作者：李雪亮, 吴奎华, 杨波, 綦陆杰, 邓少治, 刘淑莉

作者单位：国网山东省电力公司经济技术研究院，山东 济南 250001

关键词：用能行为;电动汽车;蒙特卡洛;负荷预测

摘要：

电动汽车是区域终端用户的重要组成部分，具有强随机、电源和负荷双重性等特征。为此，文章提出一种考虑电动汽车出行随机性和用户充电行为不确定性的用能时空分布分析方法。基于空间转移概率和出行链双重约束来模拟用户的出行情况。考虑到电动汽车出行的随机性，用马尔可夫决策过程来描述其路径选择。针对电动汽车用户充电行为不确定性的问题，运用蒙特卡洛模拟方法获得电动汽车日常充电负荷的时空分布，在完成出行分析的基础上，对用户进行不确定性分析，实现区域用户实时用能行为分析。仿真结果表明，所提模型和方法能够较为精准地模拟电动汽车用户的出行和用能行为。

An analysis method of regional EVs energy-using characteristics under multi-constraint
LI Xueliang, WU Kuihua, YANG Bo, QI Lujie, DENG Shaozhi, LIU Shuli
Institute of Economics and Technology, State Grid Shandong Electric Power Company, Jinan 250001, China
Abstract: Electric vehicles(EVs) are an important part of regional end users. It has the characteristics of strong randomness, power and load duality. A spatiotemporal energy-using distribution prediction method was proposed, which considers the randomness of electric vehicle(EV) travel and the uncertainty of user charging behavior. Based on the double constraint of spatial transfer probability and travel chain, the travel situation of users was simulated. Considering the randomness of the trip of EVs, Markov decision process was used to describe their route selection. Aiming at the uncertainty of charging behavior of EVs, this paper analyzed each user one by one on the basis of travel analysis to simulate real-time energy-using behavior. Monte Carlo simulation method was used to obtain the spatial and temporal distribution of daily charging load of EVs. The simulation results show that the proposed model and method can accurately simulate the travel and energy-using behavior of EVs.
Keywords: energy-using behavior;electric vehicle;Monte Carol;load prediction
2020, 46(7):108-114  收稿日期: 2020-04-20;收到修改稿日期: 2020-06-01
基金项目: 国网山东省电力公司科技项目（520625190012）
作者简介: 李雪亮(1965-),男,山东聊城市人,正高级工程师,硕士,主要研究方向为电网规划及综合能源系统规划
参考文献
[1] SCHNEIDER K, GERKENSMEYER C, MEYER M K, et al. Impact assessment of plug-in hybrid vehicles on Pacific Northwest distribution systems[C]//2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh. 2008: 1-6.
[2] CLEMENT-NYNS K, HAESEN E, DRIESEN J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J]. IEEE Transactions on Power Systems, 2010, 25(1): 371-380
[3] WU T, YANG Q, BAO Z, et al. Coordinated energy dispatching in microgrid with wind power generation and plug-in electric vehicles[J]. IEEE Transactions on Smart Grid, 2013, 4(3): 1453-1463
[4] 胡泽春,宋永华,徐智威,等. 电动汽车接入电网的影响与利用[J]. 中国电机工程学报, 2012, 32(4): 1-10,25
[5] 许文超,牛涛,颜淋丽,等. 电动汽车充电站需求影响因素及预测方法[J]. 江苏电机工程, 2011, 30(3): 41-43
[6] 杨冰,王丽芳,廖承林. 大规模电动汽车充电需求及影响因素[J]. 电工技术学报, 2013, 28(2): 22-27
[7] ZHANG H, MOURA S J, HU Z. PEV fast-charging station siting and sizing on coupled transportation and power networks[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 2595-2605
[8] 张艳娟,苏小林,闫晓霞,等. 基于电动汽车时空特性的充电负荷预测[J]. 电力建设, 2015, 36(7): 75-82
[9] 徐浩,苗世洪,钱甜甜,等. 计及多日一充模式的规模化电动汽车充电负荷建模策略[J]. 电工技术学报, 2015, 30(9): 129-137
[10] 曹娜，牛恩荃，于群，等. 考虑城市多场景和用户充电意愿的私家电动汽车充电负荷预测[J/OL]. 电测与仪表: 1-10[2020-03-26]. http://kns.cnki.net/kcms/detail/23.1202.TH.20190906.1007.007.html.
[11] 张洪财,胡泽春,宋永华,等. 考虑时空分布的电动汽车充电负荷预测方法[J]. 电力系统自动化, 2014, 38(1): 13-20
[12] 罗卓伟,胡泽春,宋永华,等. 电动汽车充电负荷计算方法[J]. 电力系统自动化, 2011, 35(14): 36-42
[13] 田立亭,史双龙,贾卓. 电动汽车充电功率需求的统计学建模方法[J]. 电网技术, 2010, 34(11): 126-130
[14] 徐青山,蔡婷婷,刘瑜俊,等. 考虑驾驶人行为习惯及出行链的电动汽车充电站站址规划[J]. 电力系统自动化, 2016, 40(4): 59-65,77
[15] 温剑锋,陶顺,肖湘宁,等. 基于出行链随机模拟的电动汽车充电需求分析[J]. 电网技术, 2015, 39(6): 1477-1484
[16] 周翔,陈杰军,谢培元,等. 基于效用最大化原则的电动汽车充电站负荷特性分析方法[J]. 电测与仪表, 2018, 55(4): 1-8
[17] 王海玲,张美霞,杨秀. 基于气温影响的电动汽车充电需求预测[J]. 电测与仪表, 2017, 54(23): 123-128
[18] U.S. Department of Transportation Federal Highway Administration. 2009 National household travel survey [EB/ OL].[2020-04-18] http://nhts.Ornl.gov.
[19] SUN S, YANG Q, YAN W. A novel Markov-based temporal-SOC analysis for characterizing PEV charging demand[J]. IEEE Transactions on Industrial Informatics, 2018, 14(1): 156-166
[20] YANG Q, SUN S, DENG S, et al. Optimal sizing of PEV fast charging stations with Markovian demand characterization[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 4457-4466
[21] TANG D, WANG P. Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 627-636
[22] 麻秀范,李颖,王皓,等. 基于电动汽车出行随机模拟的充电桩需求研究[J]. 电工技术学报, 2017, 32(2): 190-202