Spatial and Temporal Distribution Model of Electric Vehicle Load Considering Different Urban Functional Areas

doi:10.12204/j.issn.1000-7229.2021.06.007

Abstract

Abstract:

Study of the charging load characteristics of electric vehicles (EVs) is the basis for promoting the integration of EVs into the power grid. The charging load of electric vehicles has both temporal and spatial randomness. In this paper, a temporal and spatial distribution model of EVs, which considers the characteristics of different urban functional areas, is proposed on the basis of the improved gravity model. Firstly, the travel characteristics and the categories of EVs are determined, and urban areas are reasonably divided according to the categories of functional areas. Secondly, the travel matrices of urban areas at different time period, as well as the spatial distribution of EVs, are achieved according to the improved gravity model. Finally, combining with the charging modes and the state-of-charge (SOC) model of EVs, the temporal and spatial distribution model is established. Case studies show that there exists a huge discrepancy in the load distributions among different types of functional areas due to the attractiveness of each area. For the same type of functional area, areas closer to the city center generally pick up more load than their outlying counterparts. The accuracy of the proposed model is verified by analyzing the influence of different factors on the charging load distribution in various preset scenarios.

Key words: electric vehicle (EV), spatial and temporal distribution, urban functional area, gravity model

CLC Number:

TM73

FAN Lei, CHEN Liangliang, LUO Wenqian, DONG Xiaoxiao, YUAN Yue. Spatial and Temporal Distribution Model of Electric Vehicle Load Considering Different Urban Functional Areas[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(6): 67-75.

Figures/Tables 16

Fig.1 Modelling idea of EV travel distribution prediction

Fig.2 Flow chart of EV charging load prediction

Fig.3 The urban functional area division of city A

Table 1 Names of the urban functional area of each region in city A

Fig.4 The travel intention between different regions in city A

Table 2 Parameters of electric vehicles

Fig.5 Distance distribution of EVs integrated into power gird at each moment

Fig.6 Charging power of different regions of residential areas at each moment

Fig.7 Results comparison of charging power between traditional and improved gravity models

Fig.8 Charging power of public service area with or without the charging power of electric buses

Fig.9 Charging power of the city with or without the expansion of urban business district

