大用户多维度可中断特性精细化分析

doi:10.19595/j.cnki.1000-6753.tces.L80290

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (31553 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The study of large-consumers’ interruptible features is the basis to schedule interruptible loads. This paper chose the Ward system clustering method combined with improved fuzzy C-means clustering method (FCM) which takes into consideration the proximity correlation of each sample to deal with the consumers’ daily load data. The number of clusters was optimized by the validity function. Then the twice classification model was proposed to analyze large users’ interruptible characteristics. The refined load data classification was obtained by two-level clustering and the cluster centers were extracted as typical power consumption patterns. By comparing the several typical load curves, the interruptible characteristics of large users were analyzed from four dimensions: interruptible schedule mode, interruptible capacity, interruptible span and interruptible time. Finally, based on the actual historical data, the multi-dimensional interruptible characteristics of a user in the cotton textile printing and dyeing industry were successfully excavated, which verifies the effectiveness of the proposed method and model.

Key words： Improved fuzzy C-means clustering method validity function twice classification multi-dimension interruptible characteristics

Received: 29 June 2018 Published: 05 March 2020

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L80290 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/Izk1/284

[1] 李滨, 李星辰, 白晓清, 等. 电力市场下配电网可中断负荷控制策略研究[J]. 中国电机工程学报, 2018, 38(22): 6573-6582.
Li Bin, Li Xingchen, Bai Xiaoqing, et al.A research on the IL control strategy of distribution network in electricity market[J]. Proceedings of the CSEE, 2018, 38(22): 6573-6582.
[2] 郭红霞, 白浩, 刘磊, 等. 统一电能交易市场下的虚拟电厂优化调度模型[J]. 电工技术学报, 2015, 30(23): 136-145.
Guo Hongxia, Bai Hao, Liu Lei, et al.Optimal scheduling model of virtual power plant in a unified electricity trading market[J]. Transactions of China Electrotechnical Society, 2015, 30(23): 136-145.
[3] 张赢, 董立军, 黄雯. 国内需求响应发展障碍分析及展望[J]. 电气技术, 2017, 18(7): 1-10.
Zhang Ying, Dong Lijun, Huang Wen.Analysis on development obstacles of demand response and its outlook[J]. Electrical Technology, 2017, 18(7): 1-10.
[4] 孙建军, 张世泽, 曾梦迪, 等. 考虑分时电价的主动配电网柔性负荷多目标优化控制[J]. 电工技术学报, 2018, 33(2): 401-412.
Sun Jianjun, Zhang Shize, Zeng Mengdi, et al.Multi- objective optimal control for flexible load in active distribution network considering time-of-use tariff[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 401-412.
[5] Samadi P, Wong V W S, Schober R. Load scheduling and power trading in systems with high penetration of penewable energy resources[J]. IEEE Transactions on Smart Grid, 2016, 7(4): 1802-1812.
[6] 杨晓东, 张有兵, 翁国庆, 等. 基于虚拟电价的电动汽车充放电优化调度及其实现机制研究[J]. 电工技术学报, 2016, 31(17): 52-62.
Yang Xiaodong, Zhang Youbing, Weng Guoqing, et al.Virtual time-of-use tariffs based optimal scheduling and implementation mechanism of electric vehicles charging and discharging[J]. Transactions of China Electrotechnical Society, 2016, 31(17): 52-62.
[7] 贾星蓓, 窦春霞, 岳东, 等. 基于多代理系统的微电网多尺度能量管理[J]. 电工技术学报, 2016, 31(17): 63-73.
Jia Xingbei, Dou Chunxia, Yue Dong, et al.Multiple- time-scales optimal energy management in microgrid system based on multi-agent-system[J]. Transactions of China Electrotechnical Society, 2016, 31(17): 63-73.
[8] 张旭升, 李瑞生, 黄利军, 等. 基于分层储能的主动配电网需求响应控制策略研究及实现[J]. 电力系统保护与控制, 2017, 45(15): 40-49.
Zhang Xusheng, Li Ruisheng, Huang Lijun, et al.Research and application on demand response based on hierarchical power storage[J]. Power System Protection and Control, 2017, 45(15): 40-49.
[9] 孙博宇, 喻洁, 梅军, 等. 考虑可调负荷集群响应不确定性的联合调度模型[J]. 电力系统保护与控制, 2016, 44(23): 61-67.
Sun Boyu, Yu Jie, Mei Jun, et al.Combined dispatch model of adjustable load clusters considering response uncertainty[J]. Power System Protection and Control, 2016, 44(23): 61-67.
[10] 高滢, 王芃, 薛友, 等. 计及需求侧管理的电—气集成能源系统协同规划[J]. 电力系统自动化, 2018, 42(13): 3-11.
Gao Ying, Wang Peng, Xue You, et al.Collaboration planning of integrated electricity-gas energy systems considering demand side management[J]. Automation of Electric Power Systems, 2018, 42(13): 3-11.
