Optimization of travel routes and economic operation strategies for integrated transportation and energy systems considering EV carbon emissions

doi:10.3969/j.issn.2097-0706.2025.02.004

Abstract

Abstract:

To address the unbalanced power flow in distribution networks of urban areas caused by the large-scale grid-connection of electric vehicles （EVs）， a method for the optimization of travel routes and economic operation strategies for integrated transportation and energy systems considering EV carbon emissions was proposed， along with corresponding solution methods. The urban areas and their functions were analyzed， and the Huff model was used to quantify the regional travel attractiveness， enabling precise zoning of cities and numerical simulation of EV charging loads. The flow fueling location model （FRLM） evaluation system was introduced to select suitable locations for EV charging stations. This helped organize the mapping of EV loads to the power flow grid， thereby forming a "transportation-energy" coupling network that took both EV charging service coverage and power supply uniformity into consideration. Based on the carbon emissions from EV travel， the user equilibrium theory was adaptively improved， complemented by a power flow uniformity evaluation function guided by grid stability optimization. Then，an EV travel route optimization system that guided the reasonable distribution of local load peaks was established， ensuring that the proposed solution aligned with the expectations for economic and low-carbon operation. Moreover， a simulation analysis was conducted on an IEEE 33 node distribution network and a main road network model of an urban area. The results verified the effectiveness and feasibility of the proposed model.

Key words: electrical vehicle, urban road network, integrated transportation and energy system, carbon emissions, power flow distribution uniformity, power grid operation stability, user satisfaction

CLC Number:

TK011：U49

YANG Jian, HAO Guojie, WU Xinyue, GUO Mingqiang, CHEN Chong, LEI Zhimin. Optimization of travel routes and economic operation strategies for integrated transportation and energy systems considering EV carbon emissions[J]. Integrated Intelligent Energy, 2025, 47(2): 41-49.

