Thermal and performance losses in FPV systems are quantified using computational fluid dynamics (CFD) models to evaluate cooling mechanisms like convection and evaporation. Recommendations will integrate these findings into widely used tools like PVsyst. Additionally, machine learning models will assess soiling, degradation, and sea-state impacts to refine performance predictions.
By ensuring high-quality data collection from various FPV sites, including real-time monitoring and production data, the project validates models and establishes reliable benchmarks. Data from 24 sites across different partners, combined with smaller experimental systems, form the basis for robust analyses.
Digital twins play a pivotal role, combining loss mechanisms and simulations to explore interdependencies and design improvements. At two complexity levels, these twins will calibrate FPV plant performance in real-time. The project also evaluates FPV-specific cell and module technologies to optimize field efficiency, reduce costs, and improve reliability, with potential manufacturing adaptations identified.
Through these approaches, SuRE aims to establish FPV as a reliable, efficient, and sustainable renewable energy source.