Fig.10 Charging power of the city with or without the increase of working hours

Table A1 The distance between different regions in city A距离/km

Table A2 The travel intention between different regions in city A

Fig.A1 Attractiveness of each area

Fig.A2 Travel rate of each area

References 20

[1]	张明霞, 田立亭, 杨水丽, 等. 考虑电动汽车充电负荷空间分布的系统特性分析[J]. 电力系统保护与控制, 2014,42(21):86-92.
	ZHANG Mingxia, TIAN Liting, YANG Shuili, et al. Influence of electric vehicle charging load distribution on power grid[J]. Power System Protection and Control, 2014,42(21):86-92.
[2]	CHEN C C, WU Z G, ZHANG Y L. The charging characteristics of large-scale electric vehicle group considering characteristics of traffic network[J]. IEEE Access, 2020,8:32542-32550. doi: 10.1109/Access.6287639 URL
[3]	陈丽丹, 张尧, Antonio Figueiredo. 融合多源信息的电动汽车充电负荷预测及其对配电网的影响[J]. 电力自动化设备, 2018,38(12):1-10.
	CHEN Lidan, ZHANG Yao, FIGUEIREDO A. Charging load forecasting of electric vehicles based on multi-source information fusion and its influence on distribution network[J]. Electric Power Automation Equipment, 2018,38(12):1-10.
[4]	李含玉, 杜兆斌, 陈丽丹, 等. 基于出行模拟的电动汽车充电负荷预测模型及V2G评估[J]. 电力系统自动化, 2019,43(21):88-96.
	LI Hanyu, DU Zhaobin, CHEN Lidan, et al. Trip simulation based charging load forecasting model and vehicle-to-grid evaluation of electric vehicles[J]. Automation of Electric Power Systems, 2019,43(21):88-96.
[5]	BOZORGI A M, FARASAT M, MAHMOUD A. A time and energy efficient routing algorithm for electric vehicles based on historical driving data[J]. IEEE Transactions on Intelligent Vehicles, 2017,2(4):308-320. doi: 10.1109/TIV.2017.2771233 URL
[6]	IVERSEN E B, MØLLER J K, MORALES J M, et al. Inhomogeneous Markov models for describing driving patterns[J]. IEEE Transactions on Smart Grid, 2017,8(2):581-588.
[7]	陈丽丹, 聂涌泉, 钟庆. 基于出行链的电动汽车充电负荷预测模型[J]. 电工技术学报, 2015,30(4):216-225.
	CHEN Lidan, NIE Yongquan, ZHONG Qing. A model for electric vehicle charging load forecasting based on trip chains[J]. Transactions of China Electrotechnical Society, 2015,30(4):216-225.
[8]	XIANG Y, JIANG Z Z, GU C H, et al. Electric vehicle charging in smart grid: A spatial-temporal simulation method[J]. Energy, 2019,189:116221. doi: 10.1016/j.energy.2019.116221 URL
[9]	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.
	WEN Jianfeng, TAO Shun, XIAO Xiangning, et al. Analysis on charging demand of EV based on stochastic simulation of trip chain[J]. Power System Technology, 2015,39(6):1477-1484.
[10]	TANG D F, 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.
[11]	郭创新, 刘洞宇, 朱承治, 等. 电动汽车居民区充电负荷建模分析[J]. 电力自动化设备, 2020,40(1):1-9.
	GUO Chuangxin, LIU Dongyu, ZHU Chengzhi, et al. Modeling and analysis of electric vehicle charging load in residential area[J]. Electric Power Automation Equipment, 2020,40(1):1-9.
[12]	刘星平, 李世军, 于浩明, 等. 住宅小区内电动汽车有序充电优化模式[J]. 电工技术学报, 2015,30(20):238-245.
	LIU Xingping, LI Shijun, YU Haoming, et al. Coordinated charging optimization mode of electric vehicles in the residential area[J]. Transactions of China Electrotechnical Society, 2015,30(20):238-245.
[13]	MAIGHA M, CROW M L. Electric vehicle scheduling considering co-optimized customer and system objectives[J]. IEEE Transactions on Sustainable Energy, 2018,9(1):410-419. doi: 10.1109/TSTE.2017.2737146 URL
[14]	BAE S, KWASINSKI A. Spatial and temporal model of electric vehicle charging demand[J]. IEEE Transactions on Smart Grid, 2012,3(1):394-403. doi: 10.1109/TSG.2011.2159278 URL
[15]	U. S. Department of Transportation,Federal Highway Administration. 2009 national household travel survey[EB/OL]. (2018-01-02)[2020-05-10]. http://nhts.ornl.gov.
[16]	王浩林, 张勇军, 毛海鹏. 基于时刻充电概率的电动汽车充电负荷预测方法[J]. 电力自动化设备, 2019,39(3):207-213.
	WANG Haolin, ZHANG Yongjun, MAO Haipeng. Charging load forecasting method based on instantaneous charging probability for electric vehicles[J]. Electric Power Automation Equipment, 2019,39(3):207-213.
[17]	王锡凡, 邵成成, 王秀丽, 等. 电动汽车充电负荷与调度控制策略综述[J]. 中国电机工程学报, 2013,33(1):1-10.
	WANG Xifan, SHAO Chengcheng, WANG Xiuli, et al. Survey of electric vehicle charging load and dispatch control strategies[J]. Proceedings of the CSEE, 2013,33(1):1-10.
[18]	邢强, 杨祺铭, 范军太, 等. 基于数据驱动方式和行为决策的电动汽车快充需求预测模型[J]. 电网技术, 2020,44(7):2439-2453.
	XING Qiang, YANG Qiming, FAN Juntai, et al. Electric vehicle fast charging demand forecasting model based on data-driven approach and human behavior decision-making[J]. Power System Technology, 2020,44(7):2439-2453.
[19]	蒋毅舟. 规模化电动汽车用电需求的空间分布预测[D]. 北京: 华北电力大学, 2012.
	JIANG Yizhou. The spatial distribution forecast of charging demand for large-scale electric vehicles[D]. Beijing: North China Electric Power University, 2012.
[20]	李丹奇, 郑建勇, 史明明, 等. 电动汽车充电负荷时空分布预测[J]. 电力工程技术, 2019,38(1):75-83.
	LI Danqi, ZHENG Jianyong, SHI Mingming, et al. Prediction of time and space distribution of electric vehicle charging load[J]. Electric Power Engineering Technology, 2019,38(1):75-83.