摘要
大规模电动汽车聚合可调度容量是含电动汽车的虚拟电厂参与多层级多场景电力平衡辅助服务的重要技术指标之一。然而,现有电动汽车聚合可调度容量模型难以适应大规模电动汽车与省级电力调度中心互动场景。为此,文中从面向电力系统调峰、调频和调压多级多场景调控新视角,提出了数据驱动和机器学习相结合的双层聚类电动汽车聚合可调度容量建模方法。该方法通过构建电动汽车-充电桩广义储能单元的个体可调度容量模型,结合基于密度空间的聚类算法和改进的自组织映射深度聚类算法,有效地融合了电动汽车电量的时间分布和充电桩的空间分布特性,构建了面向调峰、调频和调压多场景调控的聚合可调度容量模型。采用了某省实际充电记录数据对提出的方法进行了验证,获得了“早间型”“中午型”“晚间型”等多种充电画像类型,实现了时空分布各异的电动汽车广义储能系统的自主聚合和省市级规模电动汽车参与电网不同辅助服务潜能的评估,并为聚合可调度容量的预测奠定了数据基础。
The aggregation schedulable capacity(ASC)of large-scale electric vehicles(EVs)is one of the important technical indicators for virtual power plant containing EVs to participate in multi-level and multi-scenario power balance auxiliary services.However,the existing ASC model of EVs is difficult to adapt to the interactive scenario between large-scale EVs and provincial power dispatching center.Therefore,from the new perspective of multi-level and multi-scenario regulation for peak-shaving,frequency regutalion,and voltage regulation of power systems,this paper proposes a dual-layer clustering modeling method for ASC of EVs based on data-driven and machine learning.By constructing the individual schedulable capacity model of generalized EV-charging pile energy storage unit,this method combines the density space-based clustering algorithm and the improved selforganizing map deep clustering algorithm,which effectively integrates the time distribution of electric quantity of EVs and the spatial distribution characteristics of charging piles,and constructs the ASC model for multi-scenario regulation of peak shaving,frequency regulation and voltage regulation.The proposed method is verified by actual charging records in a province of China,and various charging profiles such as“morning type”,“noon type”and“evening type”are obtained.The self-aggregation of generalized energy storage system with different spatial and temporal distributions of EVs is realized,and the potential evaluation of provincial-level EVs participating in different auxiliary services of power grid is realized.The data foundation is laid for the prediction of ASC.
作者
王杨洋
茆美琴
杨铖
周堃
杜燕
Nikos D.HATZIARGYRIOU
WANG Yangyang;MAO Meiqin;YANG Cheng;ZHOU Kun;DU Yan;Nikos D.HATZIARGYRIOU(Research Center for Photovoltaic Systems Engineering of Ministry of Education,Hefei University of Technology,Hefei 230009,China;State Grid Anhui Electric Power Co.,Ltd.,Hefei 230000,China;State Grid Anhui Electric Vehicle Service Co.,Ltd.,Hefei 230000,China;National Technical University of Athens,Athens 15780,Greece)
出处
《电力系统自动化》
EI
CSCD
北大核心
2024年第7期103-115,共13页
Automation of Electric Power Systems
基金
安徽省自然科学基金资助项目(2108085UD02)
国家自然科学基金资助项目(51577047)
高等学校学科创新引智计划(“111”计划)资助项目(BP0719039)。
关键词
电动汽车
辅助服务
数据驱动
广义储能系统
电动汽车与电网互动
可调度容量
聚合模型
双层聚类
electric vehicle
auxiliary service
data-driven
generalized energy storage system
vehicle to grid(V2G)
schedulable capacity
aggregation model
two-level clustering
作者简介
王杨洋(1993-),男,博士研究生,主要研究方向:电动汽车参与电网调度。E-mail:wangyywork@126.com;通信作者:茆美琴(1961-),女,博士,教授,博士生导师,主要研究方向:微电网能量管理系统、电动汽车参与电网调度。E-mail:mmqmail@163.com;杨铖(1983-),男,博士,高级工程师,主要研究方向:电网运行优化。E-mail:147858084@qq.com。
