基于三维空间的新型配电网关键技术时空适应性评估

doi:10.16183/j.cnki.jsjtu.2023.053

摘要/Abstract

摘要：

在新型电力系统发展的背景下,针对配电网规划建设和配电网关键技术在实际应用时与所处时空的适应能力、应用缺陷难定量化等问题,分析新型配电网关键技术的建设条件,提出一种基于电网满足度、时空资源度、成效改善度的三维立体空间的技术时空适应性评估模型.采用连续区间有序加权平均算子对层次分析法主观赋权进行改进,引入标准间冲突性相关性法构建的主客观组合赋权方法,解决了单一赋权法具有偏向性的问题.利用模糊综合评价法确定各指标隶属度,进而得到新型配电网关键技术评价等级.算例结果表明,所提方法能够量化配电网关键技术与所处时空的契合程度和双向辨识关键技术对于不同时空的适应性以及不同时空对于技术应用的满足度,揭示配电网关键技术的薄弱环节,有助于提升配电网投资效率与效益,更好地服务经济社会高质量发展.

关键词: 时空资源度, 三维空间立体评价, 组合赋权, 薄弱项分析, 连续区间数据有序加权平均算子

Abstract:

In the context of the development of new power systems, a three-dimensional spatio-temporal adaptation assessment model based on grid satisfaction, spatio-temporal resources, and effectiveness improvement is proposed to address the problems of distribution network planning and construction, the adaptability of key distribution network technologies to the space and time in which they are applied, and the difficulty of quantifying application defects. Subjective assignment in hierarchical analysis is improved using continuous interval ordered weighted average operator, and the problem of bias of the single assignment method is solved by the introduction of a subjective-objective combination assignment method constructed by the conflicting correlation among criteria method. The degree of affiliation of each indicator is determined using the fuzzy integrated evaluation method and the evaluation level is then obtained. The case studies show that the proposed method can quantify the degree of fit between the key technologies of the distribution network and the space-time, identify the adaptability of the key technologies to different space-times and the degree of satisfaction of the application of the technologies in different space-times, and reveal the weaknesses of the key technologies of the distribution network, which can help improve the efficiency and effectiveness of the investment in the distribution network and better serve the high-quality development of the economy and society.

Key words: spatio-temporal resources, three dimensions space evaluation, combination of empowerment, analysis of weaknesses, continuous interval argument-ordered weighted averaging (C-OWA) operator

中图分类号:

TM727

刘东铭, 曾庆彬, 张勇军, 张军, 樊玮, 刘宇. 基于三维空间的新型配电网关键技术时空适应性评估[J]. 上海交通大学学报, 2024, 58(10): 1489-1499.

LIU Dongming, ZENG Qingbin, ZHANG Yongjun, ZHANG Jun, FAN Wei, LIU Yu. Spatio-Temporal Adaptation Assessment of Key Technologies of New Distribution Network Based on 3D Space[J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1489-1499.

图/表 12

图1

图2

图3

表1

表2

表3

图4

表4

指标权重计算结果

指标	C-OWA
A11	0.6625	0.2509	0.1662	0.0274	0.0821
A12	0.8000	0.4072	0.3258	0.0351	0.1302
A13	0.4938	0.1208	0.0597	0.0342	0.0550
A14	0.3250	0.0546	0.0177	0.0326	0.0292
A15	0.3312	0.0643	0.0213	0.0326	0.0321
A21	0.2687	0.0407	0.0109	0.0289	0.0216
A22	0.3688	0.0615	0.0227	0.0555	0.0432
B11	0	0.0364	0	0.0548	0
B12	0.0688	0.0628	0.0043	0.0438	0.0167
B13	0	0.0364	0	0.0647	0
B14	0	0.0364	0	0.0631	0
B15	0.8688	0.4904	0.4261	0.0317	0.1415
B21	0.1250	0.0891	0.0111	0.0265	0.0209
B22	0.0313	0.0698	0.0022	0.0279	0.0095
B23	0	0.0379	0	0.0594	0
B24	0.2000	0.1408	0.0282	0.0295	0.0351
C11	0.8938	0.2674	0.2390	0.0324	0.1071
C12	0.8938	0.2674	0.2390	0.0303	0.1036
C13	0.0688	0.0335	0.0023	0.0298	0.0101
C21	0	0.0215	0	0.0268	0
C22	0	0.0215	0	0.0614	0
C23	0	0.0215	0	0.0462	0
C24	0	0.0215	0	0.0291	0
C31	0.7750	0.1811	0.1404	0.0305	0.0796
C32	0.7000	0.1152	0.0806	0.0325	0.0623
C41	0.1688	0.0494	0.0083	0.0333	0.0202
C42	0.2000	0.1408	0.0282	0.0295	0.0351

