# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 1 Introduction
[19]: _\"The DT consists of a virtual representation of a production system that is able to run on different simulation disciplines that is characterized by the synchronization between the virtual and real system, thanks to sensed data and connected smart devices, mathematical models and real time data elaboration\"_. Most definitions, such as this one, are tailored to _production systems_, which hints at the origin of DTs in the manufacturing domain. For a more general understanding, we can break down the definition to the term physical object [20] or physical entity [21]. [PERSON] et al. [21] define a physical entity as an abstraction of a \"thing\" persisting in the real world which has to be mirrored or twinned in the virtual world. In this framing, the definition by [PERSON] et al. [17] is suited for industrial energy systems, where the physical entity of a considered DT can be a single process unit or even part of that unit but also a whole energy system including boundary conditions (e.g., external energy supply systems/grids). Therefore, this is the definition that we adhere to. It is important to note the difference in the level of data integration between mere digital models of a physical entity and a full DT, which was most prominently discussed by [PERSON] et al. [19]. While DTs are using digital models to dynamically simulate the physical entity and thus can build on the same principles, their key feature is bidirectional automated data exchange, i.e., synchronization with the real world. This is where the technical complexity of DT modeling arises. Hence, DT frameworks and practical platforms are needed to allow for the strict synchronization of digital models with their physical entities and to enable additional functionalities compared to a mere model. Although the number and maturity of DT concepts is increasing, there is still no uniform platform available for the practical implementation of a DT [18]. Most proposals focus on the conceptual side, maintaining a high level of abstraction and leaving important implementation details unaddressed [22]. Concrete DT implementations, on the other hand, are mostly realized with a specific application in mind without any architectural template [21] or only offer a limited set of services [16], hence not reaching the full potential. Thus, there is still a significant gap in DT research regarding how to offer a higher number of services in the same environment to support complex decision making [16]. Missing architectural guidelines result in application-specific solutions which are barely reusable, hence increasing development time and maintenance costs [22]. After its origin in the aerospace industry [15; 23], the DT was widely announced following Grieves' Whitepaper [14] in 2014 [24]. Only then did the DT topic find its way into other sectors, such as automotive, oil and gas [25], and healthcare and medicine [26] and was driven forward massively in (discrete) manufacturing, which is apparent by the distribution of DT applications analyzed in [PERSON] et al. [12], [PERSON] et al. [17], and also [PERSON] et al. [13]. In recent years, the DT was also explored in the context of chemical process engineering [27] and the food industry [28]. In the domain of energy systems, DTs are notably represented in the domain of electric power systems [29] and especially in the context of smart grids [30] and battery management [31]. Other examples include DTs for decision making in energy system design [32] and, during operation, the application of DTs for wind turbines [33] and for scheduling [34] and state estimation [35], to name just a few. Only recently, the transfer of the DT concept to thermal IES has begun [36; 37]. For a complete overview of existing DT research and industrial application, we refer interested readers to the current literature review by [PERSON] et al. [18].