中国电机工程学报2017，Vol.37Issue(18)：5207-5219,13.DOI:10.13334/j.0258-8013.pcsee.161470
“车-路-网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响
A Spatial-temporal Charging Load Forecast and Impact Analysis Method for Distribution Network Using EVs-Traffic-Distribution Model
摘要
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.关键词
电动汽车负荷/道路交通网/起止点分析/时空分布模型/城市配电网Key words
EV charging load/transportation network/origin-destination analysis/spatial-temporal model/urban distribution network分类
信息技术与安全科学引用本文复制引用
邵尹池,穆云飞,余晓丹,董晓红,贾宏杰,吴建中,曾沅..“车-路-网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响[J].中国电机工程学报,2017,37(18):5207-5219,13.基金项目
国家863高技术基金项目(2015AA050403) （2015AA050403）
国家自然科学基金项目(51677124,51625702,51337005) （51677124,51625702,51337005）
天津市应用基础与前沿技术研究计划(15JCQNJC43500)The National High Technology Research and Development of China 863 Program (2015AA050403) （15JCQNJC43500）
Project Supported by National Natural Science Foundation of China (51677124,51625702,51337005) （51677124,51625702,51337005）
Tianjin Application Foundation and Advanced Research Technologies Program (15JCQNJC43500). （15JCQNJC43500）
