基于时空差异化充电响应半动态混合车流模型的路-电耦合网络协同优化策略

doi:10.19595/j.cnki.1000-6753.tces.250603

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Abstract

With the rapidly increasing penetration of electric vehicles (EVs) in China’s transportation system, urban road networks are increasingly characterized by a mixed traffic pattern consisting of both electric and gasoline vehicles (GVs). The dynamic interaction between these heterogeneous traffic flows significantly affects the coupling between transportation and power distribution networks. However, most existing studies focus solely on EV-based modeling or static traffic assumptions, overlooking the influence of GV flows and the spatiotemporal variability of EV charging demand. To address these limitations, this study proposes a collaborative optimization strategy for traffic-electrical coupling networks based on a semi-dynamic mixed traffic flow model incorporating spatio-temporal differentiated charging responses.
Firstly, a semi-dynamic mixed traffic flow model is established to capture the temporal evolution of traffic distribution across multiple time intervals. This model retains the computational simplicity of static models while integrating residual flow transfer to reflect dynamic traffic states. The proposed model accounts for the heterogeneity in travel behaviors and charging requirements of EV and GV users. Cumulative prospect theory (CPT) is applied to construct travel utility functions under bounded rationality, capturing individual preferences regarding time cost, congestion levels, and EV battery state-of-charge (SOC).Secondly, a spatio-temporal differentiated EV charging response model is developed. This model introduces the price elasticity coefficient of charging demand to quantify EV users' responsiveness to dynamic electricity prices across different regions and times. The EV traffic flow response is further mapped to the distribution network using a charging load conversion coefficient, which translates charging-related vehicle flow changes into electrical load variations at distribution nodes.To guide EV route and charging behavior, a differentiated pricing mechanism is embedded in the model. EV users are assumed to select routes that include at least one charging station while minimizing generalized cost, incorporating travel time, charging price, and queuing delay. The resultant variation in charging decisions reshapes the spatial and temporal distribution of EV traffic and affects load profiles in the distribution grid.
Based on the developed traffic and charging models, a collaborative optimization model is formulated to minimize the total cost of the traffic-power coupled system. The objective includes both traffic-related travel time costs and power system operational costs, such as generation costs, peak-valley penalties, and electricity purchasing costs. The constraints encompass power balance, generator output limits, road capacity limits, and charging station load capacities.To solve the nonlinear optimization problem, the model is transformed into a variational inequality formulation, and an improved method of successive weighted averages(MSWA) is adopted for equilibrium flow assignment. Additionally, a Cross-Entropy radar scanning differential evolution algorithm is used to solve the system-level optimization problem.Simulation studies are conducted on a case system combining a 22-node traffic network and an IEEE 33-node distribution network. Results demonstrate that the proposed strategy significantly improves the spatial-temporal distribution of traffic flows, alleviates road congestion, and reduces EV charging concentration at specific locations. Charging demand is effectively shifted from peak to off-peak periods, enhancing the load balancing of the power grid. Compared with static models, the proposed semi-dynamic approach yields a 5.33% reduction in total traffic cost.
In conclusion, this study presents an integrated optimization strategy that combines behavioral modeling, differentiated pricing, and hybrid traffic flow simulation to coordinate the operation of coupled transportation and electrical systems. The framework provides new insights for guiding EV users’ behavior and enhancing system-wide efficiency. Future research will explore stochastic extensions of the model to address uncertainties in EV travel patterns, renewable energy generation, and user participation in demand response programs.

Key words： Traffic electrical coupling network mixed traffic flow semi-dynamic traffic differentiated charging response cooperative optimization

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	Peng chunhua
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Peng chunhua,Sun shichong,Sun huijuan等. Collaborative Optimization Strategy of Traffic Electrical Coupling Network Based on a Semi-dynamic Mixed Traffic Flow Model with Spatio-temporal Differentiated Charging Response[J]. Transactions of China Electrotechnical Society, 0, (): 258108-258108.

