华南理工大学学报(自然科学版) ›› 2026, Vol. 54 ›› Issue (3): 104-113.doi: 10.12141/j.issn.1000-565X.250101

• 智慧交通系统 • 上一篇    下一篇

计及交通状态的高速公路服务区移动储能车辆调度优化策略

张丽娜 许宏科 代亮 王大伟   

  1. 长安大学 电子与控制工程学院,陕西 西安 710064

  • 出版日期:2026-03-25 发布日期:2025-10-11

Optimal Scheduling Strategy of Mobile Energy Storage Vehicles for Highway Service Areas Considering Traffic Status

ZHANG Lina XU Hongke DAI Liang WANG Dawei   

  1. College of Electronics and Control Engineering, Chang’an University, Xi’an 710064, Shaanxi, China

  • Online:2026-03-25 Published:2025-10-11

摘要:

针对高速公路配电网承载力不足,可再生能源发电和用能负荷的不匹配问题,利用移动储能车辆进行服务区间的能量互济,能够促进可再生能源消纳。为了提高移动储能的实时响应能力,提出一种在电动汽车换电服务平均损失率约束下,计及交通状态的移动储能车辆调度优化策略。该策略以服务区之间交通状态和换电站能量状态建立马尔可夫链模型,通过对移动储能平均运输时间成本和换电服务平均损失率进行分析,构建了移动储能车辆的调度优化问题并求解,获得最优调度策略及其参数。通过仿真验证了该调度策略的交通和能量状态双门限结构;与贪婪策略和Q-learning方法相比,移动储能平均运输时间成本分别降低了17.23%和8.89%。

关键词: 高速公路服务区, 移动储能, 交通状态, 马尔可夫链

Abstract:

In view of the insufficient carrying capacity of highway distribution grids and the mismatch between re-newable energy generation and consumption loads, the use of mobile energy storage vehicles for energy interexchange between service areas can promote the consumption of renewable energy. In order to improve the real-time responsiveness of mobile energy storage, an optimization strategy is proposed that considering the traffic status under the constraint of the average loss rate of electric vehicle battery swapping service. A Markov chain model is developed to characterize the traffic status between service areas and the energy states of battery swapping station. By analyzing the average transportation cost of mobile energy storage and the average loss rate of battery swapping service, an optimization problem for mobile energy storage vehicle dispatching is formulated and solved to obtain the optimal scheduling strategy and its parameters. The simulation results validate the proposed strategy has a dual-threshold structure with traffic status and energy state. Compared to the greedy strategy and Q-learning algorithm, the average transportation cost of mobile energy storage is reduced by 17.23% and 8.89%, respectively.

Key words: highway service area, mobile energy storage, traffic status, Markov chain