Modelling and analysis of V-shaped bifacial PV systems for agrivoltaic applications: A Python-based approach for energy optimization

Agrivoltaic systems integrate photovoltaic (PV) energy production with agricultural activities, addressing the critical challenges of land use optimization and sustainable energy generation in the context of climate changes and food security. These systems are pivotal in offering a promising solution in mitigating the environmental and social impacts of utility-scale PV installations, such as habitat disruption and competition with agricultural land. This study evaluates a patented V-shaped bifacial photovoltaic system with a single-axis solar tracking, designed to optimize energy capture but also to minimize shading effects on crops like vineyards. A custom Python-based algorithm using PVlib was developed to simulate the performance of the system, accounting for mutual shading, multiple solar radiation reflections, and dynamic tilt adjustments. Simulations conducted for Palermo, Italy, revealed that the system collects 5.2 % less solar irradiation than traditional side-by-side configurations but achieves an annual energy output of 2089.3 kWh per pair of panels, along with 24 % reduction in land use. These results highlight the system capability to optimize spatial efficiency while maintaining high energy production. The novelty of this work lies in its tailored simulation approach, addressing the unique geometry and operational dynamics of the V-shaped configuration, and its potential adaptability to diverse agrivoltaics scenarios. Unlike existing tools and methodologies in the literature, this work introduces a customized Python-based model specifically designed to analyse the performance of this innovative structure, which is of recent conception and lacks precedent in both academic studies and commercial software solutions. By advancing the methodological framework for integrating renewable energy with agriculture, this study contribute to the broader goals of sustainable development and climate resilience.