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能源大数据的系统构想及应用研究
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  • 英文篇名:System Conception and Application Research of Energy Big Data
  • 作者:金和平 ; 郭创新 ; 许奕斌 ; 廖伟涵
  • 英文作者:JIN Heping;GUO Chuangxin;XU Yibin;LIAO Weihan;China Three Gorges Corporation;College of Electrical Engineering,Zhejiang University;
  • 关键词:能源大数据 ; 物理信息融合 ; 云平台 ; 系统架构 ; 应用场景
  • 英文关键词:energy big data;;energy internet;;cyber-physical hybrid;;cloud platform;;system architecture;;application scenarios
  • 中文刊名:DBGC
  • 英文刊名:Hydropower and Pumped Storage
  • 机构:中国长江三峡集团有限公司;浙江大学电气工程学院;
  • 出版日期:2019-02-20
  • 出版单位:水电与抽水蓄能
  • 年:2019
  • 期:v.5;No.23
  • 语种:中文;
  • 页:DBGC201901005
  • 页数:13
  • CN:01
  • ISSN:32-1858/TV
  • 分类号:13-25
摘要
近年来,能源科技创新进入高度活跃期,以多能流互补、物理信息融合为特征的能源互联网,正在推动能源产业走向清洁、低碳、高效。大数据技术的发展,为能源互联网进一步实现数字化监管和"云上转型"提供了契机。本文立足于工程实际与未来需求,在能量流和信息流深度融合的能源互联网基础上,提出构建"一平台、两中心、三层次、多子系统"的能源大数据总体系统架构,支撑能源体系在数据、知识驱动下的智能化运转。最后,描绘了能源大数据的广阔应用场景,不仅实现了对能源系统运行管理的综合优化,也为智慧城市的发展提供了有力支撑。
        In recent years,energy science and technology innovation is highly active. The energy Internet,characterized by multi-energy complementarity and cyber-physical hybrid,is making the energy industry cleaner,more low-carbon and more efficient. The development of big data technology provides an opportunity for the further realization of digital supervision and cloud-based architecture of energy Internet. On the basis of the energy Internet,this paper proposes to build an engineeringoriented energy big data system architecture with one platform,two centers,three levels and multi-subsystems,which supports the intelligent operation of the energy system driven by data and knowledge. Finally,the application scenarios of energy big data are described,which not only realize the comprehensive optimization of energy system operation and management,but also provide a strong support for the development of smart cities.
引文
[1]张东霞,苗新,刘丽平,等.智能电网大数据技术发展研究[J].中国电机工程学报,2015(1):2-12.ZHANG Dongxia,MIAO Xin,LIU Liping,et al.Research on Development Strategy for Smart Grid Big Data[J].Proceedings of the CSEE,2015,35(1):2-12.
    [2]国务院办公厅.能源发展战略行动计划(2014-2020年)(摘录)[J].上海节能,2014(12):1-2.
    [3]国家发改委.关于推进“互联网+”智慧能源发展的指导意见[EB/OL].[2016-02-24].http://www.ndrc.gov.cn/zcfb/zcfbtz/201602/t20160229_790900.html
    [4]薛禹胜,赖业宁.大能源思维与大数据思维的融合(一)大数据与电力大数据[J].电力系统自动化,2016,40(1):1-8.XUE Yusheng,LAI Yening.Integration of Macro Energy Thinking and Big Data Thinking Part One Big Data and Power Bid data[J].Automation of Electric Power Systems,2016,40(1):1-8.
    [5]薛禹胜,赖业宁.大能源系统与大数据思维的融合(二)应用及探索[J].电力系统自动化,2016,40(8):1-13.XUE Yusheng,LAI Yening.Integration of Macro Energy Thinking and Big Data Thinking Part Two Applications and Explorations[J].Automation of Electric Power Systems,2016,40(8):1-13.
    [6]宋亚奇,周国亮,朱永利.智能电网大数据处理技术现状与挑战[J].电网技术,2013,37(4):927-935.SONG Yaqi,ZHOU Guoliang,ZHU Yongli,et al.Present Status and Challenges of Big Data Processing in Smart Grid[J].Power System Technology,2013,37(4):927-935.
    [7]彭小圣,邓迪元,程时杰,等.面向智能电网应用的电力大数据关键技术[J].中国电机工程学报,2015,35(3):503-511.PENG Xiaosheng,DENG Diyuan,CHENG Shijie,et al.Key Technologies of Electric power Big Data and Its Application Prospects in Smart Grid[J].Proceedings of the CSEE,2015,35(3):503-511.
    [8]Kezunovic M,Xie L,Grijalva S.The Role of Big Data in Improving Power System Operation and Protection[C]//Bulk Power System Dynamics&Control-ix Optimization,Security&Control of the Emerging Power Grid,Irep Symposium.IEEE,2013:1-9.
