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Research on big data applications in Global Energy Interconnection

Research on big data applications in Global Energy Interconnection


This work was supported by National High-technology Research and Development Program of China (863 Program) (2015AA050203); the State Grid Science and Technology Project (5442DZ170019-P).


Abstract


Construction of Global Energy Interconnection (GEI) is regarded as an effective way to utilize clean energy and   it has been a hot research topic in recent years. As one of the enabling technologies for GEI, big data is accompanied   with the sharing, fusion and comprehensive application of energy related data all over the world. The paper analyzes the technology innovation direction of GEI and the advantages of big data technologies in supporting GEI development, and then gives some typical application scenarios to illustrate the application value of big data. Finally, the architecture for applying random matrix theory in GEI is presented.



Keywords

Global Energy Interconnection, Big data, Clean energy, Random matrix theory.


Introduction

As a systematic method to solve energy shortage, environmental pollution and climate change, Global Energy Interconnection (GEI) has attracted public attentions. GEI can be briefly defined as “ultra-high voltage power grid + ubiquitous smart grid + clean energy” and it aims to serve as a secure, reliable, efficient, and interactive platform  for optimized energy development and utilization. GEI can transfer renewables from “one pole and one belt” (arctic, equatorial) energy bases to load centers by inter- continentally connecting power networks in different time zones and seasons by ultra-high voltage transmission systems. It can also take advantages of large scale energy systems interconnection, including less load peak valley gap, less conservation, and higher reliability [1].

Big data will play an important role in enabling GEI. On one hand, the GEI involves hundreds of millions of measurements, monitoring and controlling devices/systems, which generate large amounts of data during energy production, transmission, transaction, and consumption. On the other hand, GEI also shows a strong demand for data mining. Due to high penetration of renewables and wide involvement of customers, GEI faces challenges of uncertainty and complexity, therefore, physical model- based methods cannot accommodate it. However, as a data-driven method, big data analytics is often effective. Through big data analysis, all stages of energy production, distribution, transformation and consumption can be predicted scientifically; decentralized and centralized coordination of energy management can be achieved; potential risks in every stage can be detected [2-6].

Random matrix theory (RTM) is regarded as a universal data analysis method in paper [7], which is proposed to apply in the analysis of power big data for the first time, and its validation is proved in [8].

This paper presents the direction of technological innovation of GEI and its demand for big data, and identifies the main application scenarios for big data analytics. It also analyzes itsapplication value, and finally summarizes the analytical framework of RMTapplied in GEI.


Biographies

Dongxia Zhang

received her bachelor and master degree at Taiyuan University of Technology, Taiyuan, in 1985 and 1992 respectively, and Ph.D. degree at Tsinghua University, Beijing, in 1999. She is senior engineer in China Electric Power Research Institute. Her research interests include power system analysis and planning, smart grid technology, big data application in power systems.


Robert Caiming Qiu

received his bachelor degree at Xidian Unversity , Xi’an, in 1987, master degree at University of Electronic Science and Technology of China, Chengdu, in 1990 and Ph.D. degree at New York University in 1995. He is IEEE fellow. He is currently professor in Tennessee Technological University (TTU), USA and Distinguished Professor in Shanghai Jiao Tong University, Shanghai, China.His research interests include wireless sensor technology, big data theory and applications.

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