Preliminary Exploration on Digital Twin for Power Systems: Challenges, Framework, and Applications

TL;DR

This paper explores the emerging use of digital twins in power systems, discussing their framework, challenges, and applications, and highlights their potential to enhance decision-making and situational awareness in smart grids.

Contribution

It provides the first comprehensive exploration of digital twins in power systems, integrating recent technologies and proposing a framework for future research and applications.

Findings

Digital twins enable real-time monitoring and virtual testing of power grids.

Integration of IoT, AI, and big data enhances digital twin capabilities.

Application scenarios demonstrate potential for improved grid management.

Abstract

Digital twin (DT) is one of the most promising enabling technologies for realizing smart grids. Characterized by seamless and active---data-driven, real-time, and closed-loop---integration between digital and physical spaces, a DT is much more than a blueprint, simulation tool, or cyber-physical system (CPS). Numerous state-of-the-art technologies such as internet of things (IoT), 5G, big data, and artificial intelligence (AI) serve as a basis for DT. DT for power systems aims at situation awareness and virtual test to assist the decision-making on power grid operation and management under normal or urgent conditions. This paper, from both science paradigms and engineering practice, outlines the backgrounds, challenges, framework, tools, and possible directions of DT as a preliminary exploration. To our best knowledge, it is also the first exploration on DT in the context of power systems. Starting from the fundamental and most frequently used power flow (PF) analysis, some typical application scenarios are presented. Our work is expected to contribute some novel discoveries, as well as some high-dimensional analytics, to the engineering community. Besides, the connection of DT with big data analytics and AI may has deep impact on data science.