Modelling, simulation, and optimisation of agrivoltaic systems: a comprehensive review

Abstract

Agrivoltaic systems combine food production and solar energy conversion on the same land, offering a dual-use approach to address land use concerns in renewable energy development. One of the main research and market challenges for agrivoltaic systems is the ability to predict food and energy yields prior to installation. The photovoltaic modules reduce solar irradiation on the ground, altering the energy balance at the ground and crop levels, affecting thus evapotranspiration and photosynthesis. The photovoltaic modules also influence local rain distribution and wind patterns, creating a microclimate that impacts both crop production and photovoltaic efficiency. The need to evaluate these effects and their impact on crop growth before installation is underscored by the recent implementation of new standards, guidelines, and regulations governing agrivoltaic systems in various regions. This study provides a critical review of existing research with a focus on the modelling, simulation, and optimisation of agrivoltaic systems. It highlights recent advancements in simulating and optimising the design of agrivoltaic systems through integrated simulations of shading, microclimates, electrical performance, and agricultural productivity. This study highlights the critical role of optimised light distribution in enhancing both crop yields and electricity production within agrivoltaic systems. However, the diversity of modelling approaches from the PV and agricultural sectors, coupled with the absence of standardised benchmarks, complicates the selection of appropriate models for specific systems and conditions. Future research should prioritise the development of standardised benchmarks to enable consistent comparisons across models, facilitating a better understanding of trade-offs between computational efficiency, interpretability, and accuracy. Collaborative efforts, publicly available datasets, and benchmarking initiatives are essential for validating models across diverse agrivoltaic configurations and regions.