面向资源约束的电动公交车充电调度策略  

Resource constraint-oriented charging scheduling strategy for electric buses

作　　者：李斌 黄起彬 LI Bin;HUANG Qibin(Fujian Provincial Key Laboratory of Big Data Mining and Applications,Fujian University of Technology,Fuzhou 350118,China;School of Mechanical and Automotive Engineering,Fujian University of Technology,Fuzhou 350118,China;School of Transportation,Fujian University of Technology,Fuzhou 350118,China)

机构地区：[1]福建理工大学,福建省大数据挖掘与应用重点实验室,福州350118 [2]福建理工大学,机械与汽车工程学院,福州350118 [3]福建理工大学,交通运输学院,福州350118

出　　处：《交通运输工程与信息学报》2024年第1期79-94,共16页Journal of Transportation Engineering and Information

基　　金：教育部人文社会科学研究规划基金项目(19YJA630031)。

摘　　要：为减少公交运营成本、合理制定插入式充电模式下公交总站的电动公交车充电调度方案,本文基于帝国竞争算法提出了一种面向资源约束的公交车充电调度策略(RCO-CSS)。基于电动公交车运营的时空特点和充放电特性,应用多技能资源约束多项目调度问题(MSRC-MPSP)运筹规划思想对电动公交车充电问题进行抽象建模,以车队规模与充电桩数量为主要资源参数,以最小化充电成本和日均设备购置成本为目标,构建资源约束充电调度模型,进而设计一种二阶段演化帝国竞争算法(TSE-ICA)对模型进行求解,输出最佳的充电调度方案及匹配的行车运营计划。采用4个分别包含5、10、20和36条线路的公交运行实例对RCO-CSS进行了性能评估与有效性验证。在实例探讨中,首先运用Taguich法对资源参数进行了敏感性分析,发现资源越宽裕,模型输出的日充电费用越小,但车辆与充放电设备平摊至每日的购置成本越大;其次,将TSE-ICA与其他4种先进的元启发式算法进行实验数值对比,验证了所提算法的寻优性能;最后,通过与无序充电调度策略和常规有序充电调度策略进行比较,证明了RCO-CSS能够更好地降低用电成本、设备购置成本和电池充放电次数。基于MSRC-MPSP和TSE-ICA的RCO-CSS为公交运营商制定充电调度方案和行车运营计划提供了一种可行且敏捷高效的新思路。To reduce the operating cost of electric buses and formulate a reasonable charging scheduling scheme for electric buses under the mode of plug-in charging at bus terminals,a resource constraint-oriented charging scheduling strategy(RCO-CSS)is proposed,which combines operational modeling and a solution algorithm.Considering the spatiotemporal characteristics and charging and discharging characteristics of electric buses,the standard model of the multi-skill resource-constrained multi-project scheduling problem(MSRC-MPSP)operational programming philosophy is applied to implement the abstract modeling of the target problem for establishing a resource-constrained charging scheduling model(RCCSM).The RCCSM takes the fleet size and the number of charging piles as resource parameters and aims to minimize the charging cost and average daily equipment purchase cost.A two-stage evolutionary imperialist competitive algorithm(TSE-ICA)is designed to solve the RCCSM to output the optimal charging schedule and corresponding vehicle scheduling plan.Four instances containing 5,10,20,and 36 lines are utilized to evaluate the performance and check the validity of RCO-CSS.In the numerical experiments,first,the Taguchi method is used to perform a sensitivity analysis of the resource parameters,and it is discovered that more abundant resources correspond to a lower daily charging cost output by the RCCSM but a higher daily purchase cost of vehicles and charging equipment.Second,the experimental results are compared with the results of four advanced metaheuristic algorithms to verify the optimization performance of the TSE-ICA.Compared with the disordered charging scheduling strategy and the conventional ordered scheduling strategy,it is proved that the RCO-CSS can better reduce the cost of electricity consumption,purchase cost of equipment,and battery charging and discharging times.The proposed RCO-CSS combining the MSRC-MPSP and TSE-ICA can allow bus operators to formulate feasible,agile,and efficient charging scheduling schemes and

关 键 词：智能交通 充电调度策略 多技能资源约束多项目调度问题 电动公交车 帝国竞争算法 行车计划 Taguich法 

分 类 号：U492.22[交通运输工程—交通运输规划与管理]