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.