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This chapter explores bifacial PV modules, which boost energy yield by absorbing sunlight on both sides. A key design goal is maximizing rear-side light capture and uniformity through strategic choices regarding, for example, frameless glass/glass structures that minimize shading and soiling while enhancing potential-induced degradation (PID) resistance. The chapter addresses challenges like optimizing junction box placement and managing higher currents using advanced interconnection designs like multi-busbar and half-cut cells. It examines optical configurations, emphasizing transparent rear covers for albedo capture, reflective coatings, and white backsheets. A core focus is cell-to-module loss analysis, targeting optical, ohmic, and mismatch losses. Accurate I-V characterization with specialized techniques is vital, quantifying bifacial gains via Isc, Voc, and Pmax coefficients. Defect identification utilizes electroluminescence and thermography. Long-term reliability hinges on material selection (glass and encapsulants) for UV and thermal stress resistance, as well as robust junction boxes. Evolving IEC standards increasingly address bifacial-specific safety and performance requirements.
The precision of yield calculation of modern design and simulation software for photovoltaic systems strongly rely, beside the accuracy of the specified module and inverter data, on the quality of the weather data. Since data from weather stations is not available for most locations world-wide this data is calculated by using modern interpolation methods. Beside this, simulation software typically uses historical weather data. In this work the mismatch of yield simulation results based on proprietary data, meaning interpolated or also called synthetical data, and data coming from a weather station in proximity to the installation is evaluated. The simulated data sets are compared to measurement data as obtained by the inverter output and hence give a profound understanding how interpolated data may influence the simulation results. The outcome shows that the quality of the yield simulation, if compared to the measurement data, is increased by a factor of up to four if on-site weather data is used as input for the simulation. The largest source of deviation is irradiation, which varies up to 10% if synthetical and measured irradiation on-site is compared. The second largest sources for simulation mismatches are power calculation and module temperature correction.
Shaalke: Development of a MATLAB Software Tool for Advanced Statistical Outdoor Data Evaluation
(2025)
This paper introduces Shaalke, a novel analytical software tool developed for the high-accuracy evaluation of long-term photovoltaic module measurement data. Addressing the critical need for robust parameter extraction from field data, Shaalke integrates advanced filtering, linear regression, and algorithmic processing to overcome limitations of traditional evaluation methods. We demonstrate Shaalke's capability to accurately determine STC parameters and both static and dynamic temperature coefficients, showing excellent agreement with manufacturer specifications and independent laboratory measurements. A key finding is Shaalke's precise mapping of the irradiance dependence of module efficiency, filling a significant gap left by typical datasheet values which often only provide data at 1000 and 200 W/m². Furthermore, the tool enables reliable power degradation analysis, identifying modules that exceed manufacturer-tolerated limits. Shaalke provides a comprehensive, data-driven platform for understanding real-world module performance, offering invaluable insights for system design, operation, and quality assurance, thereby bridging the gap between laboratory specifications and field performance.
n-type silicon modules
(2023)
The photovoltaic industry is facing an exponential growth in the recent years fostered by a dramatic decrease in installation prices. This cost reduction is achieved by means of several mechanisms. First, because of the optimization of the design and installation process of current PV projects, and second, by the optimization, in terms of performance, in the manufacturing techniques and material combinations within the modules, which also has an impact on both, the installation process, and the levelized cost of electricity (LCOE).
One popular trend is to increase the power delivered by photovoltaic modules, either by using larger wafer sizes or by combining more cells within the module unit. This solution means a significant increase in the size of these devices, but it implies an optimization in the design of photovoltaic plants. This results in an installation cost reduction which turns into a decrease in the LCOE.
However, this solution does not represent a breakthrough in addressing the real challenge of the technology which affects the module requirements. The innovation efforts must be focused on improving the modules capability to produce energy without enlarging the harvesting area. This challenge can be faced by approaching some of the module characteristics which are summarized in this chapter.