表4

表5

图5

表6

图6

参考文献 25

[1]	刘云鹏, 刘一瑾, 刘刚, 等. 电力变压器智能运维的数字孪生体构想[J]. 中国电机工程学报, 2023, 43(22): 8636-8652.
	LIU Yunpeng, LIU Yijin, LIU Gang, et al. Digital twin conception of intelligent operation and maintenance of power transformer[J]. Proceedings of the CSEE, 2023, 43(22): 8636-8652.
[2]	侯杰, 吴桂芳, 崔勇, 等. 硅基微型静电谐振式直流电场传感器建模与仿真分析[J]. 中国电机工程学报, 2021, 41(1): 374-382.
	HOU Jie, WU Guifang, CUI Yong, et al. Modeling and simulation analysis of silicon-based miniature electrostatic resonant DC electric field sensor[J]. Proceedings of the CSEE, 2021, 41(1): 374-382.
[3]	吴阳, 张品佳. 基于漏电流测量的配电网电缆高精度状态感知技术研究[J]. 中国电机工程学报, 2022, 42(8): 2929-2940.
	WU Yang, ZHANG Pinjia. High-accuracy condition sensing for power cables in distribution grids based on leakage current measurement approach: An overview[J]. Proceedings of the CSEE, 2022, 42(8): 2929-2940.
[4]	胡选正, 丛伟, 古千硕, 等. 基于电表量测数据的低压供电网络拓扑识别方法[J/OL]. 电力自动化设备. https://doi.org/10.16081/j.epae.202208044.
	HU Xuanzheng, CONG Wei, GU Qianshuo, et al. Topology identification method for low-voltage power supply network based on metermeasurement data[J/OL]. Electric Power Automation Equipment. https://doi.org/10.16081/j.epae.202208044.
[5]	李坤, 周来, 张勇军, 等. 基于量测数据质量的低压台区拓扑识别结果可信度评价[J]. 电力系统自动化, 2021, 45(17): 99-107.
	LI Kun, ZHOU Lai, ZHANG Yongjun, et al. Credibility evaluation of topology identification results in low-voltage distribution network based on quality of measured data[J]. Automation of Electric Power Systems, 2021, 45(17): 99-107.
[6]	ZHOU L, LI Q H, ZHANG Y J, et al. Consumer phase identification under incomplete data condition with dimensional calibration[J]. International Journal of Electrical Power & Energy Systems, 2021, 129: 106851.
[7]	孙轶恺, 漆淘懿, 张利军, 等. 市场环境下含冰蓄冷空调的综合能源系统优化运行[J]. 南方电网技术, 2022, 16(4): 95-104.
	SUN Yikai, QI Taoyi, ZHANG Lijun, et al. Optimal operation of integrated energy system including ice-storage air-conditioning in power market[J]. Southern Power System Technology, 2022, 16(4): 95-104.
[8]	杨夯, 黄小庆, 于慎仟, 等. 电化学储能电站主动安全研究[J]. 电力自动化设备, 2023, 43(8): 78-87.
	YANG Hang, HUANG Xiaoqing, YU Shenqian, et al. Research on active safety of electrochemical energy storage station[J]. Electric Power Automation Equipment, 2023, 43(8): 78-87.
[9]	周椿奇, 向月, 童话, 等. 轨迹数据驱动的电动汽车充电需求及V2G可调控容量估计[J]. 电力系统自动化, 2022, 46(12): 46-55.
	ZHOU Chunqi, XIANG Yue, TONG Hua, et al. Trajectory-data-driven estimation of electric vehicle charging demand and vechicle-to-grid regulable capacity[J]. Automation of Electric Power Systems, 2022, 46(12): 46-55.
[10]	陈果, 王秀丽, 原晟淇, 等. 适用于大规模充电场站的深度强化学习有序充电策略[J]. 电力系统自动化, 2023, 47(2): 88-95.
	CHEN Guo, WANG Xiuli, YUAN Shengqi, et al. Coordinated charging strategy applicable to large-scale charging stations based on deep reinforcement learning[J]. Automation of Electric Power Systems, 2023, 47(2): 88-95.
[11]	张勇军, 羿应棋, 李立浧, 等. 双碳目标驱动的新型低压配电系统技术展望[J]. 电力系统自动化, 2022, 46(22): 1-12.
	ZHANG Yongjun, YI Yingqi, LI Licheng, et al. Prospect of new low-voltage distribution system technology driven by carbon emission peak and carbon neutrality targets[J]. Automation of Electric Power Systems, 2022, 46(22): 1-12.
[12]	龚萍, 罗舒琦, 张远欣, 等. 综合能源系统的技术评价指标体系[J]. 电器与能效管理技术, 2021(12): 28-33.
