Evaluation on the convergence potential of electric vehicles considering their subjective and objective responsiveness

doi:10.3969/j.issn.2097-0706.2023.09.001

Abstract

Abstract:

Planning and operation of distribution networks become increasingly challenging with the large-scale grid connection of electric vehicles （EVs）. Studying the orderly control and unified scheduling of EV charging and discharging can facilitate peak load regulation assisted by the load of substantial EVs. In response to the distribution network overload caused by the extensive grid-connection of EVs， a potential evaluation method for electric vehicle aggregation considering subjective and objective response capabilities of the grid side and the user side is proposed. Then， EVs are classified by the k-means clustering algorithm and contour coefficient method， which improves the efficiency of the model solving when massive EVs are connected to the grid under the premise of sufficient operational fluidity of EVs. Finally， the weight set is obtained by the comprehensive weighting method combining the analytic hierarchy process and entropy weight method， and the aggregation potential evaluation result of electric vehicle demand response is obtained by the rank-sum ratio（RSR） comprehensive evaluation method. The simulation results validate the effectiveness and superiority of the proposed evaluation method， providing a new approach for the unified scheduling of EVs， and having engineering value for ensuring the safe and stable operation of the power grid.

Key words: electric vehicle, power grid, peak load shaving, demand side response, quantitative evaluation, clustering algorithm, analytic hierarchy process, entropy weight method, RSR comprehensive evaluation method

CLC Number:

TK019：TM732

LIANG Yan, GUO Li, ZHANG Dan, LIU Zhiqi, HU Yubin, ZHOU Xia, WEI Cong, SHAN Yu. Evaluation on the convergence potential of electric vehicles considering their subjective and objective responsiveness[J]. Integrated Intelligent Energy, 2023, 45(9): 1-10.

Figures/Tables 14

Fig.1

Fig.2

Table 1

Fig.3

Table 2

Paraphrasing for the scales of the nine-scale scoring method

标度	标度意义
1	$a i$ 与 $a j$ 同等重要
3	$a i$ 较 $a j$ 稍微重要
5	$a i$ 较 $a j$ 相当重要
7	$a i$ 较 $a j$ 强烈重要
9	$a i$ 较 $a j$ 极端重要
2，4，6，8	上述相邻判断的中间值
$a i j = 1 / a j i$	倒数

Table 2

Fig.4

Fig.5

Fig.6

Fig.7

Table 3

Calculated evaluation indicators of each group

指标	群体1	群体2	群体3
$R N, i, t$ /%	0.776	0.882	0.806
$R - i, t$ （标幺值）	0.08	0.05	0.06
$T 1$ /%	0.233	0.167	0.800
$T 2$ /%	0.967	0.857	0.733
$P r e (i, t)$ /kW	30	21	35
$η (p)$ /kW	15	32	27

Table 3

Table 4

Standardized evaluation indicators of each group

指标层	群体1	群体2	群体3
$R N, i, t$	0	1.000	0.283
$R - i, t$	1.000	0	0.330
$T 1$	0.101	0	1.000
$T 2$	1.000	0.618	0
$P r e (i, t)$	0.643	0	1.000
$η (p)$	0	1.000	0.710

Table 4

Table 5

Scores made by nine-scale scoring method from experts

评价指标	$R N, i, t$	$R - i, t$	$T 1$	$T 2$	$P r e (i, t)$	$η (p)$
$R N, i, t$	1	3	5	6	3	1
$R - i, t$	1/3	1	3	3	1	1/3
$T 1$	1/5	1/3	1	2	1/2	3
$T 2$	1/6	1/3	1/2	1	1/3	1
$P r e (i, t)$	1/3	1	2	3	1	2
$η (p)$	1	3	1/3	1	1/2	1

