Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Electric Vehicle Charging Simulation Framework Considering Traffic, User, and Power Grid
Author:
Affiliation:

1.School of Electrical Engineering, Southeast University, Nanjing 210096, China;2.Department of Electronic, Electrical and System Engineering, School of Engineering, University of Birmingham, Birmingham, UK

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 51936003).

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    Abstract:

    The traffic and user have significant impacts on the electric vehicle (EV) charging load but are not considered in the existing research. We propose a novel integrated simulation framework considering the traffic, the user, and power grid as well as the EV traveling, parking and charging based on cellular automaton (CA). The traffic is modeled by the traffic module of the proposed framework based on CA, while the power grid and user are modeled in the EV charging module. The traffic flow, users charging preference, users charging satisfaction, and the total supply capability (TSC) in the surveyed region are considered in the proposed framework. Two cases are carried out to show the interactions between the user and power grid. It is shown that the proposed framework can accurately simulate the interactions among traffic situation, users behavior and TSC which are significantly lacking in the existing research. The proposed framework is scalable in considering additional interrelated elements.

    表 5 Table 5
    表 2 Table 2
    表 6 Table 6
    表 1 Table 1
    图1 3种热电联产供电成本曲线Fig.1 Power supply cost curves of three cogeneration modes
    图2 3种热电联产供热成本曲线Fig.2 Heating supply cost curves of three cogeneration modes
    图3 某工程3种热电联产供电成本曲线Fig.3 Power supply cost curves of three cogeneration modes of a project
    图4 某工程3种热电联产供热成本曲线Fig.4 Heating supply cost curves of three cogeneration modes of a project
    图1 Simulation based on CA framework.Fig.1
    图2 Structure of proposed framework.Fig.2
    图3 Daily traffic flow and power load profile.Fig.3
    图4 Daily EV charging load of different zones. (a) Commerce zone. (b)Office zone. (c) Residence zone.Fig.4
    图5 Daily EV charging load of different user’s charging preferences. (a) The highest charging load fluctuation. (b) The smallest charging load fluctuation. (c) The highest charging load. (d) Monte Carlo method.Fig.5
    图8 Charging load of commerce zone by two simulation methods when traffic jam happens around 16:00 p.m..Fig.8
    图9 Charging load profile of different TSCs in commerce zone.Fig.9
    图10 Charging load profile of different traffic flows with 1000 kW TSC in commerce zone.Fig.10
    表 3 Table 3
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History
  • Received:April 01,2020
  • Online: May 19,2021