Daniel Riley

In this article, we present a computationally efficient method for determining optimal backtracking rotations for single-axis solar trackers on nonuniform terrain. The method allows for ganged tracking, mechanical rotation constraints, uneven row spacing, and arbitrary maximum allowable shaded fractions (to enable “fractional backtracking”). As with previous 2-D approaches, the method is suitable for terrain that varies in the transverse direction with respect to the rotation axis of the trackers. The novelty of the method lies in formulating the problem of shade avoidance as a linear problem, which is achieved by using the row interception width as the optimization variable instead of rotation angles. Formulating backtracking as a linear problem enables the use of extremely efficient linear programming algorithms, making the method highly scalable, requiring less than 1 min to compute optimal rotation schedules for hundreds of trackers. It also produces more effective backtracking rotations, reducing the frequency of shading by 4× and improving system energy output by 1%–2%.

Solar trackers are advantageous for photovoltaic (PV) systems, typically generating more energy than those mounted on fixed racks. However, this is not always the case, when the sun is obscured by clouds, it is often more efficient to position the PV panel in a fixed horizontal position toward the zenith in order to collect the most diffuse irradiance. This study introduces a hybrid single-axis tracking (SAT) algorithm, named Sundelay, which utilizes sky images to determine the optimal moments for switching between sun tracking and a fixed horizontal position. Sundelay is evaluated through simulations conducted in two distinct locations: Albuquerque, New Mexico, and Eugene, Oregon. The simulations leverage a novel dataset comprising minutely recorded $180^{\circ }$ direct irradiance measurements obtained from a multiplanar irradiance sensor (MPI) and images captured by an all-sky camera. Our findings indicate energy gains of up to 4.3% with a 61% reduction in the movement of the SAT system over a month, while maintaining minimal impact on irradiance and movement during clear sky conditions.

We present outdoor observations of metal-halide perovskite modules deployed in the Photovoltaic Accelerator for Commercializing Technologies center, which houses one of the world's broadest efforts to test metal-halide perovskite photovoltaic modules outdoors. As of January 2025, outdoor testing has encompassed over 150 modules from 14 different partners. Our findings illustrate how daily changes in efficiency, driven by exposure to light, affect field performance in real-world conditions. These effects cannot be explained by existing outdoor performance models and frustrate the notion of a traditional temperature coefficient.

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

The U.S. Department of Energy's Office of Scientific and Technical Information

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.

We present an algorithm to calculate the azimuth and elevation angles for a dual axis tracker to be pointed away from the sun with a desired orientation between the sun and the tracker face. Desired tracker positions are specified in terms of angle of incidence (AOI) and AOI direction, i.e., the direction of the projection of the sun beam onto the plane of the tracker face. This algorithm was developed to enable characterization of the electro-optical response of photovoltaic (PV) and concentrating PV (CPV) modules with anisotropic response to AOI.

Neks, mammalian orthologs of the fungal protein kinase never-in-mitosis A, have been implicated in the pathogenesis of polycystic kidney disease. Among them, Nek1 is the primary protein inactivated in kat2J mouse models of PKD.

Renal cell carcinoma (RCC) is a heterogeneous disease with resistance to systemic chemotherapy. Elevated expression of multiple drug resistance (MDR) has been suggested to be one of the mechanisms for this resistance. Here, we provide an alternative mechanism to explain RCC’s resistance to chemotherapy-induced apoptosis. Never-in mitosis A-related protein kinase 1 (Nek1) plays an important role in DNA damage response and proper checkpoint activation. The association of Nek1 with the voltage-dependent anion channel (VDAC1) is a critical determinant of cell survival following DNA-damaging treatment. We report here that Nek1 is highly expressed in RCC tumor and cultured RCC cells compared to that of normal renal tubular epithelial cells (RTE). The association between Nek1 and VDAC1 is genotoxic dependent: prolonged Nek1/VDAC1 dissociation will lead to VDAC1 dephosphorylation and initiate apoptosis. Down-regulation of Nek1 expression in RCC cells enhanced their sensitivity to DNA-damaging treatment. Collectively, these results suggest that the increased Nek1 expression in RCC cells maintain persistent VDAC1 phosphorylation, closing its channel and preventing the onset of apoptosis under genotoxic insults. Based on these results, we believe that Nek1 can serve as a potential therapeutic target for drug development in the treatment of RCC.