Solar Photovoltaic system design with Digital-Twin approach and its challenges with possible mitigations: A comprehensive review
Abstract
The rapid growth of solar photovoltaic systems has exposed persistent challenges in their operation reliability, performance forecasting and integration with power systems. Therefore, accurate assessment of solar photovoltaic system behaviour and on-field reliability are required through comprehensive modelling to enable real-time mapping. Digital-twin technology is one such platform that offers a transformative solution by creating synchronised virtual counterparts that enable real-time monitoring, prediction, and optimisation across the photovoltaic lifecycle. This review work critically examines the evolution of digital-twin architectures and their applications in solar photovoltaic systems, including system design, performance monitoring, predictive maintenance, energy forecasting, and thermal optimisation. It highlights how hybrid physics-based modelling, artificial intelligence modelling, edge-cloud orchestration, and federated learning can transform current fragmented approaches into comprehensive self-immune environment. This paper introduces the concept of an ‘advanced digital twin’ for solar photovoltaics. Unlike the solar photovoltaic-digital twin surveys that dealt with system components, this work presents the first multi-layer benchmarking taxonomy that systematically compares each architectural layer of current photovoltaic-digital twin deployments. The findings emphasise that progress in photovoltaic-digital twin depends on integration, coordinating mature tools and standards into modular, interoperable, and lifecycle-aware infrastructures that transform solar photovoltaic systems into predictive, resilient, scalable, and self-optimising ecosystems.