In the context of increasing global energy demand and climate change concerns, the integration of renewable energy sources with advanced modelling technologies has become essential for achieving sustainable and efficient energy systems. Solar energy, despite its potential, continues to face challenges related to performance variability, limited real-time insights, and reactive maintenance. To overcome these barriers, this work presents a Digital Twin framework aimed at optimizing solar-integrated energy systems through real-time monitoring, predictive analytics, and adaptive control.
This work presents a Digital Twin framework designed to address the challenges of designing, operating, maintaining, and estimating renewable energy systems, specifically solar power, based on dynamic load demand. The framework enables the real-time forecasting and prediction of energy outputs, ensuring systems operate efficiently and maintain peak performance across diverse conditions.
The proposed methodology mirrors the physical system using real-time data inputs, environmental conditions, and physics-based models to create a high-fidelity virtual replica. This allows for dynamic analysis of energy flows, load forecasting, system performance prediction, and scenario testing to optimize design and operational strategies. By integrating predictive analytics, the Digital Twin adapts to changing conditions, enabling proactive maintenance, fault detection, and system calibration to meet future load demands.
Experimental validation demonstrates that the framework improves system efficiency, adaptability, and reliability, with scalable applications for both centralized and decentralized energy systems. Additionally, its integration with cloud-based platforms and IoT technologies enhances real-time monitoring, allowing for continuous optimization and data-driven decision-making. This Digital Twin approach provides an intelligent, data-driven solution for the renewable energy sector, facilitating sustainable, resilient, and efficient energy infrastructures that can reliably meet evolving load demands while optimizing performance throughout their lifecycle.