# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV-DT Architecture  
Various architectures exist for DTs depending on the specific application. However, general architectures exist that can provide essential insight into DT development. [PERSON] et al. [67] proposed a three-level architecture for DTs consisting of symbiosis, evolution, and optimization stages. Symbiosis involves physical twin data capture, update, and optimization; the evolution stage involves predictions or value deduction, while the optimization stage involves the decision-making element that provides feedback to the physical entity. The specific architecture choice will depend on the application. Here, we consider a generalized architecture for three (3) main applications: solar cell development, solar cell/module manufacturing, and PV power estimation and fault detection (Figure 4). Note that fault detection and power estimation are often combined in the same workflow. PV design and installation are not included in this phase because the basic concept of a DT requires an existing physical counterpart. As such, a 'true' DT is only feasible when the physical counterpart is available. The final stage, where power estimation and fault detection are implemented, is also described as the Operation and Maintenance (O&M) phase. A solar cell development/prototyping DT was designed in [34]. The process developed starts with using three (3) sets of parameters: experimental, fitting, and fixed parameters. The aim is to train the DT simulation so that it can emulate experimental variations that arise due to various external and internal factors. Thus, the experimental values were from certain experiments that had been run previously. The fitting data include doping densities, carrier mobility lifetime, and more. These values are the same for each iteration step, change iteratively, and are identical for all simulated curves. The fixed parameters include the band gap profile or effective state density and are the same for all steps in the process. The process implemented was run iteratively until the error was below the prescribed tolerance levels. A one-dimensional optoelectronic model was created to continuously extract values of intrinsic parameters critical to developing an improved CuInGaSe2 solar cell prototype from physical tests conducted on the physical counterpart, such as time-resolved photoluminescence, dark and light I-V tests, and C-V tests. The developed DT can further be used to calculate a hypothetical device with specified properties. The authors of [38] implemented a DT for a series solar cell production line (SSCPL) machine. The monitoring and control were executed using feedback from the DT. The SSCPL is responsible for wafer slicing, welding, assembly, encapsulation, and storage of final solar cell products. Thus, the DT replicated the whole physical process using various advanced algorithms such as the dimension reduction data (PCA/Relief), quality measurement data (QLF-SNR), online prediction data (SVMs-DS), and optimal quality loss distribution data from AGA-ALC (decision data). Furthermore, the DT replicated every stage of the machining process, such as when a component is free and available, starting, in a waiting state, in a configuration state, in a failure state, and in a maintenance state. All these were necessary to ensure a sync in the DT process stages and those of the physical counterpart. In Figure 5, we show a generalized DT architecture applicable to PV module power estimation, yield forecasting, fault detection, and monitoring. This DT highlights the sub-modules required for a functional DT. Variations of this architecture may depend on the specific purpose of the DT and system type (standalone PV system, hybrid i.e., energy storage or wind inclusive, residential, and large scale/utility). Figure 5 shows the flow of data from the physical counterpart, the processing implemented in the digital  
Figure 4: DTs in PV lifecycle. counterpart, and the feedback mechanism.