配电网大数据技术分析与典型应用案例

2015年11月Power System Technology Nov. 2015 文章编号：1000-3673（2015）11-3114-08 中图分类号：TM 7 文献标志码：A 学科代码：470·40配电网大数据技术分析与典型应用案例王璟1，杨德昌2，李锰1，范征3，Mark Chew4（1．国网河南省电力公司经济技术研究院，河南省郑州市455000；2．中国农业大学信息与电气工程学院，北京市海淀区100083；3．北京艾能万德智能技术有限公司，北京市海淀区100083；4．美国AutoGrid公司，美国加利福尼亚州94065）Analysis of Big Data Technology in Power Distribution System and Typical Applications WANG Jing1, YANG Dechang2, LI Meng1, FAN Zheng3, Mark Chew4(1. State Grid Henan Economic Research Institute, Zhengzhou 455000, Henan Province, China;2. College of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing 100083, China;3. Beijing Energywende Intelligent Technologies Co., Ltd., Haidian District, Beijing 100083, China;4. Auto Grid Co., Ltd., Redwood Shores, California 94065, USA)ABSTRACT:AutoGrid Company and its two products, Energy Data Platform (EDP) and Demand Response Optimization and Management System (DROMS), are taken as examples. Firstly, characteristics and features of active distribution system data are summarized. Then, key technologies and main functions of EDP and DROMS are described in detail. Four study cases are employed to illustrate their applications. Finally, prospective application of big data technology is analyzed based on development of active distribution system. Some useful advices and suggestions are also proposed to ensure safe, reliable and economic operation of distribution system.KEY WORDS:big data; active distribution system; energy data platform; demand response optimization and management system摘要：以美国AutoGrid公司及其2个产品(能量数据平台EDP和需求侧优化管理系统DROMS)为例，分析了大数据技术在配电网数据分析中的应用。

首先，简要介绍了主动配电网中大数据的典型特点；然后列举了EDP和DROMS的关键技术和主要功能，并介绍了AutoGrid公司的4个典型应用案例；最后，结合配电网的设备水平和运行水平，总结归纳了配电网大数据应用的关键技术，提出配电网大数据应用的实施路线图。

关键词：大数据；主动配电网；能量数据平台；需求侧优化管理系统DOI：10.13335/j.1000-3673.pst.2015.11.015基金项目：国家自然科学基金青年基金项目(51407186)。

Project Supported by National Natural Science Foundation of China (51407186). 0 ForewordWith development of smart meters, utility companies are dealing with more data coming from more connected nodes than any industry, especially in the distribution system. Recently, many published works have explored the big data application in distribution systems. The main achievements can be classed into two aspects: 1）Summaries of big data theory. Paper [1] analyzed the basic features of the big data in distribution system. The typical applications in load forecasting, operating condition evaluation and power quality monitoring & warning are analyzed. In Paper [2], the data chain and “aircraft type”theoretical framework for big data application in distribution system was proposed. Furthermore, the application areas and roadmap were described based on the development of the distribution system. Moreover, many papers have proposed the big data analysis algorithms aiming at improving the computation efficiency [3-6]. In fact, the theory of big data is relatively mature based on the efforts from universities and academic researching institutes [7-8]. 2）The applications of big data to solve the real problems in the utilities. Many key technologies or softwares, including, AutoGrid, Oracle, OpenADR, Smart Energy Profile 2.0, et al, have implemented in some fields (media, social networking services, health care, traffic)[9-10]. However, the development of dataapplications in a distribution system is still at early stage[11]. Most of the literature focused on the academic analysis key technologies. Few of companies paid attention on the practical application in real power systems. AutoGrid is one of the notable pioneers in this area.In this paper, we introduce some products from AutoGrid based on the real applications in distribution systems. Firstly, the basic features of big data in distribution system are summarized from three aspects: data source, data variety and data computation. Then, two typical products (Energy Data Platform-EDP and Demand Response Optimization and Management System-DROMS) and their applications in distribution system are analyzed. The key technologies and implementation roadmap of big data in the distribution system (ADS) are proposedbased on the device level and operating level in China.Some useful suggestions are also listed in the conclusion.1 The Feature of Data in ADSMany papers have described the basic characteristics of data in the traditional distribution system. With the integrations of DGs and new loads as well as the construction of information model, the data in ADS present new features. These features are summarized and shown in Fig.1. From Fig.1, the features of data in ADS can be analyzed from six aspects: data source, data type, data coupling, data