Numerical Modeling of PV-based Energy Systems from Devices to Renewable Energy Communities

The energy landscape is changing rapidly due to the urgent need to reduce the impact of climate change, overcome the limitations of fossil fuels, and improve energy security. A key part of this transition is the growing use of renewable energy sources (RES), especially solar energy, which plays a crucial role in building sustainable power systems. This thesis focuses on developing and applying numerical models to improve the efficiency of solar energy production and optimize energy management at different scales. The research addresses three main methodologies: electrical circuit modeling, artificial intelligence-based modeling, and simulation software approaches using tools such as EnergyPlus and PVGIS. These methods were applied to various levels of energy systems, from the design of a DC and AC electrical equivalent circuit of a Sb2Se3 photovoltaic cell, to the experimental validation of a one-diode model for bifacial PV systems, and the development of an electrical model for the entire PV conversion chain. This research also examines energy optimization in the building sector, given its substantial environmental impact. The potential of artificial neural networks (ANNs) for modeling energy demand in buildings, as well as the role of building-integrated photovoltaic systems (BIPVs) in achieving nearly zero-energy buildings (NZEBs), are explored. Finally, this thesis extends to the broader context of urban energy systems, analyzing the creation of renewable energy communities (RECs) and their potential to contribute to the global energy transition. The focus of this work was to contribute to a deeper understanding of the interaction between individual components within renewable energy systems and their integration at larger scales, ultimately supporting the development of more efficient and resilient energy solutions. ii