Publications

Photovoltaic (PV) performance is affected by reversible and irreversible losses. These can typically be mitigated through responsive and proactive operations and maintenance (O&M) activities. However, to generate profit, the cost of O&M must be lower than the value of the recovered electricity. This value depends both on the amount of recovered energy and on the electricity prices, which can vary significantly over time in spot markets. The present work investigates the impact of the electricity price variability on the PV profitability and on the related O&M activities in Italy, Portugal, and Spain. Here, it is found that the PV revenues varied by 1.6 × to 1.8 × within the investigated countries in the last 5 years. Moreover, forecasts predict higher average prices in the current decade compared to the previous one. These will increase the future PV revenues by up to 60% by 2030 compared to their 2015–2020 mean values. These higher revenues will make more funds available for better maintenance and for higher quality components, potentially leading to even higher energy yield and profits. Linearly growing or constant price assumptions cannot fully reproduce these expected price trends. Furthermore, significant price fluctuations can lead to unexpected scenarios and alter the predictions.

Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

PV module operating temperature is the second-most important factor influencing PV system yield–after irradiance–and a substantial contributor to uncertainty in energy system yield predictions. Models commonly used to predict operating temperature in system simulations are based on a simplified energy balance that lumps together different heat loss mechanisms–including radiation–and assumes an overall linear behavior. Radiative heat loss to the sky is usually substantial, but modeling it accurately requires additional information about down-welling long-wave radiation or sky temperature and increases the complexity of temperature model equations. In this work we show how radiative losses to the sky can be separated into two parts to improve the accuracy of modeling without additional complexity. We also predict and demonstrate the variation of these losses at different tilt angles and show that the effective view factor is reduced by the non- isotropic distribution of down-welling long-wave radiation. Finally, we demonstrate substantial reduction in bias (MBE) and scatter (RMSE) when the new radiative loss term is added to the Faiman model using one year of measurements at Sandia National Labs.

For the PACT center to both develop testing protocols and provide service to the metal halide perovskite (MHP) PV community, PACT will seek modules (mini and full-sized) for testing purposes. To ensure both safety and high-quality samples PACT publishes acceptance criteria to define the minimum characteristics of modules the center will accept for testing. These criteria help to ensure we are accepting technologies that are compatible with our technical facilities and testing equipment and can transition to large scale commercial manufacturing. This module design acceptance criteria document is for industry partners and is different from the acceptance criteria for research partners (academia, national laboratories) partners.

The purpose of this protocol is to bring metal halide perovskite (MHP) modules to a repeatable and relevant state prior to making a performance measurement. Performance measurements are made before and after a stressor has been applied to the module to quantify the degree of loss resulting from the stressor. This procedure is intended to be carried out both before and after the accelerated test.

For the PACT center to both develop testing protocols and provide service to the metal halide perovskite (MHP) PV community, PACT will seek modules (mini and full-sized) for testing purposes. To ensure both safety and high-quality samples PACT publishes acceptance criteria to define the minimum characteristics of modules the center will accept for testing. These criteria help to ensure we are accepting technologies that are compatible with our technical facilities and testing equipment and can transition to large scale commercial manufacturing. This module design acceptance criteria document is for research partners (academia, national laboratories) and is different from the acceptance criteria for industry partners.

The purpose of this protocol is to use accelerated stress testing to assess the durability of metal halide perovskite (MHP) photovoltaic (PV) modules. The protocol aims to apply field relevant stressors to packaged MHP modules to screen for early failures that may be observed in the field. The current protocol has been designed with a glass/glass-PIB edge seal, no encapsulant package in mind. PACT anticipates adding additional testing sequences to evaluate additional stressors (e.g., PID, reverse bias) in the future.

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.

Accurate quantification of photovoltaic (PV) system degradation rate (RD) is essential for lifetime yield predictions. Although RD is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on RD by analyzing the performance of different techniques applied to synthetic PV system data at different linear RD patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. The analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower RD deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated RD is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate RD results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate RD estimation are provided.

Operation and maintenance (OM) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An OM decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a large-scale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 €/kW/year when performing OM actions. For an electricity price of 59.98 €/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized OM cost value of 7.45 €/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV OM Cost Model.