time-scale, data magnitude and data continuity, In this section, we explore the data source, data type and data mining in detail because these features have direct relationship with the applications of EDP and DROMS.The F eatures of Data in ADSThe S ources of Data is MassiveThe Time Scale of Data is UniformedThe Types of Data is DiversifiedThe M ining of Data is DifficultThe Time Continuity of Data is Poor The Relationship of Data is Coupling Internal Data GIS/CRM/AMI SCADA/EMS/BILLING External DataWeather/Market/Environment/Government/Customer/Building/IndustryThe Differences Between Online/ Offline DataThe C omparisons of Different Sam pleIntervalsThe Time Window of Data is DiversifiedThe C onversion of Data from Same Bus The Data Computations of the Different Buses The Inter-coupling between Topology andMeasurement DataThe Errors in Data Acquisition Process The Time Delay in Data Transmission Process The Information Omission and Leakage inTransm ission ProcessT h e D a t a M a g n i t u d e i s P B a n d R e d u n d a n t D i f f i c u l t t o E x t r a c t t h e V a l u a b l e I n f o r m a t i o n T h e P a r a m e t e r D i r e c t R e p r e s e n t i n g t h e O p e r a t i n g S t a t e i s n o t E n o u g hV o l t a g e /C u r r e n t /F r e q u e n c y /P o w e r Q u a l i t y T h e S t a c k o f S t a t i c a n d D y n a m i c D a t a T h e C o m b i n a t i o n o f M u l t i v a r i a t e & H e t e r o g e n e o u s D a t a图1 主动配电网中大数据的特征 Fig. 1 Features of data in ADS1.1 Analysis of Data SourcesThe data volume of ADS is much bigger than that of the transmission system. It concerns many measurement devices and sensors.According to the data sources, the huge information in ADS can be classified into two parts: the internal data and the external data.The classification of big data is shown in Fig.2 [12-13].图2 基于数据源的数据分类Fig. 2 Data classification based on data resourcesObviously, the internal data is from the devices of ADS. On the contrary, the external data from other measured information and statistics. 1.2 Analysis of Data VarietyAs pointed in the former literature, the big data in the ADS has “4 V ” characteristics: volume, velocity, variety and value. Volume refers to the amount of data, which in this case is in the petabyte range (6 petabytes = the entire US Library of Congress *50); Velocity contains two meanings: one is the disposing speed, most of the measured data in ADS is near real time; the other is the different time scales and sampling intervals. Value stands for that most of the data contains low and repeating information. From the aspect of variety, the big data includes the device data,3116 王璟等：配电网大数据技术分析与典型应用案例V ol. 39 No. 11operating data and management data. Different datashould be analyzed by different algorithm [14-15].The illustration is shown in Fig.3.图3主动配电网中大数据的多样性Fig. 3 Variety of the big data in ADS1.3 Analysis of Data MiningThe core of the application of big data is toextract the patterns from existing disparate sets to:1）predict outcomes and optimize solution; 2）executein real-time to improve operational decision-making.A variety of algorithms is commonly used to mine thedata, including the clustering, K-mean, neuralnetwork, correlation analysis [16-18].Compared with the traditional data systems, thedata in ADS contains new characteristics. Theexplanations are summarized as follows: 1）Widesources, complex relation, fine-grained, variety ofcrystal structure and fast production can be seen as thefirst feature of big data in ADS [1-2,19]. 2）Massivesize, rich information, hard processing and so on, arethe second main feature of big data in ADS. Fromabove analysis, it is necessary to seek for appliedtechnology of big data which is compatible with thecharacters of distribution system [20-21].Although many theoretical analyses of big datahave been done, the real application of big data toserve the utility and customer are just at thebeginning. There is a lack of information on how toimplement the real application in the society. In thispaper, one typical company named AutoGrid and theirproducts are proposed in the next chapter[22].2 Introduction of Products of AutoGridIn the last decade, participants in the electricitysupply chain have been processing thousands of timesmore data than before, mostly from smart meters,distributed generation and other building and gridsensing technologies. In this context, AutoGrid wasfounded in 2011, with the vision of turning the bigdata into knowledge and effective action.AutoGrid’s products are to organize theexponentially increasing amount of energy dataproduced from a networked and automated grid andmake it