基于时变交通特征的多类型充电设施与配电系统协同规划

程杉; 李昊; 马丙泰; 叶瀚泽; 梅启程; 王锐

doi:10.19783/j.cnki.pspc.260112

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基于时变交通特征的多类型充电设施与配电系统协同规划

电动汽车充换电与驱动系统专题 | 更新时间：2026-07-19

- 基于时变交通特征的多类型充电设施与配电系统协同规划
- Coordinated planning of multi-type electric vehicle charging facilities and distribution systems based on time-varying traffic characteristics
- 电力系统保护与控制 2026年54卷第14期
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- DOI：10.19783/j.cnki.pspc.260112
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- 纸质出版：2026
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程杉, 李昊, 马丙泰, 等. 基于时变交通特征的多类型充电设施与配电系统协同规划[J]. 电力系统保护与控制, 2026,54(14).

CHENG Shan, LI Hao, MA Bingtai, et al. Coordinated planning of multi-type electric vehicle charging facilities and distribution systems based on time-varying traffic characteristics[J]. Power System Protection and Control, 2026, 54(14).
程杉, 李昊, 马丙泰, 等. 基于时变交通特征的多类型充电设施与配电系统协同规划[J]. 电力系统保护与控制, 2026,54(14). DOI： 10.19783/j.cnki.pspc.260112.

CHENG Shan, LI Hao, MA Bingtai, et al. Coordinated planning of multi-type electric vehicle charging facilities and distribution systems based on time-varying traffic characteristics[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.260112.

摘要

针对传统静态路阻函数难以刻画城市交通流时变特性，以及用户多样化充电需求对充电设施部署和配电系统扩展影响考虑不足的问题，提出一种基于时变交通特征的多类型充电设施与配电系统协同规划模型。首先，融合路网拓扑与分时通行速度数据，构建时变动态路网模型，并基于兴趣点分布划分城市功能区。其次，采用蒙特卡洛方法模拟不同功能区电动汽车充电需求时空分布。然后，以综合社会成本最小为目标，构建计及慢充、快充与超充配置的协同优化模型。最后，以实际路网与配电网耦合系统为例验证了所提方法的有效性。结果表明，所提模型可反映交通拥堵状态及充电负荷时空分布特征，在满足用户多样化充电需求的同时，提高了充电设施规划与配电系统升级的经济性。

Abstract

To address the limitations of traditional static road impedance functions in characterizing the time-varying characteristics of urban traffic flow

as well as the insufficient consideration of diverse user charging demands on charging facility deployment and distribution system expansion

a coordinated planning model for multi-type charging facilities and distribution systems based on time-varying traffic characteristics is proposed. First

road network topology and time-segmented traffic speed data are integrated to construct a time-varying dynamic road network model

and urban functional zones are divided based on points of interest. Second

the Monte Carlo method is used to simulate the spatiotemporal distribution of electric vehicle charging demand across different functional zones. Then

a coordinated optimization model considering slow

fast

and ultra-fast charging facilities is constructed with the objective of minimizing the overall social cost. Finally

the effectiveness of the proposed method is verified using a coupled real-world road network and distribution system. The results show that the proposed model can effectively reflect traffic congestion conditions and the spatiotemporal distribution characteristics of charging demand. While satisfying diverse user charging demands

it also improves the economic efficiency of charging facility planning and distribution system upgrading.

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references

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WANG S, DONG Z, LUO F, et al. Stochastic collaborative planning of electric vehicle charging stations and power distribution system[J]. IEEE Transactions on Industrial Informatics, 2018, 14(1): 321-331.

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SHAO C, QIAN T, WANG Y, et al. Coordinated planning of extreme fast charging stations and power distribution networks considering on-site storage[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 493-504.

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XING Q, CHEN Z, ZHANG Z, et al. Urban electric vehicle fast-charging demand forecasting model based on data-driven approach and human decision-making behavior[J]. Energies, 2020, 13(6).

YANG Z, XIE K, YU J, et al. A general formulation of linear power flow models: basic theory and error analysis[J]. IEEE Transactions on Power Systems, 2019, 34(2): 1315-1324.

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