Optimisation of Agrivoltaic Systems within the Water-Energy-Food Nexus

Sebastian Zainali, Silvia Ma Lu , Yuri Bellone, Pietro Elia Campana

Agrivoltaic (APV) systems, which co-locate photovoltaic (PV) panels with agricultural production, have emerged as a promising strategy to simultaneously address water, energy, and food sustainability challenges. However, the optimal design of such systems remains complex due to competing objectives, site-specific conditions, and increasingly stringent policy constraints. This study presents a multi-objective optimisation framework for APV systems design that integrates climatic variability, crop performance modelling, PV system behaviour, and national policy thresholds with a water-energy-food (WEF) nexus approach. Using a genetic algorithm (GA) as the optimisation technique, the model explores optimal configurations of three APV system types: vertical, one-axis tracking, and overhead fixed-tilt. The optimisation considers four design parameters including module tilt, azimuth orientation, row pitch, and system height. Simulations are carried out at three geographically diverse European locations: Sweden, Germany, and Italy, over a six-year crop rotation period. The framework incorporates constraints from Swedish subsidy requirements, German yield retention standards, and Italian guidelines. A composite WEF index enables flexible prioritisation among objectives and reveals strong trade-offs between energy conversion and crop productivity (correlation ≈ −0.99). The results demonstrate that combining national policies with recommended best practices can render APV deployment practically infeasible at the development stage if no accurate APV integrated models are available to clearly depict the impact of shading on microclimate and crop growth. The row pitch emerged as the most influential design variable, with optimal spacing between 5–10 meters depending on location and constraints. Furthermore, the land equivalent ratio (LER) for crops can vary by up to 10% depending solely on interannual weather variability.