This article presents a notable advance toward the development of a new method of increasing the single-axis tracking photovoltaic (PV) system power output by improving the determination and near-term prediction of the optimum module tilt angle. The tilt angle of the plane receiving the greatest total irradiance changes with Sun position and atmospheric conditions including cloud formation and movement, aerosols, and particulate loading, as well as varying albedo within a module's field of view. In this article, we present a multi-input convolutional neural network that can create a profile of plane-of-array irradiance versus surface tilt angle over a full 180^{\circ } arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N-dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. These profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.
Using a photovoltaic module where each of the 72 cells are monitored separately, we have measured the optical effects of sunlight hitting the module at different angles. As the angle of incidence increased to 60-70 degrees, we observed an increase in the nonuniformity of the light reaching the cells across the module area (up to 4% as measured by resulting cell current). The effect is hypothesized to be the result of a combination of two mechanisms: light trapping within the top sheet glass layer and reflection from the aluminum frame at the edge of the module. We confirm these effects with time-series measurements on split reference cells fielded outdoors, and with ray-tracing modeling to determine how this phenomenon may affect PV performance and module characterization.
Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-Average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.
Energy losses due to snow coverage can be high in climates with large annual snowfall. These losses may be reduced with region-specific system design guidelines. One possible factor in snow retention on PV systems could be frame presence and/or shape. Sandia is studying the effect of module frame presence on photovoltaic module snow shedding for a pair of otherwise-identical PV systems in Vermont. The results of this study provide a summary of the findings after the 2018-2019 winter period. The results clearly show that the presence of a frame inhibits PV performance in mild winter conditions.
Copper indium gallium (di)serenade (CIGS) photovoltaic cell technology has long been promoted as a cost-effective alternative to traditional PV modules based on crystalline silicon cells. However, adoption of CIGS is hindered by significant uncertainties regarding long-term reliability and performance stability, as well as a lack of accurate modeling tools to predict CIGS system performance. Sandia is conducting a multi-year study of fielded CIGS systems that range in age from 3-6 years and represent a cross-section of commercial manufacturing and packaging. Most of these arrays include modules that were thoroughly characterized prior to deployment. In this paper, we explore uncertainty in the long-term reliability and performance stability of CIGS modules by analyzing real world performance and degradation rates of these systems.
Sandia National Laboratories' continued work with bifacial PV modules has found discrepancies in the capability of bifacial PVmodules to generate energy depending on systemdesign. We have also found significant nonuniformity in rear-side irradiance across strings of bifacial PV modules, thus creating electrical mismatch between modules. Module level power electronics (MLPE) that track the maximum power point of each module alleviate some of theelectrical mismatch caused by nonuniform rear-side irradiance on bifacial PV modules. The bifacial gain of the bifacial PV modules can be increased significantly through MLPE, although the net energy gain may not be significant forunshaded bifacial PV systems. Here we present the results of a test between bifacial PV systems equipped with MLPE and the same systems without MLPE.