Figures/Tables 9

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References 22

[1]	郭剑锋, 张雪美, 曹琪, 等. 电动汽车助力我国能源安全与“碳达峰、碳中和” 协同推进[J]. 中国科学院院刊, 2024, 39(2): 397-407.
	GUO Jianfeng, ZHANG Xuemei, CAO Qi, et al. Electric vehicles contribute to China's energy security and carbon peaking and carbon neutrality[J]. Bulletin of Chinese Academy of Sciences, 2024, 39(2): 397-407.
[2]	ZHOU G Y, ZHU Z W, LUO S M. Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm[J]. Energy, 2022, 247: 123437.
[3]	ZHANG Y L, GUO Y, ZHANG N W, et al. Distributionally robust deviation flow refueling location for electric vehicle swapping stations[C]// 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020: 2834-2839.
[4]	李明扬, 窦梦园. 基于强化学习的含电动汽车虚拟电厂优化调度[J]. 综合智慧能源, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
	LI Mingyang, DOU Mengyuan. Optimal scheduling of virtual power plants integrating electric vehicles based on reinforcement learning[J]. Integrated Intelligent Energy, 2024, 46(6): 27-34. doi: 10.3969/j.issn.2097-0706.2024.06.004
[5]	TANG X Y, BI S Z, ZHANG Y J A. Distributed routing and charging scheduling optimization for internet of electric vehicles[J]. IEEE Internet of Things Journal, 2018, 6(1): 136-148.
[6]	董伟杰, 崔全胜, 郝澜欣, 等. 居民小区电动汽车有序充电策略研究[J]. 综合智慧能源, 2023, 45(1): 82-87. doi: 10.3969/j.issn.2097-0706.2023.01.010
	DONG Weijie, CUI Quansheng, HAO Lanxin, et al. Study on orderly charging strategy of electric vehicles in residential areas[J]. Integrated Intelligent Energy, 2023, 45(1): 82-87. doi: 10.3969/j.issn.2097-0706.2023.01.010
[7]	LIU J Y, LIN G, HUANG S H, et al. Collaborative EV routing and charging scheduling with power distribution and traffic networks interaction[J]. IEEE Transactions on Power Systems, 2022, 37(5): 3923-3936.
[8]	SINGH S, VAIDYA B, MOUFTAH H T. Smart EV charging strategies based on charging behavior[J]. Frontiers in Energy Research, 2022, 10: 773440.
[9]	冯雷, 蔡泽祥, 王奕, 等. 计及负荷储能特性的微网荷储协调联络线功率波动平抑策略[J]. 电力系统自动化, 2017, 41(17): 22-28.
	FENG Lei, CAI Zexiang, WANG Yi, et al. Strategy for Tie line power fluctuation suppressing of load-energy storage coordinated microgrid considering energy-storage characteristic of load[J]. Automation of Electric Power Systems, 2017, 41(17): 22-28.
[10]	李宏伟, 宋玉峰, 李帅兵, 等. 基于ArcGIS路网结构与交通拥挤度分析的电动汽车充电负荷预测方法[J/OL]. 电网技术(2023-12-15)[2024-10-23].https://doi.org/10.13335/j.1000-3673.pst.2023.1420.
[11]	田立亭, 史双龙, 贾卓. 电动汽车充电功率需求的统计学建模方法[J]. 电网技术, 2010, 34(11): 126-130.
	TIAN Liting, SHI Shuanglong, JIA Zhuo. A statistical model for charging power demand of electric vehicles[J]. Power System Technology, 2010, 34(11): 126-130.
[12]	CAO W Y, WU J Z, JENKINS N, et al. Benefits analysis of soft open points for electrical distribution network operation[J]. Applied Energy, 2016, 165: 36-47.
[13]	刘文颖, 文晶, 谢昶, 等. 基于源荷互动的含风电场电力系统多目标模糊优化调度方法[J]. 电力自动化设备, 2014, 34(10): 56-63, 68.
	LIU Wenying, WEN Jing, XIE Chang, et al. Multi-objective fuzzy optimal dispatch based on source-load interaction for power system with wind farm[J]. Electric Power Automation Equipment, 2014, 34(10): 56-63, 68.
[14]	XU R P, ZHANG C, ZHANG D M, et al. Adaptive robust load restoration via coordinating distribution network reconfiguration and mobile energy storage[J]. IEEE Transactions on Smart Grid, 2024, 15(6):5485-8499.
[15]	SINAGA K P, YANG M S. Unsupervised K-means clustering algorithm[J]. IEEE access, 2020, 8: 80716-80727.
[16]	刘东林, 王育飞, 张宇, 等. 基于Huff模型的电动汽车充电站选址定容方法[J]. 电力自动化设备, 2023, 43(11): 103-110.
	LIU Donglin, WANG Yufei, ZHANG Yu, et al. Siting and sizing method of electric vehicle charging stations based on Huff model[J]. Electric Power Automation Equipment, 2023, 43(11): 103-110.
[17]	韩林阳, 叶承晋, 朱超, 等. 高温天气下计及用户意愿的城市充电负荷空间引导策略[J]. 电力系统自动化, 2024, 48(10): 139-150.
	HAN Linyang, YE Chengjin, ZHU Chao, et al. Spatial guidance strategy for urban charging loads in high-temperature weather considering user willingness[J]. Automation of Electric Power Systems, 2024, 48(10): 139-150.
[18]	LV S, WEI Z N, SUN G Q, et al. Power and traffic nexus: From perspective of power transmission network and electrified highway network[J]. IEEE Transactions on Transportation Electrification, 2020, 7(2): 566-577.
[19]	李珂, 邵成成, 王雅楠, 等. 考虑电-气-交通耦合的城市综合能源系统规划[J]. 中国电机工程学报, 2023, 43(6): 2263-2273.
	LI Ke, SHAO Chengcheng, WANG Yanan, et al. Optimal planning of urban integrated energy systems considering electricity-gas-transportation interactions[J]. Proceedings of the CSEE, 2023, 43(6): 2263-2273.
[20]	金理军, 王关晴, 郑光华, 等. 基于全生命周期的电动汽车碳排放分析与评价[J]. 杭州电子科技大学学报(自然科学版), 2024, 44(3): 51-58.
	JIN Lijun, WANG Guanqing, ZHENG Guanghua, et al. Carbon emission analysis and evaluation of electric vehicles base on full life cycle[J]. Journal of Hangzhou Dianzi University (Natural Sciences), 2024, 44(3): 51-58.
[21]	符强, 蓝星辉, 任风华, 等. 一种双向目标RRT路径规划算法研究[J]. 计算机仿真, 2023, 40(1): 447-454, 461.
	FU Qiang, LAN Xinghui, REN Fenghua, et al. Research on route planning algorithm of bidirectional target RRT and dijkstra[J]. Computer Simulation, 2023, 40(1): 447-454, 461.
[22]	SUN B, HUANG Z, TAN X Q, et al. Optimal scheduling for electric vehicle charging with discrete charging levels in distribution grid[J]. IEEE Transactions on Smart Grid, 2016, 9(2): 624-634.

项目		平均值	节点17—18	节点2—16	节点7—18
优化前	昼间	14.31	16.10	10.18	12.97
优化前	夜间	8.72	9.14	8.16	9.47
优化后	昼间	12.02	12.50	11.73	11.36
优化后	夜间	8.11	8.78	7.92	8.83

指标方法	碳减排量/kg	总路径/km	车流量/ (辆·h^-1）	电压偏移（标幺值）
单目标EU	66.24	7.14	5.96	-0.22
单目标SPM	67.91	7.12	6.44	-0.37
多目标SPM	65.91	7.26	6.32	-0.33
本文方法	66.12	7.18	5.75	-0.18