Publications

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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.

Snow and ice accumulation on photovoltaic (PV) panels is a recognized-but poorly quantified-contributor to PV performance, not only in geographic areas that see persistent snow in winter but also at lower latitudes, where frozen precipitation and 'snowmageddon' events can wreak havoc with the solar infrastructure. In addition, research on the impact of snow and cold on PV systems has not kept pace with the proliferation of new technologies, the rapid deployment of PV in northern latitudes, and experiences with long-term field performance. This paper describes the value of a dedicated outdoor research facility for longitudinal performance and reliability studies of emerging technologies in cold climates.

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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.

Modeling and predicting snow-related power loss is important to economic calculations, load management and system optimization for all scales of photovoltaic (PV) power plants. This paper describes a new method for measuring snow shedding from fielded modules and also describes the application of this method to a commercial scale PV power plant in Vermont with two subsystems, one with modules in portrait orientation and the other in landscape. The method relies on time-series images taken at 5 minute intervals to capture the dynamics of module-level snow accumulation and shedding. Module-level images extracted from the full-field view are binarized into snow and clear areas, allowing for the quantification of percentage snow coverage, estimation of resulting module power output, and temporal changes in snow coverage. Preliminary data from the Vermont case study suggests that framed modules in portrait orientation outperform their framed counterparts in landscape orientation by as much as 24% energy yield during a single shedding event. While these data reflect a single event, and do not capture snow shedding behavior across diverse temperature and other climatic conditions, the study nonetheless demonstrates that 1) module orientation and position in the array influence shedding patterns; 2) the start of power production and bypass diode activation differ for portrait and landscape module orientations at similar percentages and orientations of snow coverage; and 3) system design is an important factor in snow mitigation and increased system efficiency in snowy climates.

Modeling and predicting snow-related power loss is important to economic calculations, load management and system optimization for all scales of photovoltaic (PV) power plants. This paper describes a new method for measuring snow shedding from fielded modules and also describes the application of this method to a commercial scale PV power plant in Vermont with two subsystems, one with modules in portrait orientation and the other in landscape. The method relies on time-series images taken at 5 minute intervals to capture the dynamics of module-level snow accumulation and shedding. Module-level images extracted from the full-field view are binarized into snow and clear areas, allowing for the quantification of percentage snow coverage, estimation of resulting module power output, and temporal changes in snow coverage. Preliminary data from the Vermont case study suggests that framed modules in portrait orientation outperform their framed counterparts in landscape orientation by as much as 24% energy yield during a single shedding event. While these data reflect a single event, and do not capture snow shedding behavior across diverse temperature and other climatic conditions, the study nonetheless demonstrates that 1) module orientation and position in the array influence shedding patterns; 2) the start of power production and bypass diode activation differ for portrait and landscape module orientations at similar percentages and orientations of snow coverage; and 3) system design is an important factor in snow mitigation and increased system efficiency in snowy climates.

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.

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