Global warming represents the most significant threat to humankind, making the need for renewable energy more crucial than ever. However, in densely populated areas near the coast, electricity production faces competition from various sectors such as agriculture, housing, and tourism. To address this challenge, one viable solution is to explore offshore electricity production.

Building upon this context, this research delves into investigating the wave-induced effect on power mismatch losses along a PV string in offshore floating photovoltaic (OFPV) systems. OFPV offers a promising solution for generating electricity in unused marine areas, complementing offshore wind energy. Although OFPV holds great potential, our understanding of its complexities remains limited, particularly regarding the impact of wave-induced power mismatch losses. To bridge this knowledge gap, a comprehensive approach is taken. A floating structure is modeled using the Bernoulli-Euler beam theory, while the fluid domain is analyzed using potential flow/linear wave theory. Structural behavior is examined in the frequency domain through the application of a FEM with the package Gridap in Julia. The wave amplitude spectra are determined using the Jonswap sea spectrum, with consideration given to four distinct sea states based on the Douglas sea scale: slight, moderate, rough and very rough. The optoelectrical modeling is conducted in pvlib in Python.

The results reveal that monthly energy losses due to power mismatch are negligible during summer months for all sea states studied. However, in winter months, monthly energy losses exceed 1%, with daily losses reaching up to 6%. Additionally, the orientation of the PV string is identified as a crucial parameter for minimizing losses. Finally, the findings indicate that using either a thick structure with a stiff and dense or a thin structure with a flexible and lightweight material can help reduce energy losses caused by power mismatch.

Photovoltaic technology has become one of the leading renewable alternatives for energy production. One main factor in the diminished performance of a photovoltaic module is partial shading, due to the resulting mismatch conditions in the module. However, it is difficult to understand how a specific PV module reacts to shading in comparison with others, as currently the ability of a module to withstand shading is usually expressed in vague qualitative terms on its datasheet. In this work, the development of a shading tolerability calculator in MATLAB was completed. This tool can relatively easily and quickly calculate the shading tolerability of a module as a numeric parameter (ST), which can then be used for characterization and comparison purposes.

First a MATLAB based model to simulate the IV characteristics of a PV module under different conditions (including partial shading) was developed. The model was developed at a cell level, and was translated to a module level by taking the series connection of cells into account, and modeling the impacts of reverse bias and bypass diodes operation. Validation with experimental data showed errors at Pmpp remained below 4.5%.

Next, the shading scenarios to be considered were defined and developed. The objective was to determine Pmpp of a given PV module under all possible shading scenarios, using the IV simulation model developed. The possible shading scenarios were based on a PV module split into 12 equal sections, and considering two irradiance levels: 100 W/m2 for shaded sections, and 1000 W/m2 for unshaded. To improve the speed of the model, which was an important aim within this project, the existence of equivalent scenarios based on the symmetry of the module was utilised. The Pmpp value was only simulated once for every unique scenario, greatly reducing the required number of simulations and simulation time.

Based on the above, the development of a calculator for the shading tolerability parameter of a PV module was accomplished. The ST values for more than 40 PV modules were calculated, giving ST% values ranging between 22% and 29%. Correlations between different module parameters were explored to see their impact on ST. One main result seen was the impact of bypass diodes on ST, specifically the considerable positive effect of a higher number of bypass diodes. Another was the positive correlation between temperature coefficient of open circuit voltage and ST.

Finally, a case study for the calculation of ST for a half-cell butterfly module was implemented. This involved modeling parallel connections in PV modules, as well as updating the IV simulation model to include this new type of PV module configuration. The ST values for two half-cell butterfly modules were calculated, giving ST% values of around 42%. This was significantly higher than those calculated for the conventional modules, highlighting the improved shading tolerance of half-cell butterfly modules. The adaptability of this model to be able to calculate the shading tolerability of any type of configuration of PV module was also demonstrated through this case study, paving the way for future research.