actionable for electricity generators andproviders, grid operators, and electricity users. It hasbuilt a highly scalable software system that is capableof processing the petabytes of data being generated,making real-time predictions and running complexoptimization algorithms across millions of variables.The customers of AutoGrid can be grouped into fourparts[22]:1）Large Utilities. By mining operational trendsand consumption patterns, AutoGrid’s Energy DataPlatform can forecast demand days, minutes or evenseconds in advance. With our tools, the emergencycapacity can be reduced by the big data analysis.2）MUNIS, CO-OPS. AutoGrid delivers servicesover the cloud, reducing the cost of demandmanagement systems by 90 percent. AutoGrid is also100-percent hardware neutral: high capital andoperational costs are no longer a barrier.3）Service Providers. With our software, ESCOs,demand response providers, electricity retailers andothers can employ demand management tools.AutoGrid increases power “yields”by 30 percent ormore.4）Facilities.High levels of security, rock-solidstability, compatibility with standards and a rapid ROImean that manufacturers and real estate managers canfollow through with plans to adopt energy efficiencystrategies.2.1 Energy Data Platform (EDP)Energy Data Platform (EDP) can analyzepetabytes of structured and unstructured data,streaming from smart meters, Supervisory Control andData Acquisition (SCADA) systems, and home,building or industrial energy management systems,along with electrical models of grid assets to create acomprehensive, multidimensional, and dynamicportrait of individual customers, groups of customers,or the entire grid. The EDP supports a multitude ofapplications, as shown in Fig.4[22].第39卷 第11期 电 网 技 术3117图4 EDP 的主要功能模块 Fig. 4 Main functions of EDPBy examining the relationship between tens of thousands of variables, EDP can discover consumption patterns, understand customer behavior, establish correlations between pricing and consumption, enable new and personalized services and pricing plans, monitor the health of the overall system in near real-time, and control grid assets to improve reliability and minimize losses across the entire electricity supply chain. Additionally, AutoGrid ’s system architecture performs all of these activities at scale-even in situations with millions of meters. By providing timely, actionable intelligence from the exponentially expanding wave of data, EDP allows creation of new applications that help with short-term tactical operations, as well as long term strategicplanning. The diagram of EDP is shown in Fig.5.图5 EDP 系统架构Fig. 5 Basic diagram of EDP2.2 Demand Response Optimization and Management System (DROMS)AutoGrid ’s DROMS is the world ’s most advanced demand management system. Harnessingbig data analytics and open standards, DROMS is a cost-effective, cloud-based service for implementing and managing a wide range of power management programs such as direct load control, critical peak pricing, peak-time rebates, grid balancing, spinning reserve and demand bidding[22].DROMS can be accessed through a highly securepublic or private cloud, depending on the needs of the customer. Because it is based on open standards, such as OpenADR and ZigBee, DROMS is compatible with the broad ecosystem of software, networks and hardware already deployed. By using open standards, AutoGrid also mean that we can leverage the advances in security and reliability being achieved through industry-wide initiatives.The basic functions of DROMS are shown in Fig.6.图6 DROMS 基本功能Fig. 6 Basic functions of DROMSThe key features of basic functions are explained as follows:1）Enrollment and program management. A complete system of record for all demand management activities.①Easy to use interface;②Compatible with all known demand response programs;③Supports millions of customer participants; ④Manage multiple programs and dual participation;⑤True operational visibility before, during and after events.2）Real-time resource forecasting. DROMS provides bottoms-up forecasting allowing aggregation and deeper insight into customer groups.①Better, more accurate representation of opt- outs; Load shed forecast is dropped by the forecasted3118 王璟等：配电网大数据技术分析与典型应用案例V ol. 39 No. 11load shed of the particular customer, rather than an average;②Allows forecasting of load shed under different price signals enabling optimization of a portfolio for hedging, revenue optimization, or reliability;③Best utilizes localized weather data leading to better regional forecasts;④Provide insights based on correlation of data points.3）Real-time Resource Optimization. Optimize demand resources across programs, resources and geographies through control and situational awareness.