面向智能交通与微电网融合的共享电动车充放电及换电服务决策优化  

Optimizing Charging,Discharging and Battery Swapping Services for Shared Electric Vehicles in Integrated Transportation and Microgrids Networks

作　　者：庞惠元 镇璐[1] PANG Huiyuan;ZHEN Lu(School of Management,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学管理学院,上海200444

出　　处：《工程管理科技前沿》2025年第2期17-24,共8页Frontiers of Science and Technology of Engineering Management

基　　金：国家杰出青年科学基金资助项目(72025103);国家自然科学基金重大资助项目(72394360,72394362);国家自然科学基金国际合作项目(72361137001)。

摘　　要：城市化进程的加速正推动智能交通和微电网的发展。交通网和微电网的双网融合能够实现城市中出行任务和电力资源的统一调度,从而提升数智化服务水平,提高未来新型智慧城市的运行效率。本文研究了交通网和微电网双网融合背景下的共享电动车出行调度、充放电及换电服务决策问题,构建了一个基于时空网络的混合整数规划模型。为高效求解该问题,本文设计了一种基于模拟退火的自适应大邻城搜索算法,通过数值实验验证了所提出模型和算法的有效性。随后通过分析共享电动车的车辆分配模式和车辆电池的性能对双网融合运行成本的影响,为城市管理者提供若干管理启示,以提高智慧城市的数智服务管理能力。Ever-increasing urbanization is driving the development of integrated transportation and microgrids.The integration of transportation and microgrid networks facilitates the unified scheduling of travel services and electricity resources within cities,yet this aspect is largely overlooked in the literature on integrated systems and battery management analysis.This study investigates the optimization of charging,discharging and battery swapping services for shared electric vehicles in integrated transportation and microgrids networks.A mixed-integer programming model is developed based on a time-space network to characterize the dynamics of the shared electric vehicles-microgrid system.To solve this problem efficiently,we develop an adaptive large neighborhood search algorithm based on simulated annealing.Numerical experiments demonstrate that the proposed model and algorithm are effective and efficient in minimizing the total operational cost of the integrated systems.Moreover,this study validates the proposed model and algorithm using the case from Zhangjiang Town in the Pudong District of Shanghai.Subsequently,we explore the impact of vehicle allocation strategies and battery performance of shared electric vehicles on the operational costs of the integrated networks.Sensitivity analysis reveals that managers can optimize vehicle allocation based on the number of vehicles in operation:for fewer than 24 vehicles,allocation based on travel demand is recommended,while for more than 24 vehicles,allocation should focus on energy deficits.This research also highlights the need to balance vehicle quantity and battery performance investments to avoid diminishing marginal returns,achieving a cost-efficiency equilibrium.Future research could focus on developing more efficient algorithms for larger-scale problems and integrating machine learning techniques to more accurately predict travel and energy demand.In conclusion,the proposed model and insights demonstrate the potential in deepening the integration of transportation

关 键 词：智慧城市 共享电动车 交通网 微电网 充放电 换电 

分 类 号：C934[经济管理—管理学]