A linear performance drop is generally assumed during the photovoltaic (PV) lifetime. However, operational data demonstrate that the PV module degradation rate (Rd) is often nonlinear, which, if neglected, may increase the financial uncertainty. Although nonlinear behavior has been the subject of numerous publications, it was only recently that statistical models able to detect change-points and extract multiple Rd values from PV performance time-series were introduced. A comparative analysis of six open-source libraries, which can detect change-points and calculate nonlinear Rd, is presented in this article. Since the real Rd and change-point locations are unknown in field data, 960 synthetic datasets from six locations and two PV module technologies have been generated using different aggregation and normalization decisions and nonlinear degradation rate patterns. The results demonstrated that coarser temporal aggregation (i.e., monthly vs. weekly), temperature correction, and both PV module technologies and climates with lower seasonality can benefit the change-point detection and Rd extraction. This also raises a concern that statistical models typically deployed for Rd analysis may be highly climatic-and technology-dependent. The comparative analysis of the six approaches demonstrated median mean absolute errors (MAE) ranging from 0.06 to 0.26%/year, given a maximum absolute Rd of 2.9%/year. The median MAE in change-point position detection varied from 3.5 months to 6 years.

Studying the mechanical behavior of silicon cell fractures is critical for understanding changes in PV module performance. Traditional methods of detecting cell cracks, e.g., electroluminescence (EL) imaging, utilize electrical changes and defects associated with cell fracture. Therefore, these methods reveal crack locations, but do not operate at the time or length scales required to accurately measure other physical properties of cracks, such as separation width and behavior under dynamic loads.

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Corrective maintenance strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to failures. In this work, a complete operation and maintenance (OM) decision support system (DSS) was developed for corrective maintenance. The DSS operates entirely on field measurements and incorporates technical asset and financial management features. It was validated experimentally on a large-scale PV system installed in Greece and the results demonstrated the financial benefits of performing corrective actions in case of failures and reversible loss mechanisms. Reduced response and resolution times of corrective actions could improve the PV power production of the test PV plant by up to 2.41%. Even for 1% energy yield improvement by performing corrective actions, a DSS is recommended for large-scale PV plants (with a peak capacity of at least 250 kWp).

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.

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Within the framework of IEA PVPS, Task 13 aims to provide support to market actors working to improve the operation, the reliability and the quality of PV components and systems. Operational data from PV systems in different climate zones compiled within the project will help provide the basis for estimates of the current situation regarding PV reliability and performance. The general setting of Task 13 provides a common platform to summarize and report on technical aspects affecting the quality, performance, reliability and lifetime of PV systems in a wide variety of environments and applications. By working together across national boundaries we can all take advantage of research and experience from each member country and combine and integrate this knowledge into valuable summaries of best practices and methods for ensuring PV systems perform at their optimum and continue to provide competitive return on investment. Task 13 has so far managed to create the right framework for the calculations of various parameters that can give an indication of the quality of PV components and systems. The framework is now there and can be used by the industry who has expressed appreciation towards the results included in the high-quality reports. The IEA PVPS countries participating in Task 13 are Australia, Austria, Belgium, Canada, Chile, China, Denmark, Finland, France, Germany, Israel, Italy, Japan, the Netherlands, Norway, Spain, Sweden, Switzerland, Thailand, and the United States of America.

The IEC 61853 photovoltaic (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 for calculating system performance. In this article, a new PV module efficiency model is presented and compared with five published models using matrix data collected from four different PV module types. The results of the comparative analysis demonstrated that the new model improves on the existing ones exhibiting root-mean-square errors in normalized efficiency well below 0.01 for all cases and PV modules. The analysis also highlighted its ability to interpolate and extrapolate performance between and beyond measured matrix points of irradiance and temperature, establishing it as a robust yet relatively simple model for several applications that are detailed throughout this article.

This paper presents the first systematic comparison between south-facing monofacial and bifacial photovoltaic (PV) modules, as well as between south-facing bifacial and vertical east-west facing bifacial PV modules in Alaska. The state’s solar industry, driven by the high price of energy and dropping equipment costs, is quickly growing. The challenges posed by extreme sun angles in Alaska’s northern regions also present opportunities for unique system designs. Annual bifacial gains of 21% were observed between side by side south-facing monofacial and bifacial modules. Vertical east-west bifacial modules had virtually the same annual production as south-facing latitude tilt bifacial modules, but with different energy production profiles.

This project has four main technical objectives: 1) 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. 2) 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. 3) Evaluate optimal bifacial system designs using simulations leveraging high-performance computing, and also using full sized and miniaturized experimental field deployments. 4) 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.