智能电网数据资产的风险管理

doi:10.11959/j.issn.2096-0271.2019017

Abstract

Abstract:

Smart grid is a strategic level construction project being promoted.However,due to the complexity of the project itself,the level of smart grid data management in various departments in China is uneven,and there are many risks in data assetization.Therefore,risk management has become an essential part of the advancement of data assetization work.The characteristics of smart grid data assets were analyzed.Combining the characteristics of power industry,big data research methods and risk management elements,risk identification and management of smart grid data assets were advised,including data governance,data security,supply chain data sharing,customer credit risk control and smart grid robustness construction.And they were supported by an actual project and implementation effect of China’s power grid.Finally,the future development trend was analyzed.

Key words: smart grid, data asset management, risk management, supply chain management

CLC Number:

TP39

Aihua LI, Siguang CHEN, Yuejin ZHANG. Risk management of smart grid data assets[J]. Big Data Research, 2019, 5(2): 104-115.

Figures/Tables 4

内容	项目名称	风险描述	风险控制方法	应用效果
数据治理	分布式发电计量与采集	在分布式发电项目中，数据采集异常会导致数据缺失，电量统计错误会涉及合法合规风险	对计量装置加强监测，提高安装覆盖率和采集完整率，规范计量精度要求与误差处理办法，使分布式系统实现优化	“两率”达到95%以上，数据治理的数据采集环节得到充分保障
数据安全	数据关联与员工账号安全性	员工账号的不规范使用可能引发信息泄露风险，是数据安全风险来源之一	对于员工账号的使用明细数据，从IP登录、使用时间、使用权限和虚拟账号等方面进行数据挖掘分析，对风险行为和账号采取识别与控制措施	清理数十个僵尸账号和虚拟账号，规范使用规则，在一定程度上避免了权限滥用和信息泄露等风险
数据共享	账卡物一致监测分析	固定资产的价值、设备台账和实物信息涉及诸多系统，数据不一致会导致资产管理风险	采集设备数据，设置识别规则，通过自动和人工干预方式，使地理信息系统（GIS）、企业资源计划（ERP）、工厂维护（PM）模块和资产管理（AM）模块等系统的数据形成联动	各系统数据实现统一共享，合并资产卡片，设备联动率从76%提升到98%
客户信用风险控制	用户缴费行为数据管控电费回收风险	用户拖欠电费是电网客户关系管理的难点，可能引发财务风险	基于用户档案、用电及缴费行为等数据，建立客户信用风险模型，分析缴费行为与欠费的关联关系，采取有针对性的措施	提早识别高风险客户，显著降低了电费回收的风险
稳健性建设	配网薄弱环节监测分析	区域性、季节性电力供应是电网影响稳健运行的高风险考验	利用遥测数据、实时数据和拓扑信息等数据，精准定位重载和薄弱环节，量化分析抢修人员结构，实时预警停电故障	针对发现的问题，已加快配套工程的实施，开展布点补强，优化资源配置，提升服务质量

