交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (4): 50-59.DOI: 10.16097/j.cnki.1009-6744.2024.04.006

• 智能交通系统与信息技术 • 上一篇    下一篇

车网互动场景下电动网约车运营与充放电动态调度策略

牛振宁,安琨* ,马万经   

  1. 同济大学,道路与交通工程教育部重点实验室,上海201804
  • 收稿日期:2024-04-15 修回日期:2024-06-03 接受日期:2024-06-11 出版日期:2024-08-25 发布日期:2024-08-21
  • 作者简介:牛振宁(1997- ),男,山西朔州人,博士生。
  • 基金资助:
    国家自然科学基金国际(地区)合作与交流项目 (72361137005);国家自然科学基金青年科学基金 (72101186);国家杰出青年科学基金 (52325210)。

Electric Vehicle Ride-hailing Operation and Charging-discharging Dynamic Scheduling Strategy in Vehicle-to-grid Scenario

NIU Zhenning,AN Kun*,MAWanjing   

  1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
  • Received:2024-04-15 Revised:2024-06-03 Accepted:2024-06-11 Online:2024-08-25 Published:2024-08-21
  • Supported by:
    FundsforInternationalCooperationandExchangeoftheNational Natural Science Foundation of China (72361137005);Young Scientists Fund of the National Natural Science Foundation of China (72101186);National Science Fund for Distinguished Young Scholars of China (52325210)。

摘要: 利用电动网约车的集中性和灵活性,结合车网互动(Vehicle-to-grid,V2G)技术,使得车辆可以在电网负荷高峰期间为电网提供应急和需求响应服务。本文旨在评估电动网约车车队参与V2G系统的潜力和效益,并对网约车的订单分配、空车调度、充放电调度方案进行动态决策。首先建立时间—空间—能量三维网络刻画车辆调度问题,并通过滚动时域优化模型最大化网约车车队的期望收益,进一步设计可行弧筛选算法降低模型规模,求解车队动态调度决策。以上海市嘉定区为例进行案例分析,结果表明,所提出的电动网约车动态调度策略可以较好地满足出行订单需求,通过空车调度平衡未来的出行需求和供给。在电网需求应急响应时段,可调度10.3%的闲置车辆参与放电,参与车辆平均收益可达104.8元·h-1,有助于减少车辆的闲置率,提升车辆净收益,应对运输服务市场逐渐饱和的问题。

关键词: 城市交通, 电动网约车, 时空能网络, 充放电, 滚动优化

Abstract: The centralized nature and flexibility of electric vehicle (EV) ride-hailing fleets offer opportunities for vehicles to provide emergency and demand-response services to the grid during peak load periods, when combined with Vehicle-to-Grid (V2G) technology. This study investigates the flexibility of EV ride-hailing fleets participating in V2G systems and aims to make dynamic decisions on vehicle-trip assignment, empty vehicle relocation, and charging/ discharging schedules. First, a time-space-energy three-dimensional network is constructed to depict the vehicle scheduling problem. Then, the rolling horizon optimization model is used to maximize the expected benefits of the fleet. Additionally, the dynamic scheduling decisions of the fleet are obtained by defining feasible arcs. A case study is conducted in Jiading, Shanghai. The results indicate that the proposed strategy for EV ride-hailing fleets can effectively respond to travel requests, balance future travel demand and supply through empty vehicle relocation, and dispatch idle vehicles for discharging. During periods of grid demand response, 10.3% of idle vehicles can be dispatched for discharging, with an average revenue of 104.8 yuan per hour per vehicle. The proposed method helps reduce vehicle idle rates, increase vehicle revenue, and address the issue of the gradually saturated transportation service market.

Key words: urban traffic, electric vehicle ride-hailing, time-space-energy network, charging and discharging, rolling horizon

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