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 Foreword

With 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 data