The transition to renewable energy sources is crucial for mitigating climate change and ensuring a sustainable energy future. Harnessing the potential of offshore wind and floating solar technologies offers a promising avenue to increase the share of renewables in the global energy mix, reducing reliance on fossil fuels and minimising environmental impact. Offshore floating PV technology in itself and its integration with existing offshore wind farms is a new application, requiring research and development to prove its viability. Within this research field, more comprehensive models are being developed to accurately estimate the power and energy output of floating PV installations, incorporating unique characteristics and conditions specific to these systems. Simulation models for land based PV systems cannot be used as they cause an error due the different environmental conditions. The objective of this thesis is to develop such a model and use it to determine the physical placement of these floating installations in the wind farm to maximise the energy generated.

EE-Farm II is a sophisticated simulation tool designed for evaluating the electrical systems of wind farms, encompassing both AC and DC components which has now been enhanced to integrate solar farm modelling. The model for floating PV was built on the existing EE-Farm II tool in MATLAB. A tilt model from literature is used to analytically determine the effect of sea waves on the tilt of the floating PV and a model to find the effect of static shading of the wind turbine on the floating installation was developed from scratch.

In the model, the effect of degradation has been neglected and it has been assumed that the MPPT of the inverters is ideal. The tilt model shows that on average the annual energy produced would be similar to the case with no tilt effects considered i.e. fixed tilt however, the power variability can be observed on a smaller temporal scale (for e.g. daily) and is dependent mainly on the wind speeds. The energy loss on a floater due to shading in the worst-case scenario for the simulation considered was found to be 10.5% and due to the wind farm being in the North Sea, shading losses are prevalent in the north of the wind turbine.

This work helped understand the behaviour and make better power variability and energy estimations for offshore floating PV installations and also helped understand how to better place floating PV when integrating it with an offshore wind farm to maximise energy production. This work can help installation companies with their analysis and to make predictions about the energy output and possible variability throughout the year in different temporal scales. Future work should focus on determining the optimal orientation of the panels or strings by improving the model by modelling bypass diodes in the modules, addressing the current limitations due to the simplifications made.