[11] 张钦, 王锡凡, 王建学, 等. 电力市场下需求响应研究综述[J]. 电力系统自动化, 2008, 32(3): 97-106.
Zhang Qin, Wang Xifan, Wang Jianxue, et al.Survey of demand response research in deregulated electricity markets[J]. Automation of Electric Power Systems, 2008, 32(3): 97-106.
[12] 张鹏, 李春燕, 张谦. 基于需求响应调度容量上报策略博弈的电网多代理系统调度模式[J]. 电工技术学报, 2017, 32(19): 170-179.
Zhang Peng, Li Chunyan, Zhang Qian.A power system dispatch model based on game-theoretic multi-agent system demand response scheduling capacity reporting[J]. Transactions of China Electro- technical Society, 2017, 32(19): 170-179.
[13] 李力行, 苗世洪, 孙丹丹, 等. 多利益主体参与下主动配电网完全信息动态博弈行为[J]. 电工技术学报, 2018, 33(15): 3499-3509.
Li Lixing, Miao Shihong, Sun Dandan, et al.Dynamic games of complete information in active distribution network with multi-stakeholder partici- pation[J]. Transactions of China Electrotechnical Society, 2018, 33(15): 3499-3509.
[14] 黄海涛, 胡学英, 李翔, 等. 实用化的激励性可中断负荷最优补偿定价模型[J]. 电网技术, 2014, 38(8): 2149-2154.
Huang Haitao, Hu Xueying, Li Xiang, et al.A practicable optimal compensation pricing model for incentive interruptible load contract[J]. Power System Technology, 2014, 38(8): 2149-2154.
[15] 李欣然, 姜学皎, 钱军, 等. 基于用户日负荷曲线的用电行业分类与综合方法[J]. 电力系统自动化, 2010, 34(10): 56-61.
Li Xinran, Jiang Xuejiao, Qian Jun, et al.A classi- fying and synthesizing method of power consumer industry based on the daily load profile[J]. Auto- mation of Electric Power Systems, 2010, 34(10): 56-61.
[16] 徐青山, 丁一帆, 颜庆国, 等. 大用户负荷调控潜力及价值评估研究[J]. 中国电机工程学报, 2017, 37(23): 6791-6800.
Xu Qingshan, Ding Yifan, Yan Qingguo, et al.Research on evaluation of scheduling potentials and values on large consumers[J]. Proceedings of the CSEE, 2017, 37(23): 6791-6800.
[17] 陈明照, 毛坚, 杜宗林, 等. 基于聚类法的工业用户需求侧管理(DSM)方案分析与研究[J]. 电力系统保护与控制, 2017, 45(7): 84-89.
Chen Mingzhao, Mao Jian, Du Zonglin, et al.Analysis on demand side management scheme of industrial enterprise based on clustering method[J]. Power System Protection and Control, 2017, 45(7): 84-89.
[18] Azari D, Torbaghan S S, Cappon H, et al.Assessing the flexibility potential of the residential load in smart electricity grids—a data-driven approach[C]// International Conference on the European Energy Market, Dresden, Germany, 2017: 1-6.
[19] Sun Yanping, Gao Yajing, Yang Wenhai, et al.Research on user optimal aggregation based on demand response potential spectrum clustering analysis[J]. Journal of Engineering, 2017(13): 2152-2157.
[20] Jiao Ruxuan, Wen Xiangming, Lu Zhaoming, et al.Dynamic user-centric clustering algorithm based on energy efficiency in cloud-RAN[C]//IEEE Inter- national Conference on Telecommunications, Barcelona, Spain, 2017: 1-7.
[21] Pandey R K, Kumar S, Kumar C.Development of cluster algorithm for grid health monitoring[C]// International Conference on Control, Instrumentation, Energy & Communication, Kolkata, India, 2016: 377-381.
[22] 张传辉, 田建艳, 高炜, 等. 基于模糊聚类分析的风电功率预测研究[J]. 太原理工大学学报, 2018, 49(1): 133-139.
Zhang Chuanhui, Tian Jianyan, Gao Wei, et al.Research on wind power forecasting based on fuzzy clustering analysis[J]. Journal of Taiyuan University of Technology, 2018, 49(1): 133-139.
[23] 李赢, 舒乃秋. 基于模糊聚类和完全二叉树支持向量机的变压器故障诊断[J]. 电工技术学报, 2016, 31(4): 64-70.
Li Ying, Shu Naiqiu.Transformer fault diagnosis based on fuzzy clustering and complete binary tree support vector machine[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 64-70.
[24] Bensaid A M, Hall L O, Bezdek J C, et al.Validity guided(re) clustering with applications to image segmentation[J]. IEEE Transactions on Fuzzy Systems, 2002, 4(2): 112-123.
[25] 钮永莉, 魏光杏. 一种改进的模糊聚类算法及其应用[J]. 长春工程学院学报: 自然科学版, 2018, 19(2): 96-98.
Niu Yongli, Wei Guangxing.An improved fuzzy clustering algorithm and its application[J]. Journal of Changchun Institute of Technology: Natural Science Edition, 2018, 19(2): 96-98.
[26] 陈兴荣, 姚宁宁. 基于能量距离推广的Ward聚类算法研究[J]. 统计与决策, 2017, 22(4): 21-25.
Chen Xingrong, Yao Ningning.Research on Ward clustering method based on energy distance extending[J]. Statistics and Decision, 2017, 22(4): 21-25.
[27] 陈舵, 李雪, 崔杜武, 等. 一种基于模糊度的聚类有效性函数[J]. 模式识别与人工智能, 2008, 21(1): 34-41.
Chen Duo, Li Xue, Cui Duwu, et al.Cluster validity function based on fuzzy degree[J]. PR and AI, 2008, 21(1): 34-41.