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DOE OSTI2022

Reference Cell Performance and Modeling on a One-Axis Tracking Surface: Preprint

Performance of five silicon-based reference cells is examined on a single-axis tracking surface. The reference cells' output are modeled using one-minute sampled spectral irradiance and reference cell temperature measurements. The model also incorporates spectral responsivity data for the reference cell. The transmission of light through the glazing multiplies the sum over all appropriate wavelengths of temperature adjusted reference cell responsivity times the measured spectral irradiance. Modeled reference cell output is compared with measured reference cell output under clear sky and totally cloudy sky conditions. For each reference cell the ratio of modeled to measured output varies by less than 2% over the year.

modeling↗

DOE OSTI2021

Prediction of Leakage Current and Depletion Voltage in Silicon Detectors Under Extraterrestrial Radiation Conditions

Silicon detection is a mature technology for registering the passage of charged particles. At the same time it continues to evolve toward increasing radiation tolerance as well as precision and adaptability. For these reasons it is likely to remain a critical element of detection of systems associated with extraterrestrial exploration. Silicon sensor leakage current and depletion voltage depend on the integrated fluence received by the sensor and on its thermal history during and after the irradiation process. For minimal assumptions on shielding and hence on the particle energy spectrum, and using published data on Martian ground temperature, we predict the leakage current density and the depletion voltage, as a function of time, of silicon sensors in transit to and deployed continuously on the Mars surface for a duration of up to 28 Earth-years, for several sensor geometries and a worst-case temperature scenario.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

TROPOS pyranometer network (PyrNet) observational data set collected during the S2VSR field campaign

During the Small-Scale Variability of Solar Radiation field campaign (S2VSR), a unique sensor network consisting of 60 autonomous pyranometer stations was deployed for a 12-week period at the ARM Southern Great Plains observatory. Stations have been distributed across a 4x4 to 6x6 km2 domain surrounding the Central Facility. Each station has recorded global radiation together with air temperature and relative humidity with a sampling frequency of 1-Hz. The network has provided a data set with an unprecedented level of detail of the small-scale variability of global solar radiation at the surface, resolving fluctuations down to the second- and decameter-scale.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Using an Absolute Cavity Pyrgeometer to Calibrate Pyrgeometers Outdoors with Respect to the International System of Units

Accurate measurement of the atmospheric longwave irradiance is important for renewable energy and atmospheric science applications. Pyrgeometers are deployed outdoors all over the world to measure the atmospheric longwave irradiance and presently are calibrated with traceability to the interim standards for atmospheric longwave radiation measurement, the standards are based on four pyrgeometers and their average irradiance is the World InfraRed Standard Group (WISG) which is developed and maintained by The Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC). Since 2013 the InfraRed Integrating Sphere (IRIS) developed by PMOD/WRC and the Absolute Cavity Pyrgeometer (ACP) developed by the National Renewable Energy Laboratory (NREL) have been compared outdoors six times at different locations and the difference between the measured atmospheric longwave irradiance by ACP and IRIS was less than 2 W/m2 with traceability to the International System of Units (SI). During the six comparisons the irradiance measured by the interim WISG was 5 W/m2 lower than the irradiance measured by the average irradiance measured by the ACP and IRIS [1]. Based on this discrepancy, the World Meteorological Organization's Commission for Instruments and Methods of Observation (CIMO) recommended that the interim WISG should be adjusted to be traceable to SI units [2]. In anticipation of CIMO's expert team agreement on establishing the world reference using the average irradiance measured by ACP and IRIS in this article we describe a procedure to calibrate pyrgeometers with traceability to SI. One Absolute Cavity Pyrgeometer (ACP95F3) was used to calibrate four pyrgeometers traceable to SI units. Three Eppley PIRs and one Kipp&Zonen CG4 were originally calibrated with traceability to the interim WISG. Using the described procedure below, the responsivity of each pyrgeometer was then adjusted to match the irradiance measured by ACP. Outdoor data was collected during one clear sky nights monitored by the output thermopile voltage of ACP95F3. The irradiance measured by the PIRs was calculated using NREL equation and the CG4 using NREL equation and PMOD/WRC equation. Using the NREL equation, the calculated uncertainty (U_95) of the PIRs varied from 2.43 W/m2 to 2.67 W/m2, and for the CG4 using the NREL equation U_95 equals 1.97 W/m2, and using the PMOD equation U_95 equals 2.88 W/m2 with respect to SI.

