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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摘要

鉴于我国当前城市路网车流呈现为电动汽车(EV)和燃油汽车(GV)两大主体混行,而路网车流时空分布会直接影响交通和配电系统的耦合关系,本文提出了一种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。考虑不同时段和路段和中EV车流充电响应的差异性,建立了EV车流时空差异化充电响应模型,并引入单位车流量充电负荷转换系数以计算充电响应后EV车流变化对配电网负荷分布造成的影响;然后充分考虑EV和GV混行特点,结合累计前景理论构建混合车流出行决策效用模型,并考虑差异化充电响应对混合车流的动态调控作用,建立了时空差异化充电响应半动态混合车流模型,以实现混合车流相互影响下的均衡配流;最后基于上述模型,综合考虑交通网通行时间成本和配电网运行成本构建了路-电耦合网络协同优化模型。算例应用结果验证了所提方法的有效性和优越性。

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关键词 ：路-电耦合网络, 混合车流, 半动态, 时空差异化充电响应, 协同优化

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

PACS:

TM73

基金资助:

国家自然科学基金项目(52267007,52167009)和江西省自然科学基金项目(20242BAB26070) 资助

通讯作者: 彭春华男,1973年生,博士,教授,博士生导师,研究方向为电力系统规划与运行、智慧能源系统优化调控。E-mail：chinapch@163.com

作者简介: 孙施翀男,2000年生,硕士研究生,研究方向为智能电网优化调度。E-mail：15380838985@163.com

引用本文:

彭春华, 孙施翀, 孙惠娟, 张新宇. 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[J]. 电工技术学报, 0, (): 258108-258108. Peng chunhua, Sun shichong, Sun huijuan, Zhang xinyu. Collaborative Optimization Strategy of Traffic Electrical Coupling Network Based on a Semi-dynamic Mixed Traffic Flow Model with Spatio-temporal Differentiated Charging Response. Transactions of China Electrotechnical Society, 0, (): 258108-258108.

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