河南理工大学工商管理学院能源经济研究中心 [2]河南理工大学财经学院,河南焦作454003

出　　处：《重庆师范大学学报（自然科学版）》2024年第2期119-128,共10页Journal of Chongqing Normal University:Natural Science

基　　金：国家自然科学基金面上项目(No.71502050);河南省哲学社会科学规划项目(No.2022BJJ048);河南省高校基本科研业务费专项项目(No.SKJZD2020-01);河南理工大学青年骨干教师资助计划(No.2019XQC-21)。

摘　　要：考虑到现有研究多是对充电站的选址进行规划,较少讨论中断情景与电动汽车(electric vehicle,EV)用户充电距离。因此,在中断情景下将研究范畴拓展至充电站与充电桩联合布局优化,以成本最小和距离最短为目标构建了多目标规划模型。针对问题的NP-困难特性,首先设计了基于K-Means聚类的启发式算法,以快速生成较好的初始可行解,然后提出改进遗传算法求解模型。通过算例分析,验证了模型的有效性。所建模型能够有效解决中断情景下的EV充电站与充电桩联合布局优化问题,所提算法在求解精度与稳定性方面要优于已有算法。Previous literature has less considered the interruption scenario and the convenience of charging for electric vehicle(EV)users,and more has focused on planning the location of charging stations.In view of this,the research scope is extended to the joint layout optimization of charging stations and multi type Charging station under the interruption scenario,and a multi-objective programming model is constructed with the goal of minimizing the cost and distance.In response to the NP hard characteristics of the problem,a heuristic algorithm based on K-Means clustering was first designed to quickly generate good initial feasible solutions,and then an improved genetic algorithm was proposed to solve the model.The validity of the model is verified by an example analysis.The model has good robustness and can effectively solve the joint layout optimization problem of EV charging station and Charging station under outage scenarios.The proposed algorithm is superior to existing algorithms in terms of solution accuracy and stability.

关 键 词：中断情景 多类型充电桩 多目标规划模型 K-MEANS聚类 改进遗传算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]