Publications

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This report describes the creation process and final content of a spectral irradiance dataset for Albuquerque NM. The spectral irradiance measurements were made using a dual-axis tracker; therefore, they represent global normal irradiance. The dataset combines spectroradiometer and weather measurements from a two-year period into a continuous calendar year. The data files are accompanied by extensive metadata as well as example calculations and graphs to demonstrate the potential uses of this database.

The virtual bifiPV Workshop was held in July 2020 to provide the solar industry with a forum for sharing and discussing research into bifacial photovoltaic (PV) technology. This report outlines major insights from the workshop to give the reader an overview of the latest developments in bifacial PV technology worldwide, from the lab to the field. Citations are drawn from this workshop unless otherwise noted, with all proceedings available online at bifipvworkshop. com. Presentations for the bifiPV2020 Workshop focused on the following areas: bifacial power plant modeling and simulation, albedo improvements, the development of encapsulants, the durability and reliability of current bifacial technologies, performance comparisons between glass-glass and glass-transparent backsheet configurations, the future of passivated emitter and rear contact (PERC) solar cells, and the growing adoption of n-type solar cells. With 650 GW total PV installed worldwide and 1 TW to come very soon, PERC is now the standard PV cell type produced en masse. However, it is already reaching 23% efficiency, the upper limit for this type of technology. PV modules breaking the 0.5-kW barrier are starting to appear, and the costs of standard PERC technology are already below 0.2 USD/Wp. In 2019, five GW of bifacial PV were installed worldwide. In 2020, the majority of bifacial installations are expected to be located in the United States, China, and Middle East and North Africa (MENA) states. N-type bifacial technologies are becoming increasingly viable and have huge potential to dominate the market in the coming years. With bifacial technology mounted on horizontal single-axis trackers (HSAT), bids below 10 USD/MWh will soon be observed in the MENA region, and later in Chile and the United States. Factory audits and reliability testing can reduce field failures by helping buyers to select producers that follow rigorous quality assurance and quality control processes.

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This work presents a worldwide analysis on the yield potential and cost effectiveness of photovoltaic farms composed of monofacial fixed-tilt and single/dual (1T/2T) tracker installations, as well as their bifacial counterparts. Our approach starts by estimating the irradiance reaching both the front and rear surfaces of the modules for the different system designs (validated based on data from real photovoltaic systems and results from the literature) to estimate their energy production. Subsequently, the overall system cost during their 25-year lifetime is factored in, and the levelized cost of electricity (LCOE) is obtained. The results reveal that bifacial-1T installations increase energy yield by 35% and reach the lowest LCOE for the majority of the world (93.1% of the land area). Although dual-axis trackers achieve the highest energy generation, their costs are still too high and are therefore not as cost effective. Sensitivity analyses are also provided to show the general robustness of our findings. This work performs a comprehensive techno-economic analysis worldwide for photovoltaic systems using a combination of bifacial modules and single- and dual-axis trackers. We find that single-axis trackers with bifacial modules achieve the lowest LCOE in the majority of locations (16% reduction on average). Yield is boosted by 35% by using bifacial modules with single-axis trackers and by 40% in combination with dual-axis trackers. Energy production of photovoltaic (PV) modules can be increased not only by solar cells that are more efficient but also by innovative system concepts. In this study, we explore two such concepts in combination: tracking and bifacial modules. A tracking setup increases energy production by moving a PV module over the course of a day, so that it always faces the sun. Bifacial modules use special solar cells and a transparent cover to collect light not only from the front but also from the rear. Through recent advances, both concepts have seen price reductions that enable them to produce electricity cheaper than conventional PV systems. Here, we analyze the technical and economic aspects of combinations of these two concepts worldwide. We find that a combination of bifacial modules with one-axis trackers produces the cheapest electricity (LCOE 16% lower than conventional systems) by significantly boosting energy production (35% more than conventional systems).

This project has four main technical objectives. Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. Evaluate optimal bifacial system designs using simulations leveraging high performance computing, and also using full sized and miniaturized experimental field deployments. Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

The objectives of this project are as follows: 1. Reduce uncertainty in PV performance models by developing and validating new and improved models and submodes. 2. Create and manage an open source repository of modeling functions and data. 3. Build and grow the PV Performance Modeling Collaborative; and, 4. Represent the US in the IEA PVPS Task 13 Working group.

Although common practice for estimating photovoltaic (PV) degradation rate (RD) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear RD based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio (PR) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, 'nonconventional' models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.

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.

It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate (R_{D}) may exhibit nonlinear behavior, which neglecting it may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear R_{D}. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the 'real' R_{D} value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

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Dust accumulation significantly affects the performance of photovoltaic modules and its impact can be mitigated by various cleaning methods. Optimizing the cleaning frequency is essential to minimize the soiling losses and, at the same time, the costs. However, the effectiveness of cleaning lowers with time because of the reduced energy yield due to degradation. Additionally, economic factors such as the escalation in electricity price and inflation can compound or counterbalance the effect of degradation on the soiling mitigation profits. The present study analyzes the impact of degradation, escalation in electricity price and inflation on the revenues and costs of cleanings and proposes a methodology to maximize the profits of soiling mitigation of any system. The energy performance and soiling losses of a 1 MW system installed in southern Spain were analyzed and integrated with theoretical linear and nonlinear degradation rate patterns. The Levelized Cost of Energy and Net Present Value were used as criteria to identify the optimum cleaning strategies. The results showed that the two metrics convey distinct cleaning recommendations, as they are influenced by different factors. For the given site, despite the degradation effects, the optimum cleaning frequency is found to increase with time of operation.

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The IEC 61853 PV module energy rating standard requires measuring module power (and hence efficiency) over a matrix of irradiance and temperature conditions. These matrix points represent nearly the full range of operating conditions encountered in the field in all but the most extreme locations, and create an opportunity to develop alternative approaches to existing modelssuch as the single-diode models and the Sandia Array Performance Modelfor calculating system performance. This report begins by discussing the bilinear interpolation and extrapolation method from IEC 61853-3, and then describes four existing model-based methods that could be used with matrix measurements. Then a new model is developed and all options are compared according to seven objective criteria using the matrix measurements of four PV modules of different technologies. The results show that the new model is an excellent candidate for launching power matrix-based PV system simulations.