Increasing the energy yield per unit area of Photovoltaic (PV) modules is one of the main challenges the PV technology is currently facing. In search of ways to address this issue, solar tracking systems are a favorable solution, which according to literature can enhance the energy yield by up to 45%. Another way is to opt for high-efficiency solar cells. Tandem cells have emerged as a promising technology, achieving efficiencies of over 30%. The integration of these tandem cells with sun-tracking techniques suggests high-performing solar modules. The present work develops a solar tracking model to simulate the performance of tracking PV systems equipped with tandem modules. The model will be incorporated into the PVMD toolbox, a PV modeling software developed within the PVMD group. This software can predict the energy yield of PV systems using self-consistent models for each aspect of the energy conversion. The current version of the toolbox makes it impractical to include solar tracking due to the time-consuming nature of ray tracing used to compute the irradiance. Ray tracing generates sensitivity values that illustrate how sensitive is the module to incoming irradiance from any direction in the skydome. Initially, this work focuses on substituting ray tracing with an alternative faster approach to express sensitivity based on view factors. The view factor and ray tracing method are compared with respect to computational time and extent of agreement. It was found that the view factor can significantly reduce the computational time from over 12 minutes, as required in ray tracing, to a few milliseconds for a single module orientation. Additionally, the view factor method generates sensitivity values closely matching those from ray tracing. For instance, a mean RMSE of 1.2% between the two methods is achieved, for an albedo of 0.2 and module tilt of 30 degrees. Sun tracking aims to locate the module orientation that maximizes the in-plane irradiance. Directly calculating the irradiance for every orientation to identify the optimal, is not a viable option, as it requires substantial time. Thus, sun tracking was expressed as an optimization problem and algorithms were employed to address it. Based on the prevailing sky conditions three optimization case studies were defined on an hourly basis: sunny, cloudy, and intermediate hours. Multiple algorithms were compared across the three cases with selected criteria the convergence to the optimum and runtime. Matlab’s surrogate solver and an author-developed algorithm were selected, as a satisfying solution, compromising those two criteria. Finally, energy yield simulations were performed on perovskite-silicon tandem modules mounted on a dual-axis tracking system. Four locations were selected, representing different real-world conditions: Stockholm, Athens, Bombay and Bogota. Results show the module’s tilt dynamic adaptability to sky conditions: increas- ing nearly to the sun’s zenith when direct light dominates, and lowering when diffuse light is prevalent. Furthermore, the seasonal fluctuations of the energy gain of tracking systems are explored, with locations further from the equator such as Stockholm exhibiting the highest variability of 19% in winter to 36.9% in summer. In addition, the annual energy gained among the locations was found to span between 24.8% (Bogota) and 34.1% (Bombay). An important finding is the direct proportionality in gains from absorbed irradiance to DC and AC yields, illustrating a 1:1:1 ratio. Then, the effect of tracking technology on mismatch losses of tandem modules was examined. Results indicated that tracking has little impact on both the current and power mismatch. For example, the power mismatch losses slightly increased from 1.10-1.46% in static PV systems to 1.29-1.77% for tracking topologies in the locations examined. Moreover, the tandem’s annual energy gain is compared to silicon heterojunction modules. The analysis showed similar gains across locations for both cell technologies.