智能电网大数据的分析与应用

doi:10.3969/j.issn.1000-7229.2015.10.002

电力建设 ›› 2015, Vol. 36 ›› Issue (10): 11-19.doi: 10.3969/j.issn.1000-7229.2015.10.002

智能电网大数据的分析与应用

高峰1，刘广一2，Chris Saunders1，朱文东1，陈振宇3,于洋3

1. 国网智研院美国研究院，Santa Clara, CA 95054分号2. 中国电力科学研究院，北京市 100192分号3. 斯坦福大学，Palo Alto 94305

出版日期:2015-10-01
作者简介:高峰（1977），博士，IEEE与INFORMS会员，高级工程师。研究方向为电力系统经济运行、电力市场交易、优化与决策分析、电力大数据、电力系统规划，人工智能技术；刘广一（1963），博士，教授级高级工程师，研究方向为电力系统经济调度、网络分析、 EMS/DMS、主动配电网、电力大数据和电力市场； Chris Saunders（1980），博士，高级工程师，研究方向为数据库架构、电力系统数据分析、电力系统数据挖掘、机器学习、电力系统运行与规划；朱文东（1969），硕士，高级工程师，研究方向为关系型数据库、NoSQL & NewSQL、分布式计算、实时流数据计算、基于内存计算的开源集群计算平台Spark及其在电力系统中的应用；陈振宇（1961），博士，斯坦福大学智能电网研究所主任。研究方向为智能电网大数据，分布式能源规划及运作，电力系统动态分析；于洋，博士，研究方向为电力市场设计、新能源接入、配电侧市场设计、电力消费分析、电力系统机制设计的环境影响、电力市场与碳市场等
基金资助:
国家自然科学基金资助项目（51261130472）；国家电网公司科技项目（DZB51201403772）。

Applications of Smart Grid Big Data Analytics

GAO Feng 1, LIU Guangyi 2, SAUNDERS Chris 1, ZHU Wendong 1, TAN Chin-woo 3, YU Yang 3

1.Smart Grid Research Institute North America Inc., Santa Clara, CA 95054分号2. China Electric Power Research Institute, Beijing 100192, China分号3. Stanford University，Palo Alto 94305

Online:2015-10-01
Supported by:
Project supported by National Natural Science Foundation of China （51261130472）；Science and Technology Program of the State Grid Corporation of China （DZB51201403772）.

摘要/Abstract

摘要：

能源互联网的兴起是全球能源环境与经济发展双重压力导致的结果，这种趋势不可避免地促进大数据分析技术的快速发展。大数据技术是指从各种各样类型的海量数据中，快速获得有价值信息的技术。简要介绍了国网智研院美国研究院大数据团队的研究工作和实验室软硬件配置，介绍了智能电网数据结构和计算架构等概念性的设计。重点介绍了与斯坦福大学合作进行的用户分组分类和需求响应定量计算方法，以及非侵入式电能分解。这些工作基于集成的数据模型和开源软件技术，将为电网公司和客户同时带来收益。

关键词: 大数据解析, 混合整数规划, 用电行为分析, 电能分解

Abstract:

With the rapid progress in information technology, a novel concept—“Energy Internet”, that concentrates on the coordination and optimization of multi-type energy flows via advanced communication and internet technology, has received a lot of attention. The result of such an inevitable trend is that a fundamental technique—Big Data Analytics—must be developed to handle massive influx of data from multiple heterogeneous sources, as well as utilize the data to swiftly derive an economic value. The paper gives an overview on research works conducted at SGRI North America big data lab with highlights on hardware configuration and software deployment of the cluster environment. The paper reviews several ongoing research topics performed in the lab with an emphasis on customer segmentation and response targeting （collaboration with Stanford University）；and energy disaggregation. These works are built on an integrated power system data model that is supported by open source technology. Preliminary results show that our research will benefit both utility companies and customers.

Key words: Big Data Analytics, mixed-integer programming, customer segmentation and targeting, energy disaggregation

中图分类号:

TM 76

高峰，刘广一，Chris Saunders，朱文东，陈振宇,于洋. 智能电网大数据的分析与应用[J]. 电力建设, 2015, 36(10): 11-19.

GAO Feng,LIU Guangyi SAUNDERS Chris,ZHU Wendong,TAN Chin-woo, YU Yang. Applications of Smart Grid Big Data Analytics[J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(10): 11-19.

参考文献

［1］What is big data analytics?-Definition from WhatIs.com［EB/OL］. http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics.
［2］Smart electric meters, Advanced metering infrastructure, and meter communications: Global market analysis and forecasts［EB/OL］ Navigant Research, 2013. http://www.navigantresearch.com/research/smart-meters.
［3］Yu N, Shah S, Johnson R, et al. Big data analytics in power distribution systems［J］. IEEE ISGT 2015.
［4］Wikipedia［EB/OL］. https://en.wikipedia.org/wiki/Phasor_measurement_unit
［5］Xie L, Chen Y, Kumar P R. Dimensionality reduction of synchrophasor data for early anomaly detection: linearized analysis［J］. IEEE Transactions on Power Systems, 2014, 29（6）: 2784-2794.
［6］Liu G, Yu Y, Gao F,et al. Research of smart gird big data model［C］. 23rd International Conference on Electricity Distribution Lyon, 2015.
［7］Saunders C, Liu G, Yu Y, et al. Data-driven distributed analytics and control platform for smart grid situational awareness［C］. CSEE, 2015.
［8］Spark official website［EB/OL］. https://spark.apache.org/.
［9］Liu G, Zhu W, Saunders C, et al. Real-Time Complex Event Processing and Analytics for Smart Grid［C］//Complex Adaptive Systems, San Jose: Conference Organized by Missouri University of Science and Technology, 2015.
［10］Kwac J, Tan C, Sintov N, et al. Utility customer segmentation based on Smart Meter Data: Empirical Study［J］. IEEE Smart Grid Community, 2013.
［11］Kwac J, Rajagopal R, Demand response targeting using big data analytics［C］// 2013 IEEE International Conference on Big Data.
［12］Dong R, Ratliff L, Ohlsson H, et al. A dynamical systems approach to energy disaggregation［C］//2013 IEEE 52nd Annual Conference on Decision and Control （CDC）.
［13］Kolter J Z, Batra S, Ng A Y. Energy disaggregation via discriminative sparse coding［C］//Advances in Neural Information Processing Systems, 2010.
［14］Drenker S, Kader A. Nonintrusive monitoring of electric loads［J］. IEEE Computer Applications in Power, 1999, 12（4）: 47-51.
［15］Rahayu D, Narayanaswamy B, Krishnaswamy S. Learning to be energy-wise: discriminative methods for load disaggregation［C］//Third International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet （e-Energy）, 2012.
［16］Farinaccio L, Zmeureanu R. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses［J］. Energy and Buildings, 1999, 30（3）: 245-259.
［17］Rajan D, Takriti S. Minimum up/down polytopes of the unit commitment problem with start-up costs［R］.IBM Research Report, 2005.

智能电网大数据的分析与应用

Applications of Smart Grid Big Data Analytics

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价