Analysis & modeling of metastable photovoltaic technologies: towards dynamic photovoltaic performance models

Climate change is one of the biggest problems in this century. To reduce the emissions that lead to the climate change, it is expected that renewable energy systems will become very important for our energy supply in the future. Among these renewable energies, photovoltaics (PV) belongs to one of the fastest growing technologies. The key drivers to justify an increasing share of photovoltaics in the energy market are the reduction in cost, the increase of efficiency and the increase in their reliability. Thin film technologies have a share of the PV market of approximately only 7%. However, thin film technologies have many advantages that show their potential for the future. Their main advantages are their low costs and their promising application for new markets, as for example for climate zones with a high amount of diffuse irradiance or their possibility to use them as building-integrated modules and deposit them on flexible substrate. A big challenge for thin film technologies is the energy yield prediction as thin film solar cells exhibit metastabilities. To solve this problem, dynamic performance models are necessary. In this thesis, the performance of thin film solar cells and modules are investigated and modeled under outdoor and laboratory conditions, whereas two approaches of dynamic performance models are implemented to improve the performance prediction of thin film modules. At the beginning of this work, a four-step procedure is defined to compare different performance models with each other. The current-density voltage (JV) curves of the outdoor modules are described with the empirical Karmalkar-Haneefa (KH) performance model. The KH model uses only four physical parameters, namely the open circuit voltage (V$_{oc}$), the differential resistance at the open circuit point (Roc), the short-circuit current density (J$_{sc}$), and the differential conductance at the short-circuit point (G$_{sc}$), to [...