This thesis investigates the integration of agrophotovoltaic (agri-PV) systems into apple and pear orchards, focusing on how orchard structure and PV array configuration influence light availability. Agri-PV offers a promising solution to improve land-use efficiency by combining food and energy production, while elevated PV modules can also protect high-value orchard crops from extreme weather. Given the light sensitivity of fruit production, accurate light modeling is essential to ensure agri-PV systems maintain both productivity and energy yield.

The main objective was to develop a flexible, modular 3D orchard model suitable for integration into a ray tracing-based light simulation framework. The study focuses on two tree-training systems compatible with agri-PV integration—Tall Spindle and Narrow Orchard System (NOS)—due to their narrow, vertically oriented canopies. Using PyVista, a customizable tree modeling framework was created, supporting seasonal development and adaptable to various training systems and species. Simulations were conducted under both open-field and agri-PV scenarios, with irradiance quantified on the canopy and PV modules for each system and array configuration.

The results showed that while total seasonal light availability was similar across systems in open-field conditions, vertical light distribution varied due to differences in canopy structure. Agri-PV simulations revealed a near-linear relationship between ground coverage ratio (GCR) and canopy light reduction, with narrow-row systems like NOS experiencing greater losses. PV array design also affected both total light availability and its vertical distribution

In conclusion, orchard geometry and PV design jointly influence light availability and distribution in agri-PV systems. Tailoring agri-PV layouts to specific orchard structures is therefore crucial, and the 3D orchard model developed in this thesis provides a valuable tool for identifying optimal design combinations.

With the rapid growth of Agri-photovoltaic (Agri-PV) systems, a spectral analysis that provides high-resolution data becomes necessary. For such a precise application, there is a need for a spectral irradiance sensor. However, for better precision instruments such as spectroradiometers that are available on the market are expensive. So, in light of this information, the thesis aimed to design and simulate a spectral irradiance sensor’s circuit. The goal was to obtain a PCB design for a high-resolution sensor, suitable for Agri-PV and to simulate the designed circuit to confirm the working theory on which the circuit was made. The circuit was designed on ALTIUM designer where the sensing unit and control unit were separated. The sensing unit consisted: photodiodes and amplifier circuits. The control unit consisted of: ADC and a microcontroller (Raspberry Pi) for signal conditioning and analysis. This circuit was then analyzed and validated through PSPICE OrCAD software. The designed PCB of the Sensing unit contains six photodiodes, which increases the resolution of the spectral sensor. These photodiodes could help in collecting data for a narrow wavelength band (say, approx 10nm width) with the help of bandpass filters. As the PCB was designed while keeping in mind the previous casing’s dimensions of the available PVMD sensor (so the new PCB design can fit on it). Hence, this study finds that it is possible to design a cost-effective spectral sensor circuit that is suitable for Agri-PV applications.

This thesis investigates the optimisation of fixed-tilt bifacial agricultural photovoltaic (agri-PV) orchard systems using a multi-objective approach, focusing on maximising both crop Photosynthetically Active Radiation (PAR) and PV radiation. Due to the complexity of modelling agri-PV systems, advanced light simulation tools like Radiance, a backward ray-tracing software, were integrated into the study. Although effective, this software introduces significant computational intensity and stochastic behavior.

The primary aims of this thesis were to develop an optimisation algorithm for such irradiation models and to optimise the system by maintaining separate objectives for crops and PV modules. This separation eliminates inter-connectivity between the conflicting metrics during the optimisation process. Additionally, a single-objective optimisation was conducted by summing the two conflicting objectives to assess the impact of this separation.

The research concluded that Bayesian optimisation with Gaussian Processes is most suitable due to its efficiency and ability to handle noise. Applied to an apple orchard case study in northern Italy, the algorithm demonstrated significant advantages over classical Radiance-based methods, achieving high accuracy in a fraction of the time. Results indicated that multi-objective optimisation offers more robust and informative solutions compared to single-objective optimisation.
In conclusion, this thesis enhances the understanding and optimisation of agri-PV systems, enabling quicker, more accurate analyses and providing practical solutions for farmers and other users.