References 18

[1]	朱扬勇, 叶雅珍 . 从数据的属性看数据资产[J]. 大数据, 2018,4(6): 65-76.
	ZHU Y Y , YE Y Z . Defining data assets based on the attributes of data[J]. Big Data Research, 2018,4(6): 65-76.
[2]	WANG J , LI W , SONG Y S ,et al. Research on the theory and method of grid data asset management[J]. Procedia Computer Science, 2018(139): 440-447.
[3]	SONG W , ZHANG Y J , WANG J ,et al. Research on characteristics and value analysis of power grid data asset[J]. Procedia Computer Science, 2018(139): 158-164.
[4]	中国电机工程学会信息化专委会.中国电力大数据发展白皮书>[S]. 北京:中国电力出版社, 2013.
	Chinese Society for Electrical Engineering. The white paper on the development of big data on China electric[S]. Beijing:China Electric Power Press, 2013.
[5]	美国国家科学技术委员会.联邦大数据研究与开发战略计划[R]. 2016.
	National Science and Technology Council. The federal big data research and development strategic plan[R]. 2016.
[6]	宋晓明 . 美国怎么建设智能电网系统[N].北京:中国电子报,2015-07-14. 北京:中国电子报,2015-07-14.
	SONG X M . How does the United States build a smart grid system[N]. Beijing:China Electronics News,2015-07-14.
[7]	National Grid Interconnector Holdings Limited. Connecting for a smarter future[Z]. 2018.
[8]	CONNOR P M , AXON C J , XENIAS D ,et al. Sources of risk and uncertainty in UK smart grid deployment:an expert stakeholder analysis[J]. Energy, 2018(161): 1-9.
[9]	张啸宇 . 智能电网大数据处理技术现状[J]. 中国科技信息, 2018,21(1): 109-110.
	ZHANG X Y . Current status of big data processing technology for smart grid[J]. China Science and Technology Information, 2018,21(1): 109-110.
[10]	冷喜武, 陈国平, 蒋宇 ,等. 智能电网监控运行大数据分析系统的数据规范和数据处理[J]. 电力系统自动化, 2018,42(19): 169-176.
	LENG X W , CHEN G P , JIANG Y ,et al. Data specification and processing in big-data analysis system for monitoring and operation of smart grid[J]. Automation of Electric Power Systems, 2018,42(19): 169-176.
[11]	王继业 . 电力大数据技术及其应用[M]. 北京: 中国电力出版社, 2017.
	WANG J Y . Electric power big data technology and application[M]. Beijing: China Electric Power PressPress, 2017.
[12]	崔立真, 史玉良, 刘磊 ,等. 面向智能电网的电力大数据存储与分析应用[J]. 大数据, 2017,3(6): 42-54.
	CUI L Z , SHI Y L , LIU L ,et al. Applications of key technologies of storage and analysis in electric power big data for smart grid[J]. Big Data Research, 2017,3(6): 42-54.
[13]	DONG R , RATLIFF L J,CA?RDENAS A A ,et al. Quantifying the utility-privacy tradeoff in the Internet of things[J].,2018.Physical Systems,2018. ACM Transactions on CyberPhysical Systems,2018.Physical Systems, 2018.
[14]	杜跃进 . 数据安全治理的几个基本问题[J]. 大数据, 2018,4(6): 85-91.
	DU Y J . Several basic questions about data security governance[J]. Big Data Research, 2018,4(6): 85-91.
[15]	ZHOU L , OUYANG X , YING H ,et al. Cyberattack classification in smart grid via deep neural network[C]// The 2nd International Conference on Computer Science and Application Engineering,October 22-24,2018,Hohhot,China.[S.l.:s.n]. 2018.
[16]	凌卫家, 施永益 . 数说电网运营:电网企业运营大数据分析案例集萃[M]. 北京: 中国电力出版社, 2016.
	LING W J , SHI Y Y . Numbers say grid operation:a collection of big data analysis cases for grid companies operating[M]. Beijing: China Electric Power PressPress, 2016.
[17]	刘军, 陈实 . 面向电网智能管控的电力信息系统研究[J]. 电力大数据, 2018,21(9): 67-70.
	LIU J , CHEN S . Research on power information system for power grid intelligent management and control[J]. Power Systems and Big Data, 2018,21(9): 67-70.
[18]	CANO M A , HIDALGO S , DOLORES M . A survey on visual data representation for smart grids control and monitoring[J]. Sustainable Energy,Grids and Networks, 2018(16): 351-369.

Metrics

Recommended 0

No Suggested Reading articles found!

Risk management of smart grid data assets

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 18

Related Articles 9

Metrics

Recommended 0

[1]	Yezheng LIU, Lanfang ZONG, Dou JIN, Kun YUAN. Security risk analysis and countermeasures in the circulation and use of data factors [J]. Big Data Research, 2023, 9(2): 79-98.
[2]	Zerui ZHAO. Research on reform of governance system around the risk of digital platform companies [J]. Big Data Research, 2022, 8(4): 17-33.
[3]	Ming ZHAO, Dazhi DONG. Study on data asset management mechanism based on blockchain technology [J]. Big Data Research, 2021, 7(4): 49-60.
[4]	Bingrong DAI, Shanshan BI, Lin YANG, Tingting JI, Mei CHEN. Research status quo and suggestions on data assets standardization [J]. Big Data Research, 2020, 6(3): 36-44.
[5]	Yufei LI, Haiyan LIU, Shu YAN. Construction of a value-oriented realization of data asset management system [J]. Big Data Research, 2020, 6(3): 45-56.
[6]	Cheng WANG, Pengfei SHU. WEB:a fraud prediction method of Internet lending using network embedding [J]. Big Data Research, 2019, 5(6): 85-100.
[7]	Cong ZHANG, Xiaoyi FAN, Xiaoten LIU, Haitian PANG, Lifeng SUN, Jiangchuan LIU. Edge computing enabled smart grid [J]. Big Data Research, 2019, 5(2): 64-78.
[8]	Lizhen CUI, Yuliang SHI, Lei LIU, Zhuofeng ZHAO, Yanbing BI. Applications of key technologies of storage and analysis in electric power big data for smart grid [J]. Big Data Research, 2017, 3(6): 42-54.
[9]	Xinhai LIU, Han HAN, Wei DING, Ti WANG. Application of telecommunication big data in credit risk management [J]. Big Data Research, 2017, 3(3): 94-102.