    [9]刘敦楠,唐天琦,赵佳伟,等.能源大数据信息服务定价及其在电力市场中的应用[J].电力建设,2017,38(2):52-59.LIU Dunnan TANG Tianqi,ZHAO Jiawei,et al.Big Energy Data Information Service Pricing and Its Application in Electricity Market[J].Electric Power Construction,2017,38(2):52-59.
    [10]刘世成,张东霞,朱朝阳,等.能源互联网中大数据技术思考[J].电力系统自动化,2016,40(8):14-21,56.LIU Shicheng,ZHANG Dongxia,ZHU Chaoyang,et al.AView on Big Data in Energy Internet[J].Automation of Electric Power Systems,2016,40(8):14-21,56.
    [11]赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11,19.ZHAO Junhua,DONG Zhaoyang,WEN Fushuan,et al.Data Science for Energy Systems:Theory,Techniques and Prospect[J].Automation of Electric Power Systems,2017,41(4):1-11,19.
    [12]王杨,于海涛,张旭,等.电力大数据基础平台建设与应用实践[M].北京:中国电力出版社,2016.
    [13]Bernstein D.The Emerging Hadoop,Analytics,Stream Stack for Big Data[J].IEEE Cloud Computing,2015,1(4):84-86.
    [14]董朝阳,赵俊华,文福拴,等.从智能电网到能源互联网:基本概念与研究框架[J].电力系统自动化,2014,38(15):1-11.DONG Zhaoyang,ZHAO Junhua,WEN Fushuan,et al.From Smart Grid to Energy Internet:Basic Concept and Research Framework[J].Automation of Electric Power Systems,2014,38(15):1-11.
    [15]Collins E.Intersection of the Cloud and Big Data[J].IEEE Cloud Computing,2014,1(1):84-85.
    [16]姜子卿,郝然,艾芊.基于冷热电多能互补的工业园区互动机制研究[J].电力自动化设备,2017,37(6):260-267.JIANG Ziqing,HAO Ran,AI Qian.Interaction Mechanism of Industrial Park based on Multi-energy Complementation[J].Electric Power Automation Equipment,2017,37(6):260-267.
    [17]Lakew A A,Bolland O.Working fluids for low-temperature heat source[J].Applied Thermal Engineering,2010,30(10):1262-1268
    [18]卫志农,张思德,孙国强,等.计及电转气的电-气互联综合能源系统削峰填谷研究[J].中国电机工程学报,2017,37(16):4601-4609.WEI Zhinong,ZHANG Side,SUN Guoqiang,et al.Powerto-gas Considered Peak Load Shifting Research for Integrated Electricity and Natural-gas Energy Systems[J].Proceedings of the CSEE,2017,37(16):4601-4609.
    [19]江秀臣,盛戈皞.电力设备状态大数据分析的研究和应用[J].高电压技术,2018,44(4):1041-1050.JIANG Xiuchen,SHENG Gehao.Research and Application of Big Data Analysis of Power Equipment Condition[J].High Voltage Engineering,2018,44(4):1041-1050.
    [20]严英杰,盛戈皞,陈玉峰,等.基于关联规则和主成分分析的输电线路状态评价关键参数体系构建[J].高电压技术,2015,41(7):2308-2314.YAN Yingjie,SHENG Gehao,CHEN Yufeng,et al.Establishment of Key Parameter System for Condition Evaluation of Transmission Line Based on Association Rules and Principal Component Analysis[J].High Voltage Engineering,2015,41(7):2308-2314.
    [21]Qiu J,Wang H,Lin D,et al.Nonparametric Regression-Based Failure Rate Model for Electric Power Equipment Using Lifestyle Data.IEEE Transactions on Smart Grid,2015,6(2):955-964.
    [22]魏星,舒乃秋,崔鹏程,等.基于改进PSO-BP神经网络和D-S证据理论的大型变压器故障综合诊断[J].电力系统自动化,2006,30(7):46-50.WEI Xing,SHU Naiqiu,CUI Pengcheng,et al.Power Transformer Fault Integrated Diagnosis Based on Improved PSO-BP Neural Networks and D-S Evidential Reasoning[J].Automation of Electric Power Systems,2006,30(7):46-50.
    [23]赵伟,白晓民,丁剑.基于协同式专家系统及多智能体技术的电网故障诊断方法[J].中国电机工程学报,2006,26(20):1-8.ZHAO Wei,BAI Xiaomin,DING Jian.A New Fault Diagnosis Approach of Power Grid Based on Cooperative Expert System and Multi-agent Technology[J].Proceedings of the CSEE,2006,26(20):1-8.
