# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 2 State of the Art
### Digitalization Developments and Opportunities in Industrial Energy Systems
The operational optimization (A6) and control of IES generally aims to decide on optimal operating points and scheduling in order to minimize energy consumption or overall costs. We consider the leveraging and marketing of demand flexibility also within this category, although it is sometimes known as demand-side management, since it is generally based on very similar methods and merely considers different operational variables, constraints and external information sources. [PERSON] et al. [41] derived the benefits of such digital applications from the literature, which were allocated into six clusters: (1) system stability, (2) environmental protection, (3) energy demand reduction, (4) revenue increase, (5) cost reduction and (6) customer expectations. These benefits are based on the potential of individual applications, i.e., to automate and improve efficiency in processes and optimize the operation and maintenance of systems but also on digitalization's potential to create interacting networks, increase transparency and improve convenience [41]. While some of these categories clearly show some correlation with each other, cost reduction is inherently covered in all of them. [PERSON] et al. [11] evaluated the main driving benefits for DT adoption in the energy sector from the literature as energy efficiency, profit, decarbonization, throughput, quality and safety. To enable individual digital applications and thus leverage the benefits ascribed to them, various methodical approaches were already successfully developed. However, although many of these solutions have been deployed in all energy value stream steps, the value stream itself has remained mostly unchanged [60]. A major problem today is that all these solutions are usually considered individually. By integrating them into a collaborative platform, their impact could be much more significant, and future development and software maintenance effort could be reduced. The DT is considered as such a platform that can host a large number of services in a single environment [16]. These services can either provide specific application purposes on their own [61] or can be loosely coupled with other small software services to build service-oriented applications [22]. This microservice architectural style gains increasing popularity in software development due to its improved scalability and maintainability [62]. \begin{table}
\begin{tabular}{l l} \hline \hline
**ID** & **Application** \\ \hline A1 & Condition monitoring \\ A2 & Anomaly/deviation detection \\ A3 & Fault classification and analysis \\ A4 & Predictive maintenance \\ A5 & Forecasting \\ A6 & Operational optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Most relevant technical categories of digital applications considered for value creation during operation of industrial energy systems. For an in-depth review, we refer to [11; 41; 51]. The most common services and features of DTs in general have been reviewed by a number of researchers. [PERSON] et al. [61] listed nine main services of DTs in the production sector, while [PERSON] et al. [16] grouped these in six categories; however, these are specifically tailored to manufacturing systems. [PERSON] et al. [51] list the most frequent goals and applications for DTs in the energy sector, and [PERSON] et al. [63] identified six groups of functional services: simulation services, monitoring services, diagnosis services, prediction services, control services, and reconfiguration services.