①Unified views of all of your programs;②Visualize program constraints and how they interact to avoid double counting resources;③Transition demand response from finite discrete events/year to a real-time resource;④Optimizes monetization of demand flexibility across programs –capacity, energy, reserves, regulation.4）Customer Notification Engine. Fully-featured customer communications platform for voice calls, Emails or Short Messages (SMS) text.①Communicate via voice calls, emails or SMS text;②Highly scalable- support millions of participants;③Multi-language support-communicate in any language;④Allow customers to put-out;⑤Advanced analytics and reporting to track customer receipts.5）Automated Demand Response. The largest ecosystem of direct, two-way, machine to machine communications to the control and instrumentation (C&I) and the residential devices, including Electric Vehicle Storage Service Equipments (EVSEs).①Central point of control to manage customer devices over multiple head-ends;②Compliant with OpenADR 1.0 and 2.0;③Compliant with Smart Energy Profile 1.x and2.0;④Seamless integration with AMI networks from Itron and Silver Spring Networks;⑤Interoperable with dozens of hardware platforms across C&I and residential;⑥Can integrate with any proprietary metering platform.6）Post event analytics. Robust real-time analytics to track events, provide feedback to customers, create settlement data and fulfill regulatory Measurement and Verification (M&V) requirements.①Run post-event analytics in real-time;②Create billing determinants for settlement;③Run M&V reports immediately after events;④Determine load shed potentials by participant class;⑤Audit legacy programs to identify non-performing devices.2.3 Study Cases2.3.1Florida Power: Predictive Asset Maintenance.In this project, the big data is utilized to improve the reliability of Florida Power & Light (FPL)’s distribution system. FPL’s Metric for reliability is System Average Interruption Duration Index (SAIDI) which they want to reduce by 17 minutes per customer per year. This will make a business impact of millions of dollars.Data that AutoGrid manages in this project include: 1）The number of smart meters is 4.6 million and the time intervals is 15 minute; 2）Substation data, Fault-current indicators, Capacitor bank reports; 3）10 other distribution automation devices on the feeder. AutoGrid’s software examines each of data streams as a single time series, allowing FPL to take advantage of patterns that span multiple data sources.2.3.2 Oklahoma Gas & Electric.Oklahoma Gas & Electric aims to avoid constructing 350MW of new power plants. These plants are need for peak periods but would only be used <2% of the time. Using accurate forecasting, AutoGrid has made load sheds reliable enough to avoid building these power plants while still keeping customers happy.The performances of EDP are that: 1）350 MW deferred power plant; 2）>95% customer satisfaction; 3）One customer can save 300$ in every year.2.3.3 E.ON: Revenue Assurance.E.ON Romania has high theft rates, every month第39卷第11期电网技术3119of undetected theft costs 15% of revenue. Moreover, the big data are collected from many different data sources and systems. The goal of the revenue assurance product is to accurately detect cases of electricity theft activity, allowing E.ON to reduce grid losses. In this situation, AutoGrid’s system employs machine learning to improve detection accuracy, resulting in a 300% improvement.2.3.4 Eneco: Flexibile Asset Management.The aim of this project is to analyze the real-time 24´7´365 dispatch grade intelligence. Eneco’s extensive portfolio of renewable resources and large customer base exposes the company to significant balancing costs, but conventional balancing resources can be unprofitable to operate. To address this problem, AutoGrid’s system uses big data and optimal dispatch to allow flexible asset owners to be paid for their flexibility. Examples of flexible assets include combined Heat and Power (CHP), C&I demand response, renewable requires real-time communication and optimal dispatch control across the Transmission System Operator (TSO). The presently solution of AutoGrid is to manage 100 MW of regulating and reserve capacity from Eneco’s agricultural CHPs.3 Roadmap of Big Data in ADSFrom above analysis, it is clear that AutoGrid has accumulated rich experience in guiding the utilities to utilize their big data sets. However, the application of big data on analysis of distribution system is very few in China. In this chapter, the key technologies, the application model and development roadmap of big data are proposed based on AutoGrid’s lessons learned.3.1 Key Technologies of Big Data in ADSMany papers have noted that many technologies have been employed to implement the big data analysis in the smart grid [1][9][10]. These technologies can be classified and described in Fig. 7.From Fig. 7, it is clear that the key technologies of big data involve all the processes of the big data. Any application of big data has direct and indirect relationships with the key technologies.3.2 Application Model of Big Data in ADSThe features of ADS can be summarizedas图7配电系统大数据关键技术Fig. 