Table 5

Table 6

Fig.8

References 23

[1]	魏震波, 张海涛, 魏平桉, 等. 考虑动态激励型需求响应的微电网两阶段优化调度[J]. 电力系统保护与控制, 2021, 49(19):1-10.
	WEI Zhenbo, ZHANG Haitao, WEI Ping'an, et al. Two stage optimal dispatching of micro grid considering dynamic excitation demand response[J]. Power System Protection and Control, 2021, 49(19): 1-10.
[2]	李昊, 季振亚, 刘晓峰, 等. 基于综合可调度潜力评估的电动汽车优先调度策略[J]. 综合智慧能源, 2022, 44(11):1-11. doi: 10.3969/j.issn.2097-0706.2022.11.001
	LI Hao, JI Zhenya, LIU Xiaofeng, et al. Priority scheduling strategy for electric vehicles based on comprehensive schedulable potential evaluation[J]. Integrated Intelligent Energy, 2022, 44 (11):1-11. doi: 10.3969/j.issn.2097-0706.2022.11.001
[3]	胡泽春, 邵成成, 何方, 等. 电网与交通网耦合的设施规划与运行优化研究综述及展望[J]. 电力系统自动化, 2022, 46(12):3-19.
	HU Zechun, SHAO Chengcheng, HE Fang, et al. Overview and prospects of facility planning and operation optimization for the coupling of power grid and transportation network[J]. Automation of Electric Power Systems, 2022, 46(12):3-19.
[4]	南琦琦, 穆云飞, 董晓红, 等. 电动汽车快速充电网综合评估指标体系与方法[J]. 电力系统自动化, 2020, 44(1):83-91.
	NAN Qiqi, MU Yunfei, DONG Xiaohong, et al. Comprehensive evaluation index system and method for electric vehicle fast charging network[J]. Automation of Electric Power Systems, 2020, 44 (1): 83-91.
[5]	杜习超, 刘永民, 徐则诚, 等. 电动汽车随机负荷建模及对配电网节点电压分布的影响[J]. 电力自动化设备, 2018, 38(6):124-130.
	DU Xichao, LIU Yongmin, XU Zecheng, et al. Modeling of random load of electric vehicle and its influence on node voltage distribution of distribution network[J]. Electric Power Automation Equipment, 2018, 38(6):124-130.
[6]	杨晓东, 任帅杰, 张有兵, 等. 电动汽车可调度能力模型与日内优先调度策略[J]. 电力系统自动化, 2017, 41(2):84-93.
	YANG Xiaodong, REN Shuaijie, ZHANG Youbing, et al. Electric vehicle dispatchability model and daily priority dispatching strategy[J]. Automation of Electric Power Systems, 2017, 41 (2): 84-93.
[7]	徐智威, 胡泽春, 宋永华, 等. 基于动态分时电价的电动汽车充电站有序充电策略[J]. 中国电机工程学报, 2014, 34(22):3638-3646.
	XU Zhiwei, HU Zechun, SONG Yonghua, et al. Ordered charging strategy for electric vehicle charging stations based on dynamic time of use tariff[J]. Proceedings of the CSEE, 2014, 34 (22): 3638-3646.
[8]	杨景旭, 李钦豪, 张勇军, 等. 考虑电网需求匹配度的多EV聚合商需求响应削峰优化建模[J]. 电力自动化设备, 2021, 41(8):125-134.
	YANG Jingxu, LI Qinhao, ZHANG Yongjun, et al. Optimal modeling of demand response peak shaving for multi EV aggregators considering grid demand matching[J]. Electric Power Automation Equipment, 2021, 41 (8): 125-134.
[9]	张帅, 刘一欣, 张宇轩, 等. 电动汽车接入对配电网运行特性影响的综合评估方法[J]. 浙江电力, 2022, 41(3):1-10.
	ZHANG Shuai, LIU Yixin, ZHANG Yuxuan, et al. Comprehensive evaluation method for the impact of electric vehicle integration on the operational characteristics of distribution networks[J]. Zhejiang Electric Power, 2022, 41 (3): 1-10.
[10]	陈卫, 杨波, 张兆云, 等. 计及电动汽车充电站接入的配电网承载能力评估与优化[J]. 电工技术学报, 2014, 29(8):27-35,45.
	CHEN Wei, YANG Bo, ZHANG Zhaoyun, et al. Evaluation and optimization of distribution network carrying capacity considering the connection of electric vehicle charging stations[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 27-35,45.
[11]	任鹏, 李翀, 陶鹏, 等. 基于加权熵TOPSIS法的电网节点脆弱度评估[J]. 电力科学与技术学报, 2019, 34(3):143-149.
	REN Peng, LI Chong, TAO Peng, et al. Vulnerability assessment of power grid nodes based on weighted entropy TOPSIS method[J]. Journal of Electric Power Science and Technology, 2019, 34(3):143-149.
