Edge computing enabled smart grid

Cong ZHANG; Xiaoyi FAN; Xiaoten LIU; Haitian PANG; Lifeng SUN; Jiangchuan LIU

doi:10.11959/j.issn.2096-0271.2019014

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Edge computing enabled smart grid

TOPIC：：EDGE COMPUTING | 更新时间：2024-06-03

- Edge computing enabled smart grid
- Big Data Research Vol. 5, Issue 2, Pages: 2019014-1(2019)
- 作者机构：
  
  1. 中国科学技术大学计算机科学与技术学院，安徽合肥 230027
  2. 深圳江行联加智能科技有限公司，广东深圳 518000
  3. 清华大学计算机科学与技术系，北京 100084
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019014
  CLC： TP393.4
- Online First：2019-03，
  
  Published：15 March 2019
- 稿件说明：
移动端阅览
Cong ZHANG, Xiaoyi FAN, Xiaoten LIU, et al. Edge computing enabled smart grid[J]. Big Data Research, 2019, 5(2): 2019014-1.
DOI：

Cong ZHANG, Xiaoyi FAN, Xiaoten LIU, et al. Edge computing enabled smart grid[J]. Big Data Research, 2019, 5(2): 2019014-1. DOI： 10.11959/j.issn.2096-0271.2019014.

摘要

随着物联网技术、高速通信技术、智能芯片技术的发展，协同利用这些技术提高传统电网在设备数据收集、储能策略分析和用电安全监控等方面的效能成为亟待解决的难题。对边缘计算技术如何助力电力企业智能化转型进行了着重分析，讨论了边缘计算在构建低时延、轻量级、高效能和高可靠的智慧电网平台方面的优势，介绍了智慧电网中的各类应用场景和对应的边缘计算解决方案。最后，对国内外两种边缘计算平台及其使用的主要技术和应用场景进行了分析。

Abstract

How to mitigate such pressure has become a critical challenge for global grid enterprises.Empowered by the development of IoT techniques

high-speed communications

and AI-chips

traditional grid enterprises are trying to adopt these new techniques to solve the challenge

especially in heterogeneous data collection

energy scheduling

and AI-enabled monitoring.Firstly

the background of smart grid and the history of edge computing were introduced.Then

edge computing enabled solutions were illustrated in three typical grid scenarios.Finally

two edge computing platforms at home and abroad were introduced.The main technologies and application scenarios of them were also analyzed.

关键词

Keywords

references

RAZA M Q , KHOSRAVI A . A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings [J ] . Renewable and Sustainable Energy Reviews , 2015 , 50 : 1352 - 1372 .

TRAN T X , HAJISAMI A , PANDEY P , et al . Collaborative mobile edge computing in 5G networks:new paradigms,scenarios,and challenges [J ] . IEEE Communications Magazine , 2017 , 55 ( 4 ): 54 - 61 .

MAO Y , YOU C , ZHANG J , et al . A survey on mobile edge computing:the communication perspective [J ] . IEEE Communications Surveys＆ Tutorials , 2017 , 19 ( a4 ): 2322 - 2358 .

NUNNA S , KOUSARIDAS A , IBRAHIM M , et al . Enabling real-time context-aware collaboration through 5G and mobile edge computing [C ] // The 12th International Conference on Information Technology-New Generations (ITNG),April 13-15,2015,Las Vegas,USA . Piscataway:IEEE Press , 2015 : 601 - 605 .

HU Y C , PATEL M , SABELLA D , et al . Mobile edge computing-a key technology towards 5G [M ] // ETSI white paper.[S.l:S.n . ] , 2015 .

NAQVI S A A , JAVAID N , BUTT H , et al . Metaheuristic optimization technique for load balancing in cloud-fog environment integrated with smart grid [C ] // The 21st International Conference on Network-Based Information Systems,September 5-7,2018,Slovakia . Cham:Springer , 2018 : 700 - 711 .

LIANG W , ZHENG M , ZHANG J , et al . WIAFA and its applications to digital factory:a wireless network solution for factory automation [J ] . Proceedings of the IEEE , 2019 ( 2 ): 1 - 21 .

屈志昊 , 叶保留 , 陈贵海 , 等 . 面向边缘计算的资源优化技术研究进展 [J ] . 大数据 , 2019 , 5 ( 2 ):1733.

QU Z H , YE B L , CHEN G H , et al . State-ofthe-art survey on resource optimization in edge computing [J ] . Big Data Research , 2019 , 5 ( 2 ):1733.

CHEN X , JIAO L , LI W , et al . Efficient multi-user computation offloading for mobile-edge cloud computing [J ] . IEEE/ACM Transactions on Networking , 2016 , 24 ( 5 ): 2795 - 2808 .

LIU J , WAN J , ZENG B , et al . A scalable and quick-response software defined vehicular network assisted by mobile edge computing [J ] . IEEE Communications Magazine , 2017 , 55 ( 7 ): 94 - 100 .

LI S , GUO Z , SHOU G , et al . QoE analysis of NFV-based mobile edge computing video application [C ] // 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC),September 23-25,2016,Beijing,China . Piscataway:IEEE Press , 2016 : 411 - 415 .

HU P , DHELIM S , NING H , et al . Survey on fog computing:architecture,key technologies,applications and open issues [J ] . Journal of Network and Computer Applications , 2017 , 98 : 27 - 42 .

JARADAT M , JARRAH M , BOUSSELHAM A , et al . The internet of energy:smart sensor networks and big data management for smart grid [J ] . Procedia Computer Science , 2015 , 56 : 592 - 597 .

SHI W , DUSTDAR S . The promise of edge computing [J ] . Computer , 2016 , 49 ( 5 ): 78 - 81 .

SATYANARAYANAN M . The emergence of edge computing [J ] . Computer , 2017 , 50 ( 1 ): 30 - 39 .

TAN K M , RAMACHANDARAMURTHY V K , YONG J Y . Integration of electric vehicles in smart grid:a review on vehicle to grid technologies and optimization techniques [J ] . Renewable and Sustainable Energy Reviews , 2016 , 53 : 720 - 732 .

ZHOU Z , HUANG Y , LU Y , et al . Lithium-ion battery remaining useful life prediction under grey theory framework [C ] // Prognostics and System Health Management Conference,August 24-27,2014,Hunan,China . Piscataway:IEEE Press , 2014 : 297 - 300 .

OKAY F Y , OZDEMIR S . A fog computing based smart grid model [C ] // 2016 International Symposium on Networks,Computers and Communications (ISNCC),May 11-13,2016,Yasmine Hammamet,Tunisia . Piscataway:IEEE Press , 2016 : 1 - 6 .

AMIN M . Smart grid [J ] . Public Utilities Fortnightly , 2015

KUMAR N , ZEADALLY S , RODRIGUES J J P C . Vehicular delay-tolerant networks for smart grid data management using mobile edge computing [J ] . IEEE Communications Magazine , 2016 , 54 ( 10 ): 60 - 66 .

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 ): 44 - 56 .

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 ): 44 - 56 .

TUBALLA M L , ABUNDO M L . A review of the development of smart grid technologies [J ] . Renewable and Sustainable Energy Reviews , 2016 , 59 : 710 - 725 .

SAPUTRO N , AKKAYA K . Investigation of smart meter data reporting strategies for optimized performance in smart grid AMI networks [J ] . IEEE Internet of Things Journal , 2017 , 4 ( 4 ): 894 - 904 .

FU S , LIU J , CHU X , et al . Toward a standard interface for cloud providers:the container as the narrow waist [J ] . IEEE Internet Computing , 2016 , 20 ( 2 ): 66 - 71 .

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