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电动汽车充电负荷概率分布的数值建模方法
作者:
作者单位:

1.智能电网教育部重点实验室(天津大学),天津市 300072;2.新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司),北京市 100192;3.国网安徽省电力有限公司经济技术研究院,安徽省合肥市 230000

摘要:

传统电动汽车充电负荷概率建模通常采用蒙特卡洛模拟方法,存在耦合参数多和计算耗时大等问题。为此,提出一种基于车辆集合的整体荷电状态(CSOC)概率分布特性的电动汽车充电负荷概率分布数值计算方法。首先,将车辆的多次行程拆解为独立的单次行程进行起止点(OD)分析,消除多行程间建模中的变量耦合误差。其次,建立考虑车辆出行特性的CSOC动态概率模型,确定各行程的起始荷电状态概率密度函数;结合大数定律得到充电负荷时空概率分布函数。最后,以12节点路网案例计算充电负荷时空概率分布。结果表明,较传统蒙特卡洛模拟方法,所提方法消除了耦合误差问题,并在保证计算精度的前提下大幅提高了计算效率。

关键词:

基金项目:

国家电网有限公司总部科技项目(含储能的快充电站与配电网的互动模式及集群调控关键技术研究与应用, 5419-201919212A)。

通信作者:

作者简介:

张宇轩(1995—),男,硕士研究生,主要研究方向:电动汽车充电负荷建模。E-mail:zhangyuxuan6@qq.com
郭力(1981—),男,博士,教授,博士生导师,主要研究方向:分布式发电接入配电网和交直流微电网的规划和运行控制。E-mail:liguo@tju.edu.cn
刘一欣(1989—),男,通信作者,博士,硕士生导师,主要研究方向:配电网/微电网规划和运行优化、电力市场。E-mail:liuyixin@tju.edu.cn


Numerical Modeling Method for Probability Distribution of Electric Vehicle Charging Load
Author:
Affiliation:

1.Key Laboratory of the Ministry of Education on Smart Power Grids, Tianjin University, Tianjin 300072, China;2.National Key Laboratory on Operation and Control of Renewable Energy and Energy Storage (China Electric Power Research Institute), Beijing 100192, China;3.Economic Research Institute of State Grid Anhui Electric Power Co., Ltd., Hefei 230000, China

Abstract:

Traditional probability modeling of electric vehicle charging load is usually based on the Monte Carlo simulation method, which faces the problems such as a large number of coupling parameters and long time computing. To this end, this paper proposes a numerical calculation method for the probability distribution of electric vehicle charging load based on probability distribution characteristics of the combined state of charge (CSOC) for vehicle collection. Firstly, multiple trips of the same vehicle are disassembled into independent single trips, and origin destination (OD) analysis is carried out on single trips to reduce the parameter coupling error in the modeling of multiple trips. Secondly, the CSOC dynamic probability model considering the probability characteristics of travelling of vehicles is established to determine the probability density function of the initial state of charge (SOC) for single trips. Then, combining the law of large numbers, the spatial-temporal probability distribution function of electric vehicle charging load is established. Finally, a 12-bus road network case is used to calculate the spatial-temporal probability distribution of the charging load. The results show that compared with the traditional Monte Carlo simulation method, the proposed method does not have the problem of coupling error, and greatly improves the calculation efficiency on the premise of ensuring the calculation accuracy.

Keywords:

Foundation:
This work is supported by State Grid Corporation of China (No. 5419-201919212A).
引用本文
[1]张宇轩,郭力,刘一欣,等.电动汽车充电负荷概率分布的数值建模方法[J].电力系统自动化,2021,45(18):61-70. DOI:10.7500/AEPS20201210011.
ZHANG Yuxuan, GUO Li, LIU Yixin, et al. Numerical Modeling Method for Probability Distribution of Electric Vehicle Charging Load[J]. Automation of Electric Power Systems, 2021, 45(18):61-70. DOI:10.7500/AEPS20201210011.
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  • 收稿日期:2020-12-10
  • 最后修改日期:2021-05-27
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  • 在线发布日期: 2021-09-16
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