The increasing deployment of photovoltaic (PV) modules in urban environments, often prone to shading, underscores the critical importance of selecting shading-resistant modules. Unfortunately, a standardized parameter for assessing and comparing PV module performance under shading remains absent. Currently, quantifying a module's ability to withstand shading remains a challenge, as datasheets typically provide vague, qualitative descriptions. This research addresses this issue by presenting the development of a shading tolerance calculator using MATLAB. This calculator yields a numeric parameter known as Shading Tolerability (ST), providing a quantitative measure of a module's ability to cope with shading conditions. By offering a standardized metric for shading tolerance, this tool enables precise characterization and facilitates direct comparisons between different PV modules.

The research's primary objective is to advance the development of a tool that can calculate the ST of any PV module using readily available datasheet parameters. The tool will subsequently be validated through experimental testing and employed to establish a comprehensive database for commercial PV modules, offering guidelines for achieving high ST. Initially, the model transitioned from sectional resolution to cell-level calculations, allowing for a more detailed analysis. The study compares ST results obtained at the cell-level with those at the 12-section level and examines the ensuing impact on ST.

A sensitivity analysis explores the influence of key PV characteristics, including breakdown voltage, nominal operating cell temperature, and bypass diodes, on ST. Notably, the analysis reveals that bypass diodes have a positive effect on ST. Based on these findings, guidelines are formulated to enhance PV module performance under shading conditions and improve the module's ST value.

In existing standards for photovoltaic (PV) plants, little guidance is provided in the spatial allocation of pyranometers. No universal and easily scalable algorithms exist that help designers of monitoring infrastructure in solar parks choose the most representative positions for irradiance sensors.

To fill this gap, a software tool is created for determining the most representative sensor locations requiring only the PV plant's main characteristics, layout, and future data usage as input. If necessary, advice on the number of sensors can be given.

The sky view factor (SVF) was determined for all relevant planes in the PV plant. This was done by adding all SVF contributions of N_a altitude bands and NA azimuth slices. The horizon obstruction at each location was determined using 30m spatial resolution digital surface model (DSM) data from Sentinel Hub imagery service. Two SVF modelling variables were optimised by varying them in SVF calculations at 3800+ existing PV plant locations in Europe. N_A,optimal=1080 and r_max,optimal=2000m were found.

The Perez model stood out from a literature review of sky diffuse model comparison studies. Moreover, six decomposition models were compared using data from twelve European Baseline Surface Radiation Network (BSRN) weather stations. A seasonal bias was found and compensated in an attempt to improve the already best-performing BRL model. The average normalised root mean squared error (nRMSE) improved from 31.8% to 30.5%. The average absolute relative mean bias error (rMBE) improved from 11.2% to 10.6%.

The software tool determines SVF maps for different orientations and tilts. Ground albedo time series is extracted from the NASAPOWER database, and historical global horizontal irradiance is imported from PV-GIS. Plane-of-array irradiance maps are constructed through transposition modelling. Subsequently, error maps are created, from which the location with minimal measurement deviations can be extracted.

The software tool was tested for a case study in Eisleben (Germany) and Kolindros (Greece). The relative prevented measurement deviation (rPMD) was up to 1.2% in the Kolindros case, with an average of 0.8% compared to 0.3% in the Eisleben case. Instantaneous measurement deviations up to seven times the rPMD were seen. Furthermore, a simplified allocation algorithm only based on the SVF maps was found, only valid under the current assumptions.