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

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-loopintegration 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.

Digital Twins for Enhancing Efficiency and Assuring Safety in Renewable Energy Systems: A Systematic Literature Review

“…Some researchers [32,59,68] have discussed the necessity of developing a DT for renewable energy generators. He and Ai [69] proposed a framework that emphasizes the importance of leveraging technologies such as big data, artificial intelligence, 5G, cloud computing, and IoT to enhance the capabilities of the power system digital twin (PSDT). By integrating data-driven and model-based tools, the PSDT aims to improve system understanding, decision-making, and overall grid management in the power sector.…”

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

“…[23], [30]- [33] [24], [34]- [38] 3 an accurate digital representation of a physical asset, offering monitoring capabilities throughout the whole lifetime of its real twin.…”

“…Besides objective function optimisation, another application of ML within DTs is to make predictions about the future behaviour of the physical asset. In this context, Artificial Neural Networks (ANNs) have been used in [30] to predict future samples of the active power component based on historical time series data. However, ML models, and in particular DL methods, are generally perceived as black-boxes [81].…”

“…Physics-based modeling, Mixture of Experts and Gaussian Processes were used to predict future machine states by analysing multiple time-scale time series data. Electrical Engineering ANN [30] Artificial Neural Networks are used to predict the time series samples of the active power component sensor based on historical data.…”

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

“…The main questions that are addressed with power grid digital twins aim to understand and uncover what changes in the grid functioning when the grid structure and dynamics change, i.e., when nodes and/or edges are removed or new ones are added, or when the features characterizing such nodes and edges are changed in terms of their size and nature or type (see above). As known [85,86], the consequences of such changes, even when very localized and of small proportions, such as the removal of one single distribution line, can lead to broad outages, as in Germany in 2003 [87] and in India [88,89].…”