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DOE OSTI2022

Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors

Abstract The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres ( > 40 000 km 2 ) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for ∼10%–30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by ∼10%–50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly ∼50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.

14 SOLAR ENERGY↗

DOE OSTI2023

Radiometric Standards and Best Practices: Recent Progress

International standards and best practices for solar resource assessments provide assurance for traceable measurements associated with a low uncertainty. This benefits the solar energy industry by reducing the investment risks through heightened confidence in the solar resource information. NREL, in collaboration with international organizations, such as subcommittees G03 of ASTM (Radiometry) and TC180/SC1 of ISO (Climate measurement and data), recently revised some standards that are widely used by the solar industry. These include radiometric standards that assist in (i) maintaining calibration traceability; (ii) uncertainty analysis; (iii) measurement quality assurance, and (iv) establishing reference spectral irradiance distributions. The latter are widely used by the solar community to evaluate the actual absorptance, reflectance, and transmittance of solar energy materials, or the performance of solar energy devices and systems, relative to standard conditions. This paper provides a summary of the recent changes brought to these standards as a way to better support the solar energy industry.

industries↗

DOE OSTI2020

Integration of Total-Sky Imager Data with a Physics-Based Smart Persistence Model for Intra-Hour Forecasting of Solar Radiation

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Our previous study introduced a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposed the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle were accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique was used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction were predicted for future time steps using a two-stream approximation and a 5-minute exponential weighted moving average, respectively. The model evaluation indicated the estimation and forecast of cloud fraction mostly contributed to the uncertainty of the PSPI though it overcame the persistence and smart persistence models in all forecast time horizons between 5 and 60 minutes. This study aims to enhance the PSPI by ingesting surface-based observations of cloud fraction from a total sky imager (TSI). The estimation and forecast of cloud albedo is correspondingly improved by utilizing the cloud fraction observations and thus leads to more accurate GHI forecast. Various time-series analysis methods are also investigated on the forecasting of cloud fraction and cloud albedo for further improving the GHI forecast. These improvements are valuable for many applications, such as forecasting energy use for buildings, grid operations, and ultimately bringing down the cost of solar energy.

14 SOLAR ENERGY↗

DOE OSTI2022

Hole Trapping in Halide Perovskites Induces Phase Segregation

Metal halide perovskites have garnered a great deal of attention for their applications in photovoltaics, LEDs, and radiation detection. The ease of solution processing high-quality perovskite semiconductors with large absorption coefficients and tolerance to native defects is decidedly attractive. Additionally, the ability to precisely tune the band gap of halide perovskites through compositional alloying of the halide ion is of particular interest for a range of applications, especially for tandem solar cells. However, under steady state light irradiation, an initially homogeneous mixed halide perovskite (MHP) will form local domains that are rich in one halide ion (e.g., Br or I). This light-induced phase segregation in MHPs forms iodide-rich domains that act as charge carrier traps and lowers the efficiency of perovskite-based devices. Thus, phase segregation poses a serious challenge to the implementation of MHPs in real-world device settings. Interestingly, when a phase segregated MHP film is placed in the dark, entropic driving forces become dominant and the segregated perovskite remixes and returns to its initially homogeneous state. Several key mechanistic details of phase segregation have been elucidated over the years. However, there are still aspects of halide segregation that are not clear, and there is ongoing debate in the literature as to what are the key factors that contribute to the mechanism. This Account discusses recent results that point to the specific role of hole trapping in phase segregation. Interestingly, generation of holes through above-band-gap excitation or through electrochemical injection increases ion migration and leads to phase segregation. The thermodynamic and redox properties of halide perovskites provide a strong driving force for hole trapping and oxidation of iodide species in MHPs. However, mobile halide species within the perovskite lattice take time to migrate and generate halide-rich domains. When in contact with a nonpolar solvent, the migration of iodine species is further extended to expulsion of iodine from the perovskite film. Thus, the mobility of halides and their susceptibility to hole-induced oxidation play a crucial role in determining the long-term stability of metal halide perovskites. Strategies to gain kinetic control over ion migration to slow phase segregation are needed to overcome these hurdles and achieve stable mixed halide perovskites. Modification of the perovskite composition through introduction of different cations or halide ions, or introduction of low-dimensional perovskite phases may suppress phase segregation. Furthermore, in achieving stability and improving the efficiency of perovskite solar cells and light emitting devices with minimal impacts, suppression of segregation remains the key factor.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Use of Physics to Improve Solar Forecast: Part II, Machine Learning and Model Interpretability

Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.

interpretability↗

DOE OSTI2024

REST2 [SWR-23-96]

REST2 is the high-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation.

