# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
###### Abstract  
The rapid acceptance of solar photovoltaic (PV) energy across various countries has created a pressing need for more coordinated approaches to the sustainable monitoring and maintenance of these widely distributed installations. To address this challenge, several digitization architectures have been proposed, with one of the most recently applied being the digital twin (DT) system architecture. DTs have proven effective in predictive maintenance, rapid prototyping, efficient manufacturing, and reliable system monitoring. However, while the DT concept is well established in fields like wind energy conversion and monitoring, its scope of implementation in PV remains quite limited. Additionally, the recent increased adoption of autonomous platforms, particularly robotics, has expanded the scope of PV management and revealed gaps in real-time monitoring needs. DT platforms can be redesigned to ease such applications and enable integration into the broader energy network. This work provides a system-level overview of current trends, challenges, and future opportunities for DTs within renewable energy systems, focusing on PV systems. It also highlights how advances in artificial intelligence (AI), the internet-of-Things (IoT), and autonomous systems can be leveraged to create a digitally connected energy infrastructure that supports sustainable energy supply and maintenance. digital twin; solar photovoltaics; artificial intelligence; autonomous systems; renewable energy; sustainable infrastructure +
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as a virtual (digital) replica of a physical counterpart that allows automatic data exchange between the physical and digital versions in a bidirectional manner. A frequent misconception of DT terminology is that it can apply to any digital replica or software model of a physical entity. As described in [5], a digital replica of an asset could also simply be a digital model or a digital shadow. A digital model allows manual data exchange between the digital version and physical asset while a digital shadow allows automated data flow from the physical asset to the digital version while data flow is manual from the digital model to the physical model or vice versa. For a DT, the data flow must be automated, real-time, and bidirectional since the parameters (attributes) of the digital version are expected to change as those of the physical asset vary. Furthermore, for digital models and digital shadows, the manual data exchange only occurs when necessary, or may not take place at all [3]. DTs have found use cases in several industries, such as manufacturing [6], construction [7], and energy [8]. It is estimated that the global DT market size will be about USD 16.75B in 2023 with a compound annual growth rate (CAGR) of 35.7% [9]. For PV systems, several continuously evolving challenges require sustainable custom solutions, such as real-time monitoring and prediction of energy yield, early detection of faults, historical maintenance logs, panel cleaning and imaging procedures, battery storage charging, and more. Due to their real-time monitoring capability, DTs have been proposed as suitable solutions for enhanced management processes. In this work, we investigate DT implementations within the energy sector, particularly for PV systems. We analyze various works that have been conducted in academia and industry from the viewpoint of digital models, shadows, and twins. We examine the current implementation trends, challenges, and untapped potential. As part of the contribution of this work, we examine DT frameworks for PV at different stages of cell research and development, cell/module manufacturing, and the post-construction phase which is often neglected in the literature. Furthermore, we discuss the interconnected use of autonomous platforms (robots and unmanned aerial vehicles-UAVs) to project into the future of digitally interconnected energy systems.