7 Key technologies of big data in ADS follows: 1）The number of data collection point is huge. Usually, every data collection point which located in different voltage levels can measure many kinds of data. 2）The sampling intervals of different data collection points are verified. 3）Normally, there are many redundancies, errors and missing in the data acquisition. 4）Generally speaking, the collected data are utilized to different application area. 5）Integration of distributed generations (DGs) into distribution system increases the complexity and uncertainty of the data collection. Based on these features, the application model of big data in ADS also can be analyzed from six aspects:1）In the planning aspect: analysis of relationship between the economic and power consumption; power map in the city; climate and weather data collection and analysis; analysis on power balance; the spatial planning of substations , et al.2）In the operating aspect: the drawing of load curve; the short time load and generation forecasting; the fast fault location and identification; the real time state estimation; the optimal controlling strategy; the stable/transient stability analysis, et al.3）In the managing aspect: the electric equipment management; electrical power theft management; demand side management; the integration management of DG and electric vehicle; the customer energy efficiency management; business expansion and marketing management, et al.4）In the controlling aspect: the real-time control of generation, network and load; the emergency control of whole and local system; the coordination of more kinds of DGs; the multi-time and spatial scale control; the control of energy storage devices, et al.5）In the serving aspect: the customized power service; the customer energy saving service; the3120 王璟等：配电网大数据技术分析与典型应用案例 V ol. 39 No. 11complementary standby service; the electricity fee analysis service, the active and reactive supporting service to the transmission system, the power system black-start service, et al.6）In the participating aspect: the active participation of DGs; the active participation of controllable load; the active participation of the electrical vehicle, et al.3.3 The Application Roadmap of Big Data in ADSBased on above descriptions and the actual application of software from AutoGrid, the application roadmap of big data in ADS can be summarized as follows: 1）From the aspects of time scale, the development of the big data technology should be arranged step by step (2015—2017—2020—2025); 2） From the aspects of the content, big data should be penetrated into all the part of the ADS (planning, operating, managing controlling, serving andparticipating); 3）From the aspect of implementation, the application of big data should be moved gradually (pilot project-gradual expansion-full construction). The road map of big data application in ADS can be illustrated in Fig. 8.From the description in Fig. 8, it is clear that the application of the Big Data in ADS contains 6 aspects: planning, operating, managing, controlling, serving and participating. From the time scale, it can be divided into four stages: 1）2015—2017, the demonstration projects should be built from the 6 contents of ADS; 2）2017—2020, the experience from typical application should be expanded from pilot project; 3） 2020—2025, the function of big data should be penetrated into all the respects of ADS, the application of big data into full implementation stage; 4）2025—future, big data technologies are mature and utilized in the whole active energy system.Build of the Pilot Project ExpansionConstructionMature图8 大数据在主动配电网中的应用路线图 Fig. 8 Road map of big data application in ADS4 ConclusionIn this paper, the big data technologies are introduced from the data sources, variety and analysis. Furthermore, the typical applications of big data areproposed based on the products of AutoGrid. The main conclusions are summarized as follows: 1）The roadmap of big data should be synchronized with the development of the ADS; 2）The application of the big第39卷第11期电网技术3121data technologies must be included in the power market reform and energy revolution; 3）The big data application in ADS should be connected with the cloud computation and internet technology. The theoretical analysis in this paper will guide the big data application in the developing process of the distribution system.References[1] 刘科研，盛万兴，张东霞，等．