The growing global energy demand and correlated rise in carbon emissions are increasing the need of renewable energy sources. This spread requires land to be occupied, competing with other activities such as agriculture and residency. This project can help the spread of photovoltaic (PV) technologies in an environment still little explored: the water. Offshore floating photovoltaic (OFPV) gains increasing attention in research due to a substantial reduction in land occupancy and a lower operating temperature. Therefore, this study aims to evaluate the power output of a OFPV system located in the North Sea considering the effect of the waves on tilt and azimuth of PV modules.

First, the JONSWAP spectrum theory was employed to simulate the sea surface. Then, the interaction between the waves and the floater was modelled to obtain the orientation of the PV modules. The system energy yield was simulated through the PVMD Toolbox, a physics-based tool developed by the PhotoVoltaic Materials Devices Group (PVMD Group) at Delft University of Technology. Finally, experimental analysis was conducted on a commercial string inverter emulating the DC power output from the OFPV modelled plant.

The waves generally cause lower irradiance hitting PV modules. However, fluctuations do not always have a negative influence. For example, the research found that with a calm sea, a system under the effect of waves produces 1% more than an offshore stationary 0° tilt plant. Nevertheless, the variable PV orientation scenario shows substantial losses for higher sea agitation states compared to the 0° tilt situation. Over a year, an OFPV under waves effect loses 0.84% and 17.97% of the production compared to stationary 0° and optimal installation tilt, respectively. From the laboratory activity, it appeared that with the same irradiance, the oscillations have a negative impact on the efficiency, especially that of the maximum power point tracking (MPPT) block, which reaches -3.2% with rough sea.

In the end, we can conclude that the power output losses are not as dramatic as expected and that the development of OFPV technology will probably depend on future costs for offshore installations and the competition for inland areas.

Solar energy is an abundant, scalable, and clean source of energy. With an exponential drop in prices of PV modules, more and more rooftop photovoltaic (PV) systems are being installed worldwide. Since these small-scale PV systems do not use expensive sensors, it is difficult to detect malfunctions for these systems. This could lead to lower energy generation along with financial losses for the owners. Thus, a method for PV yield monitoring is developed for early and remote fault detection. This method does not use the conventional analytical approach as it depends on inaccurately extrapolated weather data. Instead, the proposed method uses data from similar or neighbouring or peer PV systems for estimating the expected energy generation. By comparing the expected energy generation with actual energy generation, a faulty system can be flagged. In this project, information from about 12000 PV systems is used, which includes system design information such as location, number of panels, panel orientation, etc. along with the historical daily energy generation for periods ranging from two months to up to seven years per system.

In this thesis, a machine learning model was developed for predicting energy yields, which uses a Genetic Algorithm (GA) for optimization. This model splits the available data into system design, system location and system yield data. Thus, the model uses these as criteria for finding PV systems similar to the monitored system. Once good peer systems are located, system yield data of those systems are used for estimating the expected energy yields of the monitored system. The three criteria used by the model do not have equal influence on finding good peers, thus, the model had to be trained or optimization was done using the training data. Post optimization, the relative influence of system design: system yield: system location was found to be 0.125:0.875:0 with on average 16 good peers needed for accurate predictions. The proposed model has a mean normalized RMSE of 0.057 and about 95% of the systems tested had an R2 score higher than 0.85. The existing commercial software at Solar Monkey has a mean normalized RMSE of 0.082 and about 83% of the systems tested had an R2 score higher than 0.85.

The predicted energy generation calculated by the proposed model is compared with the actual energy generation to detect any malfunctions that may have occurred in the monitored system. Thus, 120 randomly chosen PV systems were analysed for faults. Based on this, a semi-automatic categorization framework was created with the proposed model as one of the criteria to detect common faults in the system such as missing data, under-performance, over-performance and false positives. Using the categorization framework, certain PV systems were found as interesting examples for under-performance with broken panels or string, over-performance with system size change and false positives. The model is especially useful for separating system design mismatch from actual system malfunctions. With the framework, it was shown how the proposed peer-to-peer model can be used for fault detection along with certain other models.