Situation Awareness of Energy Internet of Things in Smart City Based on Digital Twin: From Digitization to Informatization

Abstract

Rapid growth of diversity, uncertainty, and coupling effect of units in modern energy systems jointly challenge the traditional model-based situation awareness (SA) in energy internet of thing (EIoT). This work explores digital twin of EIoT (EIoT-DT), and then provides a novel data-driven SA paradigm, named DT-SA, as a promising alternative. Based on the combination of the latest data technologies and machine learning algorithms, DT-SA transfers those stubborn SA challenges to digital space, and then addresses them by building a domain-specific and data-friendly DT model upon massive data. The established model can be quantitatively tested via iterative virtual-real interaction, and thus be evaluated and updated through closed-loop feedback to improve its performance in the physical world. To this end, some engineering and scientific problems are raised: a) virtual-real interaction mechanism relevant to resource flow and data flow; b) unified modelling and analysis of heterogeneous spatial-temporal data; c) DT configuration and evolution; and d) domain-specific DT-SA characterization. To solve these problems, cloud-edge-terminal configuration, big data analytics (BDA), digital twin, and SA indicator systems are studied, respectively. Then, random matrix theory (RMT), overarching DT-SA framework are designed as a roadmap. Besides, some potential applications and under-going projects on the terminal, edge, or cloud are discussed, e.g., condition assessment of equipment, digital monitoring and diagnosis of power grid network, and EIoT construction in the smart city. Lastly, some perspectives and recommendations are proposed in conclusion for future researches. This research can be regarded as an efficient handbook for both energy engineering and data science, which may benefit enterprise digitization, smart city, etc.