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 .
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 , which is proposed to apply in the analysis of power big data for the first time, and its validation is proved in .
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.