A systematic literature review of digital twin research for photovoltaic systems: Trends, challenges, and opportunities

Abstract

The accelerating deployment of photovoltaic (PV) systems intensifies the urgency to address various challenges in their performance prediction, operation and maintenance, and long-term reliability. Digital Twin (DT) technology – leveraging advancements in Industry 4.0 – offers great potential to tackle such challenges, by serving a wide range of PV applications and use cases. Nevertheless, the adoption of Digital Twins for PV systems (PVDTs) is still in its early stages, with limited published research work in this area. This paper presents a systematic literature review (SLR) of 61 peer-reviewed PVDT studies, aiming to map recent research trends, identify gaps, and provide recommendations guided by the review results. The works presented in the reviewed articles were categorized based on predefined review criteria, and were examined against a set of proposed PVDT eligibility criteria, stemming from commonly accepted generalized DT definitions and taxonomies. The review reveals that most reported implementations lack essential features, mainly bidirectional data flows and self-adaptability, with only 3.3 % of papers meeting all the eligibility criteria. Key identified trends include a dominance of data-driven models for power prediction, and limited utilization for life cycle assessments and design optimizations. Based on the review findings, the paper further introduces a general DT taxonomy tailored to PV applications and guided by the identified trends and gaps. This study emphasizes the need for unified and standardized PVDT definitions, comprehensive multi-domain modelling approaches, and integration of sustainability metrics to guide future research and industrial adoption.