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电动汽车充放电负荷预测研究综述 被引量:95

Overview of Charging and Discharging Load Forcasting for Electric Vehicles
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摘要 随着电动汽车的持续推广以及充电设施的建设和逐步普及,电动汽车将得到越来越多的应用,而电动汽车充电负荷时空分布预测是分析电动汽车接入电网相关研究的基础。首先,分别从车辆性能、充电设施、用户行为习惯等微观层面,以及政策、环境、市场经济等宏观层面总结影响电动汽车充电负荷的关键因素;其次,对电动汽车充电负荷时间分布、空间分布预测方法的发展进行了系统阐述,分析了用户行为不确定性处理技术,同时概括了现有研究中一些新技术的应用情况,并针对电动汽车参与放电潜力评估的研究现状进行了介绍;最后,总结了现有研究方法中存在的不足和改进方向。 The adoption rate of electric vehicles(EVs)will considerably increase based on the continuous promotion of EVs and the gradual construction of charging infrastructures.The spatio-temporal distribution prediction of EVs charging load is the basis for the research related to the integration of EVs into the power grid.The key factors of EVs charging load are firstly investigated.Three aspects are considered at the micro level,which are the characteristics of EV,charging facility relevant factors and EV user behavior-related.At the macro level,three main components are addressed in terms of policy,environment and market.Secondly,the paper provides a summary of previous work regarding modeling of spatio-temporal EV charging,where the uncertainties of user behavior are discussed in detail.The application of some new technologies in the current research is presented.Moreover,the research status of EVs participation in discharging potential assessment is delivered.Finally,research gaps that need to be further explored are identified.
作者 陈丽丹 张尧 Antonio FIGUEIREDO CHEN Lidan;ZHANG Yao;Antonio FIGUEIREDO(School of Electrical Engineering,Guangzhou College of South China University of Technology,Guangzhou 510800,China;School of Electric Power,South China University of Technology,Guangzhou 510640,China;Department of Electronic Engineering,University of York,YO10 5DD,UK)
出处 《电力系统自动化》 EI CSCD 北大核心 2019年第10期177-197,共21页 Automation of Electric Power Systems
基金 国家自然科学基金资助项目(61603141) 广东省普通高校青年创新人才自然科学项目(2015KQNCX229) 国家留学基金资助项目(201708440511)~~
关键词 电动汽车 负荷预测 影响因素 时空分布 充放电 需求响应 electric vehicle(EV) load forecasting influence factors spatio-temporal distribution charging and discharging demand response
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