Bifacial photovoltaic modules capture solar radiation from both front and rear surfaces, offering significant efficiency gains over traditional monofacial designs. However, predicting their real-world performance requires sophisticated modeling that accounts for optical reflection, thermal dynamics, and electrical behavior simultaneously—a capability that has been limited in existing analysis tools.
Researchers have now developed and validated a comprehensive coupled multiphysics model that integrates all three physical domains. The optical component uses view factor theory to calculate rear-side irradiance, accounting for ground albedo and module geometry. The thermal layer employs a five-layer energy balance equation to predict module temperature, while electrical performance is simulated using a single-diode equivalent circuit with parameters optimized through hybrid genetic and interior-point algorithms.
Testing against a 580 W bifacial module in Tehran achieved high accuracy, with prediction errors of 4.61% for power output and 0.93% for temperature. Parametric studies reveal critical performance drivers: increasing wind speed from 1 to 6 m/s boosts bifacial power by 4.9% through enhanced cooling, while rear irradiance increases from 100 to 600 W/m² raise bifacial power by 37.7%.
Installation geometry strongly influences bifacial gains. At a 30° tilt angle—optimal for annual energy yield—the bifacial advantage grows as rear irradiance increases. Vertical installation delivers a 33.5% bifacial gain relative to monofacial reference, driven by enhanced ground reflection capture. Ground albedo emerges as a critical design variable; lighter surfaces substantially amplify rear irradiance collection.
These findings provide solar engineers with quantitative guidance for site assessment and system design. However, the validation is limited to a single outdoor location and restricted testing period. Researchers emphasize that further validation across different climates, seasons, and mounting configurations is essential before generalizing performance predictions for global deployment.