Benton, Brandon↗

DOE OSTI2024

Use of physics to improve solar forecast: Part III, impacts of different cloud types

Cloud-type impacts present a great challenge to solar forecasting due to diverse and complex cloud-radiation interactions. This third part of our paper sequence seeks to address this challenge by quantifying the forecast accuracies under eight cloud types: cumulus (Cu), stratified clouds (St), altocumulus (Ac), altostratus (As), cirrostratus/anvil (Cr), cirrus (Ci), congestus (Co), deep convective clouds (Dc) across four physics-informed persistence models reported in Part I. To generalize the cloud impacts, the eight cloud types are further grouped into three cloud categories based on their common features: weak convective clouds, stratiform clouds, and strong convective clouds. Here, the decade-long (2001 ~ 2014) collocated measurements of irradiances and cloud types at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program South Great Plain (SGP) Central Facility site are used for model evaluation. Results reveal a clear performance hierarchy for global horizontal irradiance (GHI) and direct normal irradiance (DNI): best for weak convective clouds and cirrus, intermediate for stratiform clouds, and worst for strong convective clouds. Performance for diffuse horizontal irradiance (DHI) is less influenced by cloud types. Cloud albedo dominates all three irradiances for Dc, while both cloud albedo and cloud fraction are influential for other cloud types. A 12 %~33 % improvement in accuracy at 6-hour lead time compared to the benchmark smart model confirms the effectiveness of incorporating physics into the models for various cloud types; further improvements are expected by directly integrating cloud type information into forecasting models by modifying the physical formulation of cloud-radiation interaction, and/or using more advanced machine learning models.

14 SOLAR ENERGY↗

DOE OSTI2024

17.2% Efficient CdSexTe1−x solar cell with (InxGa1−x)2O3 emitter on lightweight and flexible glass

High-efficiency, lightweight, and flexible solar cells are sought for a variety of applications particularly when high power density and flexible form factors are desired. Development of solar cells on flexible substrates may also offer production advantages in roll-to-roll or sheet-to-sheet processes. Here, we report device efficiencies of 17.2% and 14.6%, under AM1.5G and AM0 irradiances, respectively, for a flexible, lightweight, CdTe-based solar cell. To advance the efficiency relative to the highest previously reported AM1.5G value of 16.4%, we used an indium gallium oxide (IGO) emitter layer on a cadmium stannate (CTO) transparent conductor, which was deposited on 100-μm thick Corning® Willow® Glass. A sputtered CdSe layer was employed to incorporate Se into a CdTe absorber that was deposited by close-space sublimation, and CuSCN was used as a hole transport layer between the CdTe and the back metal electrode. The IGO and CTO layers remained intact during the high temperature film processing as seen in cross-sectional imaging and elemental mapping. This device configuration offers great promise for building-integrated photovoltaics, space applications, and higher rate manufacturing.

Physics↗

DOE OSTI2023

Coupled Experimental and High-Temperature Discrete-Element Method Modeling Studies of Aluminosilicate Particle Handling in Concentrated Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrated solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrated solar power environments. Coupled experimental and numerical studies of aluminosilicate particles in rotary kilns and dense particle curtains were performed for bulk temperatures up to 1073 K. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Temperature, spatial, and velocity profile data were extracted from experimental runs using embedded K-type thermocouple probes and particle image velocimetry techniques. Experimental and numerical studies were compared using spatial temperature profiles, velocity fields, and shape profiles of the bulk, granular flows. Numerical models were developed using commercially available discrete element method modeling software, Aspherix®. Existing Aspherix® functionality was expanded by introducing coupled radiative exchange modeling tools. The laboratory-scale rotary kiln was developed to investigate the steady-state heat and mass transfer performance of aluminosilicate particles based on particle type, bulk handling temperature, and wall roughness. The rotational speed of the rotary kiln was varied to control the relative impact of collisional and frictional effects upon the granular flow behavior. Heat and mass transfer performance was categorized based on the Froude number and the observed flow regimes of slipping, rolling, cascading, and centrifuging. Coupled discrete element method modeling studies were used to evaluate the effects of temperature-dependent, particulate mechanical properties upon bulk flow behavior and upon the relative effects of radiative, advective, and/or conductive heat transfer. A high-temperature (< 1073 K) falling particle curtain was similarly fabricated to investigate the heat and mass transfer performance of aluminosilicate particles in particle handling situations dominated by inter-particle collisions. The impact of particle type, flow preheat temperatures (< 1073K), and bulk mass flow rates were investigated upon the particle curtain shape, temperature, and velocity profiles. Coupled discrete element method modeling studies were performed to evaluate the varying impact of temperature-dependent, particulate mechanical properties on the bulk flow behavior and the temperature profile of the particle curtain.

