Trip Simulation Based Charging Load Forecasting Model and Vehicle-to-Grid Evaluation of Electric Vehicles

被引：0
作者：
Li H. [1 ]
Du Z. [1 ]
Chen L. [1 ]
Guan L. [1 ]
Zhou B. [2 ]
机构：
[1] School of Electric Power, South China University of Technology, Guangzhou
[2] Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 21期
基金：
中国国家自然科学基金;
关键词：
Electric vehicle (EV); Spatial-temporal characteristic; Traffic road network; Vehicle-to-grid (V2G);
D O I：
10.7500/AEPS20180521002
中图分类号：
学科分类号：
摘要：
A charging load forecasting model of electric vehicles (EVs) for trip simulation of users is proposed considering both traffic network and different trip chains. Firstly, a traffic road model is established that takes into account the network topology of traffic road, relationship and regional characteristics of road-impedance function. Then, different trip chain models with various complexities are constructed to simulate the trip characteristics of users. The Dijkstra algorithm is adopted to select the shortest time-consuming driving path. Moreover, Monte Carlo method is applied to simulate the spatial-temporal characteristics of charging load for household EVs which is attached to the combination of regional traffic road network and trip chains. The power and capacity affected by vehicle-to-grid (V2G) are calculated by fuzzy algorithm according to three characteristic factors such as spatial-temporal distribution of load for EVs, comprehensive state of charge, stopping time and electricity price based on spatial-temporal distribution of load for EVs. The effect of state of charge on response results is analyzed as well. Finally, taking a certain area as an example, the spatial-temporal distribution of charging demand for EVs is obtained by simulation, and the V2G response is evaluated. The results verify the effectiveness of the proposed model and method. © 2019 Automation of Electric Power Systems Press.
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收藏
页码：88 / 96
页数：8
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