	GONG Ping, LUO Shuqi, ZHANG Yuanxin, et al. Technical evaluation index system for integrated energy system[J]. Electrical & Energy Management Technology, 2021(12): 28-33.
[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 index system and its evaluation methods of urban distribution network development form[J]. Power System Protection & Control, 2021, 49(1): 62-71.
[14]	侯金鸣, 孙蔚, 肖晋宇, 等. 电力系统关键技术进步与低碳转型的协同优化[J]. 电力系统自动化, 2022, 46(13): 1-9.
	HOU Jinming, SUN Wei, XIAO Jinyu, et al. Collaborative optimization of key technology progress and low-carbon transition of power systems[J]. Automation of Electric Power Systems, 2022, 46(13): 1-9.
[15]	FAN W, LIU D M, YANG Z N, et al. A multidimensional adaptive assessment model for innovative technologies in new-type power system[C]// 2023 5th Asia Energy and Electrical Engineering Symposium. Chengdu, China: IEEE, 2023: 796-802.
[16]	罗宁, 贺墨琳, 高华, 等. 基于改进的AHP-CRITIC组合赋权与可拓评估模型的配电网综合评价方法[J]. 电力系统保护与控制, 2021, 49(16): 86-96.
	LUO Ning, HE Molin, GAO Hua, et al. Comprehensive evaluation method for a distribution network based on improved AHP-CRITIC combination weighting and an extension evaluation model[J]. Power System Protection & Control, 2021, 49(16): 86-96.
[17]	陈祉如, 郭亮, 杜艳, 等. 基于改进层次分析法的电能计量系统综合评价[J]. 山东大学学报(工学版), 2022, 52(6): 167-175.
	CHEN Zhiru, GUO Liang, DU Yan, et al. Comprehensive evaluation of metering system based on improved analytic hierarchy process[J]. Journal of Shandong University (Engineering Science), 2022, 52(6): 167-175.
[18]	徐悦, 李博, 孙建军, 等. 基于运行韧性评价的配电网电压暂降治理评估[J]. 电力系统自动化, 2021, 45(5): 104-110.
	XU Yue, LI Bo, SUN Jianjun, et al. Evaluation of voltage sag management in distribution network based on operation resilience assessment[J]. Automation of Electric Power Systems, 2021, 45(5): 104-110.
[19]	CHEN X, QIU J, REEDMAN L, et al. A statistical risk assessment framework for distribution network resilience[J]. IEEE Transactions on Power Systems, 2019, 34(6): 4773-4783.
[20]	亓彦珣. 城市配电网发展形态评估方法研究[D]. 保定: 华北电力大学(保定), 2020.
	QI Yanxun. Research on evaluation method of urban distribution network development form[D]. Baoding: North China Electric Power University, 2020.
[21]	杨锡运, 刘欢, 张彬, 等. 组合权重相似日选取方法及光伏输出功率预测[J]. 电力自动化设备, 2014, 34(9): 118-122.
	YANG Xiyun, LIU Huan, ZHANG Bin, et al. Similar day selection based on combined weight and photovoltaic power output forecasting[J]. Electric Power Automation Equipment, 2014, 34(9): 118-122.
[22]	KABIRIFAR M, FOTUHI-FIRUZABAD M, MOEINI-AGHTAIE M, et al. Reliability-based expansion planning studies of active distribution networks with multiagents[J]. IEEE Transactions on Smart Grid, 2022, 13(6): 4610-4623.
[23]	张显, 史连军. 中国电力市场未来研究方向及关键技术[J]. 电力系统自动化, 2020, 44(16): 1-11.
	ZHANG Xian, SHI Lianjun. Future research areas and key technologies of electricity market in China[J]. Automation of Electric Power Systems, 2020, 44(16): 1-11.
[24]	LI Z H, WU W C, TAI X, et al. Optimization model-based reliability assessment for distribution networks considering detailed placement of circuit breakers and switches[J]. IEEE Transactions on Power Systems, 2020, 35(5): 3991-4004.
[25]	高孟友. 智能配电网分布式馈线自动化技术[D]. 济南: 山东大学, 2016.
	GAO Mengyou. Distributed feeder automation technology for intelligent distribution network[D]. Jinan: Shandong University, 2016.

