Publications

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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.

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.

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.

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.

The precise estimation of performance loss rate (PLR) of photovoltaic (PV) systems is vital for reducing investment risks and increasing the bankability of the technology. Until recently, the PLR of fielded PV systems was mainly estimated through the extraction of a linear trend from a time series of performance indicators. However, operating PV systems exhibit failures and performance losses that cause variability in the performance and may bias the PLR results obtained from linear trend techniques. Change-point (CP) methods were thus introduced to identify nonlinear trend changes and behaviour. The aim of this work is to perform a comparative analysis among different CP techniques for estimating the annual PLR of eleven grid-connected PV systems installed in Cyprus. Outdoor field measurements over an 8-year period (June 2006-June 2014) were used for the analysis. The obtained results when applying different CP algorithms to the performance ratio time series (aggregated into monthly blocks) demonstrated that the extracted trend may not always be linear but sometimes can exhibit nonlinearities. The application of different CP methods resulted to PLR values that differ by up to 0.85% per year (for the same number of CPs/segments).

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.

Geographic analysis of photovoltaic (PV) performance factors across large regions can help relevant stakeholders make informed, and reduced risk decisions. High temporal and spatial resolution meteorological data from the National Solar Radiation Database are used to investigate performance and cost as an effect of varying system characteristics such as the module temperature coefficients, mounting configurations and coatings. The results demonstrated the strong climatic dependence that these characteristics have on annual energy yield whereas the revenues were dominated by the electricity price.

We evaluate the use of reference modules for monitoring effective irradiance in PV power plants, as compared with traditional plane-of-array (POA) irradiance sensors, for PV monitoring and capacity tests. Common POA sensors such as pyranometers and reference cells are unable to capture module-level irradiance nonuniformity and require several correction factors to accurately represent the conditions for fielded modules. These problems are compounded for bifacial systems, where the power loss due to rear side shading and rear-side plane-of-array (RPOA) irradiance gradients are greater and more difficult to quantify. The resulting inaccuracy can have costly real-world consequences, particularly when the data are used to perform power ratings and capacity tests. Here we analyze data from a bifacial single-axis tracking PV power plant, (175.6 MWdc) using 5 meteorological (MET) stations, located on corresponding inverter blocks with capacities over 4 MWdc. Each MET station consists of bifacial reference modules as well pyranometers mounted in traditional POA and RPOA installations across the PV power plant. Short circuit current measurements of the reference modules are converted to effective irradiance with temperature correction and scaling based on flash test or nameplate short circuit values. Our work shows that bifacial effective irradiance measured by pyranometers averages 3.6% higher than the effective irradiance measured by bifacial reference modules, even when accounting for spectral, angle of incidence, and irradiance nonuniformity. We also performed capacity tests using effective irradiance measured by pyranometers and reference modules for each of the 5 bifacial single-axis tracking inverter blocks mentioned above. These capacity tests evaluated bifacial plant performance at ∼3.9% lower when using bifacial effective irradiance from pyranometers as compared to the same calculation performed with reference modules.

We evaluate the use of reference modules for monitoring effective irradiance in PV power plants, as compared with traditional plane-of-array (POA) irradiance sensors, for PV monitoring and capacity tests. Common POA sensors such as pyranometers and reference cells are unable to capture module-level irradiance nonuniformity and require several correction factors to accurately represent the conditions for fielded modules. These problems are compounded for bifacial systems, where the power loss due to rear side shading and rear-side plane-of-array (RPOA) irradiance gradients are greater and more difficult to quantify. The resulting inaccuracy can have costly real-world consequences, particularly when the data are used to perform power ratings and capacity tests. Here we analyze data from a bifacial single-axis tracking PV power plant, (175.6 MWdc) using 5 meteorological (MET) stations, located on corresponding inverter blocks with capacities over 4 MWdc. Each MET station consists of bifacial reference modules as well pyranometers mounted in traditional POA and RPOA installations across the PV power plant. Short circuit current measurements of the reference modules are converted to effective irradiance with temperature correction and scaling based on flash test or nameplate short circuit values. Our work shows that bifacial effective irradiance measured by pyranometers averages 3.6% higher than the effective irradiance measured by bifacial reference modules, even when accounting for spectral, angle of incidence, and irradiance nonuniformity. We also performed capacity tests using effective irradiance measured by pyranometers and reference modules for each of the 5 bifacial single-axis tracking inverter blocks mentioned above. These capacity tests evaluated bifacial plant performance at ∼3.9% lower when using bifacial effective irradiance from pyranometers as compared to the same calculation performed with reference modules.

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).

The advent of bifacial PV systems drives new requirements for irradiance measurement at PV projects for monitoring and assessment purposes. While there are several approaches, there is still no uniform guidance for what irradiance parameters to measure and for the optimal selection and placement of irradiance sensors at bifacial arrays. Standards are emerging to address these topics but are not yet available. In this paper we review approaches to bifacial irradiance monitoring which are being discussed in the research literature and pursued in early systems, to provide a preliminary guide and framework for developers planning bifacial projects.

The advent of bifacial PV systems drives new requirements for irradiance measurement at PV projects for monitoring and assessment purposes. While there are several approaches, there is still no uniform guidance for what irradiance parameters to measure and for the optimal selection and placement of irradiance sensors at bifacial arrays. Standards are emerging to address these topics but are not yet available. In this paper we review approaches to bifacial irradiance monitoring which are being discussed in the research literature and pursued in early systems, to provide a preliminary guide and framework for developers planning bifacial projects.

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The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

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