# 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  
Technical developments such as Internet of Things (IoT) technologies and the growing data acquisition in energy systems have resulted in new challenges and opportunities for energy systems [40]. Digital innovations have the potential to trigger significant changes in the energy sector in the near future [41]. For example, the increasing maturity of machine learning approaches enables applications to improve the accuracy of demand, generation, and price forecasting [40]. Various other solutions have been proposed, such as intelligent energy management [42], demand response [43], operational optimization [44] and design optimization [45], fault prediction [46] as well as preventive and predictive maintenance for energy efficiency [47] and to extend the lifespan of machinery [48]. In the area of battery management, IoT and data science technologies enable numerous solutions to optimize battery manufacturing, operation and re-utilization [49]. Existing DT concepts and implementations in the energy sector aim to provide at least one specific solution. In a recent study by [PERSON] et al. [50], targeting energy neighborhood applications emphasized the use of DT in energy storage use cases, which are equally important in IES. The estimation of stage of charge and aging state of storage devices as well as the cloud-based interconnection of multiple units to enhance computational abilities and overall operation management were highlighted. A recent summary of the modest amount of DT development in the area of power generation was given by [PERSON] et al. [37], including electricity generation, power distribution, the renewable and nuclear power industry, and the energy vehicle and storage sector. They concluded that while the energy industry is actively pursuing the tremendous opportunities of DT, most articles did not include details on the comprehensiveness of their DTs or details on the used models and enabling technologies. Furthermore, the scope of implementations is mostly very limited to specific applications scenarios. [PERSON] et al. [41] provided a structured overview of digital applications in the German energy sector, and [PERSON] et al. [51] listed the most frequent application scenarios for DTs in smart energy systems based on a systematic literature review. Furthermore, [PERSON] et al. [11] recently derived a structured list of DT applications in the process and energy industry from the literature. Grouped into three lifecycle phases, these included [11]: virtual testing [52] and design optimization [53] in the Desing phase, process optimization [54], prediction [55], monitoring [56] and training [57] as well as production control [58] in the processing phase and fault detection and diagnosis [59] in the service phase. Based on these studies, we deduced a list of application categories which we consider most relevant for industrial energy systems from a technical perspective (see Table 1). The condition monitoring (A1) of physical components by analyzing measurement data is crucial to guarantee the optimal and safe operation of energy systems [33]. Furthermore, condition monitoring via models and \"soft sensors\" can facilitate this process, especially in harsh environments and for quantities that cannot be directly measured. On top of monitoring, anomaly or deviation detection (A2) aims to detect deviations between the expected and observed behavior of physical components, which is often via the use of simulation models. The application of fault classification (A3) is typically designated to identify the type and cause of detected anomalies or errors and sometimes also to predict future fault scenarios. Predictive maintenance (A4) aims to predict and extend the remaining useful lifetime of machinery to determine an optimal maintenance schedule. Forecasting (A5) of energy demand, prices and environmental conditions becomes increasingly relevant and also more feasible by using historical and external data and advanced analytic methods.