# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 2 Digital Replicas in Energy Systems
This is considered as a digital shadow (DS). An example is the automated extraction of energy yield data from the physical installation to the digital model in inverter software like SolarEdge [19]. At this integration level, the overall or even individual solar panel yield can be observed but none of the physical components or processes can be controlled or adjusted. Further advancement involving comprehensive simulation and predictive models that offer recommendations for maintaining or charging/discharging associated battery systems leads to a DT. For different energy systems, the digital counterpart can be a digital replica of an Individual component [20] or system [21] depending on the system's complexity, requirements, or process [22; 23]. Component and system-level DTs focus on the digital representation of physical objects and their interaction. At the same time, a process DT is differentiated by its use to model specific or interconnected processes within a large system. These can be developed for manufacturing processes, chemical reactions, supply chain logistics, and more. A brief overview of the current application of DTs in the energy industry is presented in Table 1.  
Table 1 shows various applications of DTs within energy systems and the different levels of integration. The applications are extensive, and further analysis can be performed. However, for this work, we focus on applications within the scope of PV to provide an in-depth analysis of current trends and future applications. Figure 2 also provides data on the relevance of DTs as observed in the existing academic literature. Using the 'Web of Science' and 'Scopus' databases, we extracted literature seeking to address aspects of various DTs. The search using the keywords \"Digital twin and energy\" was conducted in both Web of Science and Scopus in the \"Title, Abstract, and Keywords\". The publication count was 2504 when limited to only articles, journals, and early access. Figure 2a shows the geographic distribution of published works in this field, with 'deep red' indicating very high intensity and 'white' indicating the absence of verified literature. This highlights the progress in research around DT implementation. In Figure 2b, we examine the literature that speaks explicitly to energy systems and other fields of study. It is observed that building energy management and smart energy systems have the highest proportion. At the same time, a significant amount of the studies--64%--may not be closely associated with energy management studies. In Figure 2c, we estimate the publication count, and as may be expected, there has been a significant increase over the years, thus showing the relevance of DTs and this study. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference Energy Type** & **Application** & **DT Type** & **Level of Abstraction** \\ \hline \multirow{2}{*}{[23]} & \multirow{2}{*}{Wind} & Wind farm flow system for accurate & \multirow{2}{*}{Process} & \multirow{2}{*}{Twin} \\  & & wind flow information & & \\ \multirow{2}{*}{[24]} & \multirow{2}{*}{Electrical} & More accurate estimation of transmission & \multirow{2}{*}{Component} & \multirow{2}{*}{Shadow} \\  & & & line temperature and compliance & \\ \multirow{2}{*}{[20]} & \multirow{2}{*}{Hydro-power} & Water flow system for hydro-turbines & \multirow{2}{*}{Process} & \multirow{2}{*}{Model} \\  & & & & System \\ \multirow{2}{*}{[25]} & \multirow{2}{*}{Wind} & \multirow{2}{*}{Construction of wind farm} & \multirow{2}{*}{System} & \multirow{2}{*}{Model} \\  & & Enhancement of aerobic digestion and & & \\ \multirow{2}{*}{[26]} & \multirow{2}{*}{Bioenergy} & optimization of methane content through & \multirow{2}{*}{Process} & \multirow{2}{*}{Twin} \\  & & oxygen rate adjustment & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: DTs in the energy industry.