Digital Tool for Optimizing PV Module Transparency [BRITE / CRES]

BRITE faces a significant industrial challenge in optimizing the transparency of photovoltaic (PV) modules used in agrivoltaic systems. Currently, transparency levels are selected empirically, relying on manual trial-and-error methods that balance light transmission for crops with energy yield. This approach is time-consuming, non-standardized, and may lead to suboptimal performance. The challenge is to develop a systematic, data-driven digital tool that automatically calculates the optimal PV transparency based on key parameters such as climate zone, crop type, and local solar conditions. Ideally, the tool would evolve into an AI-enabled platform that integrates real-time data from scientific databases, publications, and online weather sources, providing immediate and reliable design recommendations. Such a solution would improve both agricultural productivity and solar energy generation while enhancing design consistency across different sites and crop types.

Technology Readiness Level (TRL):8–9 BRITEʼs PV modules are already on the market. The proposed optimization tool would enhance replicability, taking the technology toward full industrial maturity.

Expected Outcomes: Improved crop and energy yields through data-driven transparency design, Broader applicability of BRITEʼs PV modules across diverse crops and regions, Reduced design time and development costs, Increased competitiveness and sales through product differentiation

Impact on Operations: Without a standardized optimization tool, module design remains partially empirical, limiting scalability and market expansion. Automating transparency design would unlock new application areas (e.g., different crops, building-integrated PV) and strengthen BRITEʼs market position by offering customized, high-performance agrivoltaic solutions.

Current State / Next Actions: Transparency optimization is currently done manually using shading simulations and empirical adjustments. While effective for single-crop studies, the process lacks automation and generalization.Research publications confirm the importance of transparency optimization but no commercial digital tools exist yet.

Next Steps / Collaboration Opportunities: Software and methodology development, AI model training, and joint R&D proposals for EU or national funding programs.