等级类型	主要特征	等级特征
不契合型	X、Y、Z三轴评价指标均为差,或其中仅有一轴为良	所评价的配电网关键技术完全不适合在该时空建设应用
较不契合型	X、Y、Z三轴评价指标中有两轴为良一轴为差,或有两轴为差一轴为优	所评价的配电网关键技术不适合在该时空建设应用
契合型	X、Y、Z三轴评价指标均为良,或三轴分别为优良差	所评价的配电网关键技术可以在该时空建设应用
较为契合型	X、Y、Z三轴评价指标中有两轴为良一轴为优,或有两轴为优一轴为差	所评价的配电网关键技术较为适合在该时空建设应用
完全契合型	X、Y、Z三轴评价指标均为优,或其中仅有一轴为良	所评价的配电网关键技术完全适合在该时空建设应用

标度	含义
1	表示两个元素相比,具有同样的重要性
3	表示两个元素相比,前者比后者稍重要
5	表示两个元素相比,前者比后者明显重要
7	表示两个元素相比,前者比后者非常重要
9	表示两个元素相比,前者比后者极其重要
2,4,6,8	表示上述相邻判断的中间值
1~9的倒数	表示相应两因素交换次序比较的重要性

评价指标	指标类型	分类等级
评价指标	指标类型	3级(优)	2级(良)	1级(差)
A11	逆向型	[0,400)	[400,600]	(600,+∞)
A12/%	正向型	(90,100]	[80,90]	(-∞,80)
A13/%	逆向型	[0,5)	[5,7]	(7,+∞)
A14/%	正向型	(50,+∞)	[35,50]	(-∞,35)
A15/%	逆向型	[0,10)	[10,25]	(25,+∞)
A21	正向型	(15,+∞)	[6,15]	(-∞,6)
A22/%	逆向型	[0,10)	[10,20]	(20,+∞)
B11	正向型	(6000,+∞)	[5000,6000]	(-∞,5000)
B12	区间型	(10,20)	[0,10]∪[20,25]	(-∞,10)∪(25,+∞)
B13	区间型	(6.5,11)	[3.2,6.5]∪[11,23]	(-∞,3.2)∪(23,+∞)
B14/%	正向型	(60,100]	[30,60]	(-∞,30)
B15/%	正向型	(70,100]	[40,70]	(-∞,40)
B21	正向型	(3,+∞)	[2.2~3]	(-∞,2.2)
B22/%	正向型	(40,+∞)	[20,40]	(-∞,20)
B23	正向型	(80,+∞)	[40~80]	(-∞,40)
B24	正向型	(1000,+∞)	[300~1000]	(-∞,300)
C11/%	正向型	(90,100]	[70,90]	(-∞,70)
C12	逆向型	[0,0.8)	[0.8,3]	(3,+∞)
C13/%	正向型	[98,100]	[96,98.5]	(-∞,96)
C21/%	正向型	(98,100]	[96.5,98]	(-∞,96.5)
C22/%	正向型	(60,+∞)	[30,60]	(-∞,30)
C23/%	正向型	(30,+∞)	[20,30]	(-∞,20)
C24/%	正向型	(10,+∞)	[0,10]	(-∞,0)
C31/%	正向型	(99,100]	[97,99]	(-∞,97)
C32/%	正向型	(85,100]	[60,85]	(-∞,60)
C41/%	正向型	(90,100]	[70,90]	(-∞,70)
C42/%	正向型	(50,100]	[15,50]	(-∞,15)

地区	X	Y	Z	评价等级
A	14.80	30.00	22.16	完全契合型
B	30.00	26.70	21.89	完全契合型
C	19.05	17.72	18.47	契合型
D	14.32	5.59	14.89	较不契合型
E	4.86	2.56	9.06	不契合型

地区	配网关键技术	X	Y	Z	评价等级
C	I.分布式光伏	12.72	21.73	23.84	完全契合型
	II.小水电微网	10.48	7.58	30.00	契合型
	III.分布式风电	17.97	10.53	24.82	较为契合型
D	I.分布式光伏	9.96	30.00	18.56	契合型
	II.小水电微网	7.42	2.16	25.22	较不契合型
	III.分布式风电	19.70	2.67	17.97	较不契合型