<i>V2Sim</i>: An Open-Source Microscopic V2G Simulation Platform in Urban Power and Transportation Network

This paper proposes V2Sim, an open source Pythonbased simulation platform designed for advanced vehicle-togrid (V2G) analysis in coupled urban power and transportation networks. By integrating a microscopic urban transportation network (MUTN) with a power distribution network (PDN), V2Sim enables precise modeling of electric vehicle charging loads (EVCL) and dynamic V2G operations. The platform uniquely combines SUMO for MUTN simulations and an optimized DistFlow model for PDN analysis, with dedicated models for fast charging stations (FCS) and slow charging stations (SCS), capturing detailed charging dynamics often overlooked in existing simulation tools. V2Sim supports a range of customizable V2G strategies, advanced fault-sensing in charging stations, and parallel simulation through multi-processing to accelerate large-scale case studies. Case studies using a real-world MUTN from Nanjing, China, demonstrate V2Sim’s capability to analyze the spatial-temporal distribution of EVCL and evaluate V2G impacts, such as fault dissemination and pricing variations, in unprecedented detail. Unlike traditional equilibrium models, V2Sim captures single-vehicle behavior and charging interactions at the microscopic level, offering unparalleled accuracy in assessing the operational and planning needs of V2G-compatible systems. This platform serves as a comprehensive tool for researchers and urban planners aiming to optimize integrated power and transportation networks.

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Publication Details

Journal
IEEE Transactions on Smart Grid
Published
2025-04-21
DOI
https://doi.org/10.1109/tsg.2025.3560976
Citations
27
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
19.72

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article

V2Sim: An Open-Source Microscopic V2G Simulation Platform in Urban Power and Transportation Network

Mingyu Fang, Chengcheng Shao, Tao Qian, Qinran Hu et al.
27 citations
IEEE Transactions on Smart Grid
Electric Vehicles and Infrastructure
19.72
article

V2Sim: An Open-Source Microscopic V2G Simulation Platform in Urban Power and Transportation Network

Mingyu Fang, Chengcheng Shao, Tao Qian, Qinran Hu, Junyi Zheng
article en
27 citations

Abstract

This paper proposes V2Sim, an open source Pythonbased simulation platform designed for advanced vehicle-togrid (V2G) analysis in coupled urban power and transportation networks. By integrating a microscopic urban transportation network (MUTN) with a power distribution network (PDN), V2Sim enables precise modeling of electric vehicle charging loads (EVCL) and dynamic V2G operations. The platform uniquely combines SUMO for MUTN simulations and an optimized DistFlow model for PDN analysis, with dedicated models for fast charging stations (FCS) and slow charging stations (SCS), capturing detailed charging dynamics often overlooked in existing simulation tools. V2Sim supports a range of customizable V2G strategies, advanced fault-sensing in charging stations, and parallel simulation through multi-processing to accelerate large-scale case studies. Case studies using a real-world MUTN from Nanjing, China, demonstrate V2Sim’s capability to analyze the spatial-temporal distribution of EVCL and evaluate V2G impacts, such as fault dissemination and pricing variations, in unprecedented detail. Unlike traditional equilibrium models, V2Sim captures single-vehicle behavior and charging interactions at the microscopic level, offering unparalleled accuracy in assessing the operational and planning needs of V2G-compatible systems. This platform serves as a comprehensive tool for researchers and urban planners aiming to optimize integrated power and transportation networks.

IEEE Transactions on Smart GridVol. 16(4)
Zhejiang Energy Research Institute (CN), State Key Laboratory of Electrical Insulation and Power Equipment, Southeast University (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Sustainable cities and communities
Openalex Percentile: Top 0%
Electric Vehicles and Infrastructure
19.72
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