智能配电网大数据应用需求和场景分析研究[J]．中国电机工程学报，2015，35(2)：287-293．Liu Keyan，Sheng Wanxing，Zhang Dongxia，et al．Big data application requirements and scenario analysis in smart distribution network[J]．Proceedings of the CSEE，2015，35(2)：287-293(in Chinese)．[2] 赵腾，张焰，张东霞，等．智能配电网大数据应用技术与前景分析[J]．电网技术，2014，38(12)：3305-3312．Zhao Teng，Zhang Yan，Zhang Dongxia．et al．Application technology of big data in smart distribution grid and its prospect analysis[J]．Power System Technology，2014，38(12)：3305-3312(in Chinese)．[3] 刘道新，胡航海，张健，等．大数据全生命周期中关键问题研究及应用[J]．中国电机工程学报，2015，35(1)：23-28．Liu Daoxin，Hu Hanghai，Zhang Jian，et al．Research on key issues of big data lifecycle and its applications[J]．Proceedings of the CSEE，2015，35(1)：23-28(in Chinese)．[4] Han Jiawei，Micheline K，Pei Jian．Data mining：concepts andtechnique[M]．Third Edition．New York：Elsevier，2011：123-188．[5] 朱征，顾中坚，吴金龙，等．云计算在电力系统数据灾备业务中的应用研究[J]．电网技术，2012，36(9)：43-50．Zhu Zheng，Gu Zhongjian，Wu Jinlong，et al．Application of cloud computing in electric power system data recovery[J]．Power System Technology，2012，36(9)：43-50(in Chinese)．[6] 沐连顺，崔立忠，安宁．电力系统云计算中心的研究与实践[J]．电网技术，2011，35(6)：171-176．Mu Lianshun，Cui Lizhong，An Ning．Research and practice of cloud computing center for power system[J]．Power System Technology，2011，35(6)：171-176(in Chinese)．[7] 王德文．基于云计算的电力数据中心基础架构及其关键技术[J]．电力系统自动化，2012，36(11)：67-71．Wang Dewen．Basic framework and key technology for a new generation of data center in electric power corporation based on cloud computation[J]．Automation of Electric Power Systems，2012，36(11)：67-71(in Chinese)．[8] Manyika J，Chui M，Brown B，et al．Big data：the next frontier forinnovation，competition，and productivity[R]．USA：Mckinsey Global Institute，2011．[9] 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，2015，35(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(in Chinese)．[10] 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．Present status and challenges of big data processing in smart grid[J]．Power System Technology，2013，37(4)：927-935(in Chinese)．[11] 曲朝阳，王磊，曲楠，等．智能电网知识处理模型与可视化方法[M]．北京：科学出版社，2013：61-113．[12] 刘树仁，宋亚奇，朱永利，等．基于Hadoop的智能电网状态监测数据存储研究[J]．计算机科学，2013，40(1)：81-84．Liu Shuren，Song Yaqi，Zhu Yongli，et al．Research on data storage for smart grid condition monitoring using Hadoop[J]．Computer Science，2013，40(1)：81-84(in Chinese)．[13] Christophe B．The smart grid：Hadoop at the Tennessee ValleyAuthority(TVA)[EB/OL]．2009-06-01[2013-02-01]．http://www./blog/2009/06/smart-grid-hadoop-tennessee- valley-authority-tva/．[14] Kawasoe S，Igarashi Y，Shibayama K，et al．Examples of distributedinformation platforms constructed by power utilities in Japan[C]//CIGRE 2012．Paris：CIGRE，2012：1-7．[15] Kenneth P B，Lakshmi G，Robbert van R．Running smart grid controlsoftware on cloud computing architectures[C]//Workshop on Computational Needs for the Next Generation Electric Grid．Ithaca：Lawrence Berkeley National Laboratory，2011：1-4．[16] Merv Adrian．Big data：it’s going main stream and it’s your nextopportunity[J]．Teradata Magazine，2011，5(1)：3-5．[17] 张沛，吴潇雨，和敬涵．大数据技术在主动配电网中的应用综述[J]．电力建设，2015，36(1)：52-59．Zhang Pei，Wu Xiaoyu，He Jinghan，et al．Review on big data technology applied in active distribution network[J]．Electric Power Construction，2015，36(1)：52-59 (in Chinese)．[18] 邱健，牛琳琳，于海承，等．基于多源数据的在线数据评估技术[J]．电网技术，2013，37(9)：2658-2663．Qiu Jian，Niu Linlin，Yu Haicheng，et al．Research on online data assessment technology based on the multiple data source[J]．PowerSystem Technology，2013，37(9)：2658-2663(in Chinese)．[19] 闫湖，狄方春，袁荣昌，等．电网智能调度中的大数据及应用场景研究[J]．电力信息与通信技术，2014，12(10)：7-12．Yan Hu，Di Fangchun，Yuan Rongchang，et al．Research of on big data and its application scenarios in grid intelligent dispatching field [J]．Electric Power ICT，2014，12(10)：7-12(in Chinese)．[20] 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(in Chinese)．[21] 刘巍，黄曌，李鹏，等．面向智能配电网的大数据统一支撑平台体系与构架[J]．电工技术学报，2014，29(增刊1)：486-491．Liu Wei，Huang Zhao，Li Peng，et al．Summary about system and framework of unified supporting platform of big data for smart distribution grid transactions of China[J]．Transactions of China Electrotechnical Society，2014，29(Sup.1)：486-491(in Chinese)．[22] AutoGrid Corporation Software Group．AutoGrid creates technologyto help large utilities，munis，co-ops，services providers，facilities [EB/OL]．2013-11[2014-03]．/．收稿日期：2015-07-15。