考虑复杂出行链基于双链马尔科夫的电动汽车负荷建模方法
A Load Modeling Method for Electric Vehicles Based on Double Chain Markov Considering Complex Travel Chains
余洋 1陆文韬 1刘霡 1夏雨星 1陈东阳 1石金玮 2蔡新雷3
作者信息
- 1. 华北电力大学(保定)新能源电力系统国家重点实验室,河北保定 071003;华北电力大学(保定)河北省分布式储能与微网重点实验室,河北保定 071003
- 2. 华北电力大学(保定)新能源电力系统国家重点实验室,河北保定 071003;华北电力大学(保定)数理系,河北保定 071003
- 3. 广东电网有限责任公司电力调度控制中心,广东广州 510600
- 折叠
摘要
为解决电动汽车(electric vehicle,EV)负荷建模困难且精度低的问题,提出了考虑复杂出行链基于双链马尔科夫的电动汽车负荷建模方法.首先,对 5 种状态下EV的荷电状态进行划分;其次,对出行时间、停驻时长等与EV出行相关的内部因素以及道路拥堵、天气状况、空调启停等外部因素进行分析,并据此构建考虑内外部因素的EV复杂出行链模型;最后,在确定主链和辅链的状态转移概率矩阵基础上,推导双链马尔科夫的一步转移概率矩阵,建立不同状态下考虑复杂出行链的EV负荷模型.对所提EV负荷模型进行仿真验证,并与典型日EV负荷数据及其他建模方法进行对比,结果表明,所提负荷模型的精度更高,能够更加准确地描述EV充放电负荷.
Abstract
To address the difficulty and low accuracy existing in modeling the load of electric vehicles(EVs),a load modeling method for EV based on double chain Markov considering complex travel chains is proposed in this paper.Firstly,the state of charge of EVs is divided into five different states.Secondly,the internal factors related to EV travel,such as travel time and parking time,as well as external factors such as road congestion,weather conditions,and air conditioning on/off are analyzed,and an EV complex travel chain model that takes into account internal and external factors is constructed.Finally,on the basis of determining the state transition probability matrix of the main and auxiliary chains,the one-step transition probability matrix of the dual chain Markov is derived,and the EV load model considering complex travel chains in different states is established.The proposed EV load model is simulated and validated,and compared with typical daily EV load data and other modeling methods.The results show that the load model proposed in the paper can more accurately describe the charging and discharging load of the EV.
关键词
电动汽车/复杂出行链/双链马尔科夫/荷电状态划分/转移概率Key words
electric vehicle/complex travel chain/double chain Markov/state of charge division/transition probability引用本文复制引用
基金项目
中国南方电网有限责任公司科技项目(036000KK52190005)
中国南方电网有限责任公司科技项目(GDKJXM20198110)
出版年
2024
NETL
NSTL
维普
万方数据