International System of Units↗

DOE OSTI2022

NREL Comparison of Absolute Cavity Pyrgeometers, InfraRed Integrating Sphere, and Pyrgeometers Traceable to World Infrared Standard Group: September 26-October 7, 2022

The comparison of the absolute cavity pyrgeometers (ACPs) with the InfraRed Integrating Sphere (IRIS), Eppley Precision Infrared Radiometer (PIR) pyrgeometers, and Kipp & Zonen (KZ) pyrgeometers traceable to the World Infrared Standard Group (WISG) was held during NREL ACP and IRIS Comparisons (NAIC) from September 26 to October 7, 2022. Data from all instruments was collected during nighttime clear sky conditions only. The irradiance measured by the ACPs is collected in 30 seconds intervals during the measurement period of two hours, and 10 seconds intervals during the calibration period of 6 minutes. During the comparison, the average (av) irradiance difference measured by ACPs and IRIS varied from -0.80 W/m2 to 0.29 W/m2 and standard deviation (sd) from 0.98 W/m2 to 1.78 W/m2. The average irradiance difference measured by ACP95F3 minus the irradiance measured by all pyrgeometers varied from 2.07 to 5.03 W/m2 with sd from 2.64 W/m2 to 2.67 W/m2.

14 SOLAR ENERGY↗

DOE OSTI2022

A Physics-Based DNI Model for Advancing Solar Resource Assessment and Forecasting: Preprint

Direct Normal Irradiance (DNI) is one of the most used quantities to quantify the magnitude of solar energy resource. The concept of DNI is often interpreted differently for ground measurements and solar forecasting by numerical weather prediction (NWP) models, leading to substantial bias during evaluation of DNI forecasts especially under cloudy-sky conditions. To eliminate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to provide a physics-based solution of solar radiation in the circumsolar region. The FARMS-DNI is implemented in the Weather Research and Forecasting model with solar extensions (WRF-Solar) to forecast day-ahead DNI in the north America. By comparing with conventional predictions from WRF-Solar and satellite observations from the National Solar Radiation Data Base (NSRDB), we found significant improvements in our prediction of DNI.

DNI↗

DOE OSTI2022

Measuring and Modeling Bifacial Technologies

Measuring and modeling bifacial technologies is of interest for new system design, capacity testing, and performance evaluation. The bifacial PV field at NREL has been gathering data for different bifacial technologies and sensors for the last 2.5 years, making it possible to compare bifacial performance with monofacial counterparts, as well as experimental demonstrations of best locations to place sensors to match modeled and perceived irradiance non-uniformity on the rear of the bifacial modules. This presentation summarizes performance, degradation, sensor position for module edge effects and the impact of row edge effects, and the roadmap for NREL bifacial modeling tools.

bifacial PV↗

DOE OSTI2023

Viewfactor and Raytracing for AgriPV Modeling

View factor models are used in due diligence software to calculate rear irradiance for bifacial modules. An intermediate step in this calculation is the irradiance at the ground level, which can be leveraged for evaluating the Photo Active Radiation available for crops in Agrivoltaic setups. This paper presents the metrics and modifications to the model for ground irradiance study with the view factor approach, compares it to the raytracing method, and validates it with field measurements of ground irradiance. It is found that for the clearances, row-to-row setups, and tilts studied, the view factor method matches with raytracing results within 2% MBD. The comparison is performed for the nine most common agriPV configurations using high-performance computing and the NSRDB database for the whole US, with results and data made available open-source on the InSPIRE AgriPV website.

AgriPV↗

DOE OSTI2023

PV Module Operating Temperature - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has an extensive test site where PV modules and other system components are deployed and monitored for testing and evaluation. For this dataset PV Performance Labs has assembled one year of measurements from the Systems Long-Term Evaluation (SLTE) project (formerly known as PV Lifetime) providing the main variables needed to investigate and validate PV module operating temperature models: irradiance, ambient temperature, wind speed and back-of-module temperature. For use with more advanced thermal modeling, an estimate of down-welling long-wave radiation is also included.

14 SOLAR ENERGY↗

DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗

DOE OSTI2022

Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. Here, in this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Clear-sky detection for PV degradation analysis using multiple regression

A method is presented to detect clear-sky periods for plane-of-array irradiance time-averaged data that is based on the algorithm originally described by Reno and Hansen. Here we show this new method improves the state-of-the-art by providing accurate detection at longer data averaging intervals. Moreover, our new method detects clear periods in plane-of-array data, which is novel. The new method is developed by applying a Design of Experiment approach to optimize the parameters used in the Reno method, and Monte Carlo simulations are used to understand the robustness of the found parameters. Clear-sky detection accuracy is compared among four methods: the Reno method, the default clear-sky filter in RdTools, the Ellis method, and the method outlined in this work, using a hand-labeled two-year data set of 1-min plane-of-array irradiance for a fixed tilt system. The RdTools clear-sky filter is marred by excessive false positives. The other methods all perform well at 1-min data intervals; the method developed here provides more accurate detection at longer data averaging intervals. We show that the parameters are directly linked to the data frequency in the hope that these input variables may not have to be optimized for every data frequency and location. However, only a single fixed system in one location was carefully examined. Finally, we illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