    [24]熊浩,李卫国,宋伟,等.概率聚类技术应用于变压器DGA数据故障诊断[J].高电压技术,2008,34(5):1022-1026.XIONG Hao,LI Weiguo,SONG Wei,et al.Application of Density-based Clustering Technology in Diagnosis of DGA Data of Transformer[J].High Voltage Engineering,2008,34(5):1022-1026.
    [25]郭创新,朱承治,张琳,等.应用多分类多核学习支持向量机的变压器故障诊断方法[J].中国电机工程学报,2010,30(13):128-134.GUO Chuangxin,ZHU Chengzhi,ZHANG Lin,et al.A Fault Diagnosis Method for Power Transformer Based on Multiclass Multiple-kernel Learning Support[J].Proceedings of the CSEE,2010,30(13):128-134.
    [26]周湶,孙超,安文斗,等.基于云推理及加权隐式半Markov模型的变压器故障预测[J].高电压技术,2015,41(7):2268-2275.ZHOU Quan,SUN Chao,AN Wendou,et al.Transformer Failure Prediction Based on Cloud Reasoning and Weighted Implicit Semi-Markov Model[J].High Voltage Engineering,2015,41(7):2268-2275.
    [27]张翔,宋子彤,杨致慧,等.一种基于负载率和设备检测信息的油浸式变压器故障率模型[J].电网技术,2013,37(4):1159-1165.ZHANG Xiang,SONG Zitong,YANG Zhihui,et al.AFailure Model for Oil-Immersed Transformer Based on Load Factor and Equipment Inspection Information[J].Power System Technology,2013,37(4):1159-1165.
    [28]王坤.考虑多重不确定性因素的光伏出力预测研究[D].华北电力大学,2013.
    [29]Lydia M,Selvakumar A I,Kumar S S,et al.Advanced Algorithms for Wind Turbine Power Curve Modeling[J].IEEETransactions on Sustainable Energy,2013,4(3):827-835.
    [30]高阳,张碧玲,毛京丽,等.基于机器学习的自适应光伏超短期出力预测模型[J].电网技术,2015,39(2):307-311.GAO Yang,ZHANG Biling,MAO Jingli,et al.Machine Learning-Based Adaptive Very-Short-Term Forecast Model for Photovoltaic Power[J].Power System Technology,2015,39(2):307-311.
    [31]徐龙博,王伟,张滔,等.基于神经网络平均影响值的超短期风电功率预测[J].电力系统自动化,2017,41(21),40-45.XU Longbo,WANG Wei,ZHANG Tao,et al.Ultra-shortterm Wind Power Prediction Based on Neural Network and Mean Impact Value[J].Automation of Electric Power Systems,2017,41(21),40-45.
    [32]王扬.风电短期预测及其并网调度方法研究[D].浙江大学,2011.
    [33]金和平,潘建初,朱强.水电工程信息化特征、架构与实践[J].水电与抽水蓄能,2018,4(05),1-9.JIN Heping,PAN Jianchu,ZHU Qiang.Hydropower Energy Engineering Information System Characteristics,Architecture and Practice[J].Hydropower and Pumped Storage,2018,4(05),1-9.
    [34]Hagan M T,Behr S M.The time series approach to short-term load forecasting[J].IEEE Transactions on Power System,1987,2(3):25-30.
    [35]王保义,赵硕,张少敏.基于云计算和极限学习机的分布式电力负荷预测算法[J].电网技术,2014,38(2):526-531.WANG Baoyi,ZHAO Shuo,ZHANG Shaomin.A Distributed Load Forecasting Algorithm Based on Cloud Computing and Extreme Learning Machine[J].Power System Technology,2014,38(2):526-531.
    [36]王德文,孙志伟.电力用户侧大数据分析与并行负荷预测[J].中国电机工程学报,2015,35(3):527-537.WANG Dewen,SUN Zhiwei.Big Data Analysis and Parallel Load Forecasting of Electric Power User Side[J].Proceedings of the CSEE,2015,35(3):527-537.
    [37]张素香,赵丙镇,王风雨,等.海量数据下的电力负荷短期预测[J].中国电机工程学报,2015,35(1):37-42.ZHANG Suxiang,ZHAO Bingzhen,WANG Fengxu,et al.Short-term Power Load Forecasting Based on Big Data[J].Proceedings of the CSEE,2015,35(1):37-42.
    [38]段斌,陈明杰,李辉,等.基于电能质量态势感知的分布式发电主动运行决策方法[J].电力系统自动化,2016,40(21):176-181.DUAN Bin,CHEN Mingjie,LI Hui,et al.Decision Method of Proactive Operation for Distributed Generation Based on Power Quality Situation Awareness[J].Automation of Electric Power Systems,2016,40(21):176-181.
    [39]徐成,梁睿,程真何,等.面向能源互联网的智能配电网安全态势感知[J].电力自动化设备,2016,36(6):13-18.XU Cheng,LIANG Rui,CHENG Zhenhe,et al.Security Situation Awareness of Smart Distribution Grid for Future Energy Internet[J].Electric Power Automation Equipment,2016,36(6):13-18.
