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A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model

Shao, Yinchi, Mu, Yunfei, Yu, Xiaodan, Dong, Xiaohong, Jia, Hongjie, Wu, Jianzhong and Zeng, Yuan 2017. A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model. Proceedings of the CSEE: Smart Grid 37 (18) , pp. 5207-5219. 10.13334/j.0258-8013.pcsee.161470

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Abstract

A method for the forecast of charging load of electric vehicles (EVs) under “EVs-Traffic-Distribution” (ETD) system was developed to precisely manifest the spatial-temporal characteristics of large scale EV charging load in urban area and to evaluate the impact of the load on urban distribution network. An EV model with charging characteristics, a traffic network model with urban road topology and a classic velocity-capacity model were introduced to provide the spatial-temporal driving routes and velocity. With the above information, origin-destination (OD) analysis was used to simulate the mobility of each EV. And, Monte Carlo simulation was conducted to estimate the EVs’ spatial-temporal charging load characteristics over a day. By allocating the charging load of each EV to the nearest node in the distribution network, sequential power flow was conducted to evaluate the impact of charging load on the distribution network. A 29-node urban area combined with the geographic information and a 33-node distribution system was selected to validate the proposed method. Simulation results demonstrate its effectiveness.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Date of Acceptance: 16 September 2016
Last Modified: 21 May 2019 11:00
URI: http://orca.cf.ac.uk/id/eprint/121065

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