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[1] 王晓姬, 王道涵, 王柄东, 等. 电动汽车驱动/充电一体化系统及其控制策略综述[J]. 电工技术学报, 2023, 38(22): 5940-5958.
Wang Xiaoji, Wang Daohan, Wang Bingdong, et al.A review of drive-charging integrated systems and control strategies for electric vehicles[J]. Transactions of China Electrotechnical Society, 2023, 38(22): 5940-5958.
[2] 张睿骐, 阳辉, 王子睿, 等. 面向车-库-网多层级协调控制系统的电动汽车多时段可调度域的构建和分析[J/OL]. 电工技术学报, 2025: 1-21. (2025-03-06). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.241173.
Zhang Ruiqi, Yang Hui, Wang Zirui, et al. Establishment and analysis of multi-stage dispatchable region in vehicles-garage-grid multi-level coordinated control system[J/OL]. Transactions of China Electrotechnical Society, 2025: 1-21. (2025-03-06). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.241173.
[3] 林铭蓉, 胡志坚, 高明鑫, 等. 考虑需求响应和电动汽车负荷路-电耦合特性的配电网可靠性评估[J]. 电力建设, 2021, 42(6): 86-95.
Lin Mingrong, Hu Zhijian, Gao Mingxin, et al.Reliability evaluation of distribution network considering demand response and road-electricity coupling characteristics of electric vehicle load[J]. Electric Power Construction, 2021, 42(6): 86-95.
[4] 傅质馨, 朱韦翰, 朱俊澎, 等. 动态路-电耦合网络下电动出租车快速充电引导及其定价策略[J]. 电力自动化设备, 2022, 42(4): 9-17.
Fu Zhixin, Zhu Weihan, Zhu Junpeng, et al.Fast charging guidance and pricing strategy for electric taxis based on dynamic traffic-grid coupling network[J]. Electric Power Automation Equipment, 2022, 42(4): 9-17.
[5] 彭春华, 姜治, 孙惠娟, 等. 路-电耦合配电网调度与充电策略协同鲁棒优化[J]. 电网技术, 2023, 47(7): 2762-2775.
Peng Chunhua, Jiang Zhi, Sun Huijuan, et al.Coordinated robust optimization of distribution network scheduling and charging strategy with traffic-grid coupling[J]. Power System Technology, 2023, 47(7): 2762-2775.
[6] Su Su, Li Yujing, Yamashita K, et al.Electric vehicle charging guidance strategy considering “traffic network-charging station-driver” modeling: a multiagent deep reinforcement learning-based approach[J]. IEEE Transactions on Transportation Electrification, 2024, 10(3): 4653-4666.
[7] 曹昉, 胡佳彤, 罗进奔, 等. 基于路网动态模型下EV路径模拟的快速充电站容量配置[J]. 电力自动化设备, 2022, 42(10): 107-115.
Cao Fang, Hu Jiatong, Luo Jinben, et al.Capacity configuration of fast charging stations based on EV path simulation under dynamic model of transportation network[J]. Electric Power Automation Equipment, 2022, 42(10): 107-115.
[8] 方晓涛, 严正, 王晗, 等. 考虑“路-车-源-荷” 多重不确定性的交通网与配电网概率联合流分析[J]. 电力系统自动化, 2022, 46(12): 76-87.
Fang Xiaotao, Yan Zheng, Wang Han, et al.Analysis on probabilistic joint flow for transportation network and distribution network considering multiple uncertainties of road-vehicle-source-load[J]. Automation of Electric Power Systems, 2022, 46(12): 76-87.
[9] 朱峻良, 武志刚, 刘嘉宁. 基于半动态交通均衡的电动汽车充电负荷概率分布建模[J]. 电网技术, 2024, 48(2): 640-654.
Zhu Junliang, Wu Zhigang, Liu Jianing.Electric vehicle charging load probability distribution modeling based on semi-dynamic traffic equilibrium[J]. Power System Technology, 2024, 48(2): 640-654.
[10] Lü Si, Wei Zhinong, Chen Sheng, et al.Integrated demand response for congestion alleviation in coupled power and transportation networks[J]. Applied Energy, 2021, 283: 116206.
[11] Lv Si, Wei Zhinong, Sun Guoqiang, et al.Power and traffic nexus: from perspective of power transmission network and electrified highway network[J]. IEEE Transactions on Transportation Electrification, 2021, 7(2): 566-577.
[12] 潘超, 汤中卫, 廖海君, 等. 基于非对称一致性学习的多类型电动汽车协同参与需求响应方法[J]. 电工技术学报, 2025, 40(7): 2178-2190.
Pan Chao, Tang Zhongwei, Liao Haijun, et al.Asymmetric consensus learning-based multi-type electric vehicle collaborative participation demand response method[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2178-2190.
[13] 李晓涵, 曹伟. 弹性充电需求下电动汽车调频激励机制及控制策略[J]. 中国电力, 2025, 58(4): 148-158.
Li Xiaohan, Cao Wei.Frequency regulation incentive mechanism and control strategy for electric vehicles under elastic charging demand[J]. Electric Power, 2025, 58(4): 148-158.
[14] 葛少云, 朱林伟, 刘洪, 等. 基于动态交通仿真的高速公路电动汽车充电站规划[J]. 电工技术学报, 2018, 33(13): 2991-3001.
Ge Shaoyun, Zhu Linwei, Liu Hong, et al.Optimal deployment of electric vehicle charging stations on the highway based on dynamic traffic simulation[J]. Transactions of China Electrotechnical Society, 2018, 33(13): 2991-3001.
[15] 彭春华, 张金克, 陈露, 等. 计及差异化需求响应的微电网源荷储协调优化调度[J]. 电力自动化设备, 2020, 40(3): 1-7.
Peng Chunhua, Zhang Jinke, Chen Lu, et al.Source-load-storage coordinated optimal scheduling of microgrid considering differential demand response[J]. Electric Power Automation Equipment, 2020, 40(3): 1-7.
[16] Yang H M, Zhang J, Qiu J, et al.A practical pricing ap proach to smart grid demand response based on load classifica tion[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 179-190.
[17] 田丽君. 考虑个体微观特征的通勤行为建模与仿真[M]. 北京: 科学出版社, 2018.
[18] Zhang Manyuan, Cheng Lin.The method of successive averages and method of successive weighted averages for logit-based stochastic user equilibrium assignment[C]//COTA International Conference of Transportation Professionals 2017, Shanghai, China, 2018: 2114-2121.
[19] 彭春华, 熊志盛, 张艺, 等. 基于多场景置信间隙决策的风光储联合鲁棒规划[J]. 电力系统自动化, 2022, 46(16): 178-187.
Peng Chunhua, Xiong Zhisheng, Zhang Yi, et al.Joint robust planning of wind-photovoltaic-energy storage system based on multi-scenario confidence gap decision[J]. Automation of Electric Power Systems, 2022, 46(16): 178-187.
[20] He Xiaozheng, Wu Xinkai.Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system[J]. Transportation Research Part D: Transport and Environment, 2018, 63: 907-922.