14 SOLAR ENERGY↗

DOE OSTI2020

Iterative and Self-Consistent Estimation of Degradation and Soiling Loss in PV Systems - A Case Study

In many parts of the world, soiling is a significant loss mechanism for PV energy production. Methods for accurately quantifying historical and current soiling losses are needed. Furthermore, state-of-the-art methods to estimate degradation rates based on production data do not handle soiling in a satisfactory manner. In order to accurately estimate either of these quantities, it is in many cases helpful to consider their combined effect on PV system performance. In this work we make use of the Combined Degradation and Soiling (CODS) algorithm, which iteratively decomposes time series of daily normalized energy yield into four components: a soiling ratio, a degradation trend, seasonal variations, and noise. We demonstrate the use of the model on field performance data from a utility-scale power plant in a high-soiling environment in the Middle East. We apply the algorithm to the temperature-corrected performance index of the DC power on inverter level, quantifying how soiling varies over time and through the plant. The irradiation-weighted average soiling loss in this plant is 2.3 %, with a median soiling rate of -0.086 %/day. Between the inverters in the site in study, estimated degradation rates range between -0.86 %/year and -0.15 %/year, with a mean rate of -0.48 %/year.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting: Preprint

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term PV forecasting. Independent engineering firms typically build P50 forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions, or Pvalues, are constructed through a Monte Carlo analysis that includes various factors such as: satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within Pvalues. In this work soiling information from 16 sites in the U.S. Southwest are combined with 20 years of rainfall data to generate 20 years of energy production with soiling losses and then subsequently generate Pvalues. The results show that the spread of Pvalues (P1-P99) can increase from 0-13% when interannual soiling variability is included.

interannual variability↗

DOE OSTI2022

Single-Axis Tracker Control Optimization Potential for the Contiguous United States

Conventional tracker control algorithms maximize collection of direct irradiance with no regard for collection of diffuse irradiance. Therefore, a tracker control algorithm that optimizes for maximal total irradiance, not just direct, might realize improved insolation collection. Using weather data gridded at 0.25° by 0.25° latitude/longitude spacing covering the contiguous United States, we evaluate the insolation gain of two alternative control algorithms optimized for improved total irradiance collection in monofacial arrays and present annual and monthly geographic heatmaps showing the gains across the contiguous United States. Certain locations show potential annual insolation gains approaching 1.0%, but most locations with recently-built tracker systems show annual gains between 0.1% and 0.4%. We also demonstrate a relationship between a climate's annualized diffuse insolation fraction and its potential tracker optimization insolation gain.

diffuse↗

DOE OSTI2022

Single-Axis Tracker Control Optimization Potential for the Contiguous United States: Preprint

diffuse↗

DOE OSTI2022

PV Reference Cells for Outdoor Use: Comparison of First-Year Field Measurements

Reference cells are widely used in the photovoltaic (PV) industry to measure irradiance. For field applications and outdoor use, a variety of products are on the market, and they are often perceived as low-cost alternatives to thermopile pyranometers. But reference cell characteristics, such as directional and spectral response, fundamentally differ from pyranometers, and measurements made by the former cannot be substituted for or directly compared with the latter. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory is currently in a multiyear effort to develop guidance and recommendations for the design and use of outdoor reference cells, with a view to reducing inconsistencies and measurement uncertainty. The core of this effort is the long-term deployment of multiple products from different manufacturers that are mounted at a fixed tilt, on a single-axis tracker, and on a dual-axis tracker. This report provides an analysis of the data collected during the first year of operation at the NREL location. An overall assessment is made by comparing the total energy measured by each instrument to the other instruments having the same orientation. These totals are calculated for three different scenarios: (1) using the factory calibration factors, (2) using the previously determined NREL Cell Lab calibration factors, and (3) using the newly determined field calibration factors derived from the recorded measurements. Because the true or correct measurement values are unknown, pair-wise comparisons are made. The results show that the difference between the highest and lowest measured energy can exceed 3% when using the factory calibration factors. Consistency among instruments improves when Cell Lab calibrations are used, and it further improves when using field calibration factors.

14 SOLAR ENERGY↗

DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term photovoltaic (PV) forecasting. Independent engineering firms typically build forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions are constructed through a Monte Carlo analysis that includes various factors, such as satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within long-term performance uncertainty. In this work soiling information from 16 sites in the U.S. Southwest are combined with 24 years of rainfall data to generate 24 years of energy production with soiling losses and then subsequently generate probability of exceedance values (e.g., P50, P90, P95…). The results show that the size of the 90% confidence interval (P5–P95) can increase from –0.7% to 10.1% when interannual soiling variability and soiling rate uncertainty is included.

14 SOLAR ENERGY↗