[12]	顾洁, 王春义, 牟宏, 等. 秩和比法在高压配电网项目评价中的应用[J]. 电力科学与技术学报, 2015, 30(1):80-86.
	GU Jie, WANG Chunyi, MOU Hong, et al. Application of rank sum ratio method in high voltage distribution network project evaluation[J]. Journal of Electric Power Science and Technology, 2015, 30(1): 80-86.
[13]	肖勇, 陆文升, 李云涛, 等. 城市配电网发展形态指标体系及其评估方法研究[J]. 电力系统保护与控制, 2021, 49(1):62-71.
	XIAO Yong, LU Wensheng, LI Yuntao, et al. Research on the indicator system and evaluation method of urban distribution network development form[J]. Power System Protection and Control, 2021, 49(1): 62-71.
[14]	YIN W, WEN T, ZHANG C. Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism[J]. Energy, 2023, 263: 125627. doi: 10.1016/j.energy.2022.125627
[15]	WANG K, WANG H, YANG J, et al. Electric vehicle clusters scheduling strategy considering real-time electricity prices based on deep reinforcement learning[J]. Energy Reports, 2022, 8:695-703. doi: 10.1016/j.egyr.2022.01.233
[16]	郭豪杰, 崔双喜. 基于分时电价的电动汽车有序充电策略研究[J]. 机电工程技术, 2021, 50(10):20-24.
	GUO Haojie, CUI Shuangxi. Research on ordered charging strategy for electric vehicles based on time of use tariff[J]. Mechanical & Electrical Engineering Technology, 2021, 50(10):20-24.
[17]	程杉, 汪业乔, 廖玮霖, 等. 含电动汽车的新能源微电网多目标分层优化调度[J]. 电力系统保护与控制, 2022, 50(12):63-71.
	CHENG Shan, WANG Yeqiao, LIAO Weilin, et al. Multi objective hierarchical optimization scheduling of new energy microgrids including electric vehicles[J]. Power System Protection and Control, 2022, 50 (12):63-71.
[18]	LI B, CHEN Y, WEI W, et al. Resilient restoration of distribution systems in coordination with electric bus scheduling[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3314-3325. doi: 10.1109/TSG.2021.3060801
[19]	电动汽车传导式充电接口: QC/T 841—2010[S].
[20]	黄春辉, 朱自伟, 裘昕月, 等. 偏差考核下售电公司的电动汽车电价策略研究[J]. 可再生能源, 2020, 38(9):1258-1265.
	HUANG Chunhui, ZHU Ziwei, QIU Xinyue, et al. Study on the electric vehicle tariff strategy of electricity selling companies under deviation assessment[J]. Renewable Energy Resources, 2020, 38 (9): 1258-1265.
[21]	王康平, 黄曲粲, 张兴科, 等. 考虑调峰成本的大规模电动汽车分群调度策略[J]. 综合智慧能源, 2022, 44(11): 12-19. doi: 10.3969/j.issn.2097-0706.2022.11.002
	WANG Kangping, HUANG Qucan, ZHANG Xingke, et al. Strategy for large-scale electric vehicles cluster scheduling considering peak-shaving cost[J]. Integrated Intelligent Energy, 2022, 44(11): 12-19. doi: 10.3969/j.issn.2097-0706.2022.11.002
[22]	周星月, 黄向敏, 张勇军, 等. 基于需求响应潜力模糊评估的电动汽车实时调控优化模型[J]. 电力自动化设备, 2022, 42(10):30-37.
	ZHOU Xingyue, HUANG Xiangmin, ZHANG Yongjun, et al. Optimization model for real-time regulation of electric vehicles based on fuzzy evaluation of demand response potential[J]. Electric Power Automation Equipment, 2022, 42 (10): 30-37.
[23]	张永新, 闫鹏领, 朱国栋. 电动汽车充电站站网互动运行优化技术研究与实践[J]. 综合智慧能源, 2022, 44(6):45-51. doi: 10.3969/j.issn.2097-0706.2022.06.005
	ZHANG Yongxin, YAN Pengling, ZHU Guodong. Research and practice on optimization technology for interactive operation of electric vehicle charging station network[J]. Integrated Intelligent Energy, 2022, 44 (6): 45-51. doi: 10.3969/j.issn.2097-0706.2022.06.005

充电模式	额定电压/V	额定电流/A	适用场所
慢速充电	单相220	16	小区
常规充电	单相220	32	商场、停车场
	三相380	32
	三相380	63
快速充电	600	300	高速公路服务区

项目	群体1	群体2	群体3
得分	1.27	2.40	1.88
排名	3	1	2