    [40]Cai H,You S,Bindner H W,et al.Load Situation Awareness Design for Integration in Multi-Energy System[C]//IEEEInternational Conference on Energy Internet.IEEE,2017:42-47.
    [41]Gu Y,Jiang H,Zhang Y,et al.Knowledge Discovery for Smart Grid Operation,Control,and Situation AwarenessA Big Data Visualization Platform[C]//North American Power Symposium.IEEE,2016:1-6.
    [42]冯庆东.能源互联网与智慧能源[M].北京:机械工业出版社,2015.
    [43]郝然,艾芊,肖斐.基于多元大数据平台的用电行为分析构架研究[J].电力自动化设备,2017,37(8):20-27.HAO Ran,AI Qian,XIAO Fei.Architecture based on Multivariate Big Data Platform for Analyzing Electricity Consumption Behavior[J].Electric Power Automation Equipment,2017,37(8):20-27.
    [44]孟巍,吴雪霞,李静,等.基于大数据技术的电力用户画像[J].电信科学,2017,33(S1):15-20.MENG Wei,WU Xuexia,LI Jing,et al.Power User Portraits based on Big Data Technology[J].Telecommunications Science,2017,33(S1):15-20.
    [45]Yi Z,Bauer P H.Adaptive Multi-Resolution Energy Consumption Prediction for Electric Vehicles[J].IEEETransactions on Vehicular Technology,2017,PP(99):1-1.
    [46]胡海涛,郑政,何正友,等.交通能源互联网体系架构及关键技术[J].中国电机工程学报,2009,29(0):1-13.HU Haitao,ZHENG Zheng,HE Zhangyou,et al.The Framework and Key Technologies of Traffic Energy Internet[J].Proceedings of the CSEE,2009,29(0):1-13.
    [47]李正烁,郭庆来,孙宏斌,等.计及电动汽车充电预测的实时充电优化方法[J].电力系统自动化,2014,38(9):61-68.LI Zhengshuo,GUO Qinglai,SUN Hongbin,et al.Real-time Charging Optimization Method Considering Vehicle Charging Prediction[J].Automation of Electric Power Systems,2014,38(9):61-68.
    [48]张彦,张涛,刘亚杰,等.基于模型预测控制的家庭能源局域网最优能量管理研究[J].中国电机工程学报,2015,35(14):3656-3666.ZHANG Yan,ZHANG Tao,LIU Yajie,et al.Optimal Energy Management of a Residential Local Energy Network Based on Model Predictive Control[J].Proceedings of the CSEE,2015,35(14):3656-3666.
    [49]张延宇,曾鹏,臧传治.智能电网环境下家庭能源管理系统研究综述[J].电力系统保护与控制,2014,42(18):144-154.ZHANG Yanyu,ZENG Peng,ZANG Chuanzhi,et al.Review of Home Energy Management System in Smart Grid[J].Power System Protection and Control,2014,42(18):144-154.
    [50]徐永锋,李明,罗熙,等.分布式光伏能源驱动制冰蓄冷系统能量转化与流的数值模拟[J].中国电机工程学报,2016,36(12):3270-3277.XU Yongfeng,LI Ming,LUO Xi,et al.Numerical Simulation on Energy Conversion and Exergy Flow if Ice Storage System Driven by Distributed Solar Photovoltaic system[J].Proceedings of the CSEE,2016,36(12):3270-3277.
    [51]王坚,张悦.企业节能生产调度优化一阶混杂Petri网方法[J].计算机集成制造系统,2012,18(5):1011-1020.WANG Jian,ZHANG Yue.Enterprises Energy-Saving Production Dispatching Optimization based on First-Order Hybrid Petri net[J].Computer Integrated Manufacturing Systems,2012,18(5):1011-1020.
    [52]Wu J Y,Wang J L,Li S.Multi-Objective Optimal Operation Strategy Study of Micro-CCHP System[J].Energy,2012,48(1):472-483.
    [53]Wang J J,Jing Y Y,Zhang C F.Optimization of Capacity and Operation for CCHP System by Genetic Algorithm[J].Applied Energy,2010,87(4):1325-1335.
    [54]王静远,李超,熊璋,等.以数据为中心的智慧城市研究综述[J].计算机研究与发展,2014,51(2):239-259.WANG Jianyuan,LI Chao,XIONG Zhang,et al.Survey of Data-Centric Smart City[J].Journal of Computer Research and Development,2014,51(2):239-259.
    [55]郑宇.城市计算概述[J].武汉大学学报(信息科学版),2015,40(01):1-13.ZHENG Yu.Introduction of Urban Computing[J].Geomatics and Information Science of Wuhan University,2015,40(01):1-13.

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