14 SOLAR ENERGY↗

DOE OSTI2021

A Comparison of Time Series Gap-Filling Methods to Impute Solar Radiation Data

Complete solar resource datasets play a critical role at every stage of solar project phases. However, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including but not limited to, data gaps, data quality issue, etc. In order to mitigate these issues an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Being motivated by this, in this study we compare the performances of eight different gap filling methods extensively by creating random and artificial data gaps in (i) hourly irradiance data for one year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) one-minute ground measurement dataset from Surface Radiation Budget Network (SURFRAD) stations.

clearness index↗

DOE OSTI2022

Parameterization of cloud transmittance for expeditious assessment and forecasting of all-sky DNI

Radiative transfer models require vast computing resources to solve cloud transmittance and reflectance from the radiative transfer equation. As a result, models offering precise simulation in operations often acquire individual cloud transmittance or reflectance from a lookup table precomputed for practicable scenarios. To further expedite the computation of global horizontal irradiance and to reduce the storage requirements, the Fast All-sky Radiation Model for Solar applications (FARMS) parameterized the lookup table using elementary functions with specified coefficients. This study extends FARMS direct normal irradiance (DNI) computation by utilizing hyperbolic tangent functions and various polynomial functions to parameterize the cloud transmittance for scattered solar radiation in the circumsolar region. The parameterization is implemented in FARMS with DNI (FARMS-DNI) and accounts for the circumsolar radiation when assessing or forecasting DNI. The evaluation, with long-term observations at the National Renewable Energy Laboratory's, Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement, Southern Great Plains, Central Facility, shows that the parameterized DNIs are virtually identical with those computed by coupling FARMS-DNI to a lookup table of cloud transmittance. This parameterization has diverse applications in radiative transfer models and numerical weather prediction models used to assess or forecast direct solar radiation.

14 SOLAR ENERGY↗

DOE OSTI2022

A Comparison of Time-Series Gap-Filling Methods to Impute Solar Radiation Data: Preprint

Complete solar resource data sets play a critical role at every stage of solar energy projects; however, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including, but not limited to, data gaps and data quality issues. To mitigate these issues, an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Motivated by this, in this study, we extensively compare the performance of eight different gap-filling methods by creating random and artificial data gaps in (i) hourly irradiance data for 1 year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) 1-minute ground measurement data sets from the Surface Radiation Budget Network (SURFRAD) and the National Renewable Energy Laboratory (NREL) stations.

clearness index↗

DOE OSTI2021

Getting their days in the sun

Although metal halide perovskite solar cells are extensively investigated in the lab their performance and degradation in real-world outdoor conditions are still poorly understood. Now, researchers propose a method to analyse field data to identify how and why the outdoor device performance changes over time.

14 SOLAR ENERGY↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗

DOE OSTI2024

Equating Reference Cell and Broadband Pyranometer Data

An adjustment algorithm has been developed for broadband Class-A pyranometers, to modify the pyranometer data such that it more closely matches that of silicon-based reference cells. The adjustment algorithm uses modeled clear-sky spectral data as one of its inputs. The adjustment also uses a reference cell angle of incidence modifier. A comparison between the pyranometer and the reference cells is performed with and without the adjustment applied. The adjustment aligns the pyranometer and reference cell data to within +-2 % under clear skies when the zenith angle is less than 70 degrees .

incident angle modifier↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

changepoint detection↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗

DOE OSTI2024

NREL's Tools for AgriPV Modeling

As part of the Tools and Apps for Agrivoltaics session, NREL presents their tools that have capabilities for modeling PV and agricultural dual use sites. These include the System Advisor Model (SAM), bifacial_radiance open-source raytracing tool, and a simplified agriPV calculator available in the InSPIRE website.

agriculture↗