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Local Optical Spectroscopies for Subnanometer Spatial Resolution Chemical Imaging
The evanescently coupled photon scanning tunneling microscopes (STMs) have special requirements in terms of stability and optical access. We have made substantial improvements to the stability, resolution, and noise floor of our custom-built visible-photon STM, and will translate these advances to our infrared instrument. Double vibration isolation of the STM base with a damping system achieved increased rigidity, giving high tunneling junction stability for long-duration and high-power illumination. Light frequency modulation with an optical chopper and phase-sensitive detection now enhance the signal-to-noise ratio of the tunneling junction during irradiation.
PV Module Manufacturing and Testing: Industry Requirements for Perovskite PV Modules
What standard tests are required for commercializing a new PV module technology such as perovskite photovoltaics? This summary document provides answers in the areas of (1) quality assurance for manufacturing, (2) safety and reliability testing, and (3) performance characterization.
The Capital Intensity of Photovoltaics Manufacturing
Factory capital expenditure (capex) for photovoltaic (PV) module manufacturing strongly influences the per-unit cost of a c-Si module. This provides a significant opportunity to address the U.S. DOE SunShot module price target through capex innovation. Innovation options to reduce the capex of PV manufacturing include incremental and disruptive process innovation with c-Si, platform innovations, and financial approaches. and financial approaches.
Bifacial PV System Mismatch Loss Estimation & Parameterization
Non-uniform irradiance on the rear-side of bifacial PV systems can cause additional mismatch loss which may not be appropriately captured in PV energy production estimates and software. We have evaluated several rooftop mounted systems over high albedo reflective roofs. Mismatch losses of up to 2% annual loss for very close-mounted (0.15 m) rooftop systems were found, but losses for high ground-clearance rooftop systems were lower (<0.5%). A simplified empirical relationship was found that links the spatial variation of irradiance (specifically the mean absolute difference of irradiance) to the resulting mismatch loss factor, with a R2 fit better than 0.985. This approximate relationship has been validated by experiments on mismatched PV modules, and provides a basis for fast estimation of bifacial mismatch loss factors for use in hourly PV performance simulations such as PVSyst or SAM.
Bifacial PV System Mismatch Loss Estimation and Parameterization: Preprint
Non-uniform irradiance on the rear-side of bifacial PV systems can cause additional mismatch loss which may not be appropriately captured in PV energy production estimates and software. We have evaluated several scenarios including horizontal single-axis tracking (HSAT) and rooftop mounted systems over high albedo reflective roofs. Mismatch losses of up to 2% annual loss for very close-mounted (0.15 m) rooftop systems were found, but losses for HSAT systems and high ground-clearance rooftop systems were lower (<0.5%). A simplified empirical relationship was found that links the spatial variation of irradiance (specifically the mean absolute difference of irradiance) to the resulting mismatch loss factor, with a R2 fit better than 0.985. This approximate relationship has been validated by experiments on mismatched PV modules, and provides a basis for fast estimation of bifacial mismatch loss factors for use in hourly PV performance simulations such as PVSyst or SAM.
Measuring Sustainability of PV in Energy Transition: Mass, Energy, and Circularity
Energy transition to a carbon-free energy system is crucial for global decarbonization and underpins circular economy (CE) goals. Photovoltaic (PV) technology is required for energy transition, but manufacturing and circular pathways can be material, energy, and carbon-intensive. Therefore, we need a prioritization of sustainability strategies for PV evolution. In this study, we compare sustainable PV module designs in terms of their entailed mass, energy, circularity, and carbon impacts to achieve Energy Transition. Results will support decision-makers through data-backed scenario and tradeoff analyses offering prioritization of sustainable PV module designs and CE pathways for meeting Energy Transition goals.
De-Risking Large-Scale PV Systems Through Data Analytics
Large-scale photovoltaic system performance analysis is being conducted within the US Department of Energy's-sponsored PV Fleet Performance Data Initiative. This collaboration with commercial PV system owners collects and evaluates PV field performance data, and provides reports on aggregated results. Drawing on over 2200 sites across the US and over 24,000 separate PV inverters we have collected in excess of 8.3 gigawatts (GW) of performance data, representing 6-7% of the entire US installed PV capacity. A mixture of utility-scale and large commercial systems are represented, averaging 4.1 megawatts (MW) in size and 5 years in age. Initial results show average system degradation rates at -0.75% / year, which is slightly higher than historically reported module-level values of -0.5%/year. We also found that the availability of systems averaged 97.7%, which is lower than the typical 99% uptime assumed by many project economic forecasts. Given these results, we compared monthly performance with expected production values, based on satellite weather data and a simple PVWatts performance model. We found that systems were performing within 10% of monthly expectation over 90% of the time, with a fleet average vs expected monthly value of 0.994. We also evaluated the impact of extreme weather events on system performance, and found a range of short-term and longer-term performance effects ranging from grid outage, system downtime, module damage and accelerated long-term degradation rate.
THE COOL SURGE FOLLOWING FLUX EMERGENCE IN A RADIATION-MHD EXPERIMENT
Cool and dense ejections, typically Hα surges, often appear alongside EUV or X-ray coronal jets as a result of the emergence of magnetized plasma from the solar interior. Idealized numerical experiments explain those ejections as being indirectly associated with the magnetic reconnection taking place between the emerging and preexisting systems. However, those experiments miss basic elements that can importantly affect the surge phenomenon. In this paper we study the cool surges using a realistic treatment of the radiation transfer and material plasma properties. To that end, the Bifrost code is used, which has advanced modules for the equation of state of the plasma, photospheric and chromospheric radiation transfer, heat conduction, and optically thin radiative cooling. We carry out a 2.5D experiment of the emergence of magnetized plasma through (meso) granular convection cells and the low atmosphere to the corona. Through detailed Lagrange tracing we study the formation and evolution of the cool ejection and, in particular, the role of the entropy sources; this allows us to discern families of evolutionary patterns for the plasma elements. In the launch phase, many elements suffer accelerations well in excess of gravity; when nearing the apex of their individual trajectories, instead, the plasma elements follow quasi-parabolic trajectories with accelerations close to g{sub ⊙}. We show how the formation of the cool ejection is mediated by a wedge-like structure composed of two shocks, one of which leads to the detachment of the surge from the original emerged plasma dome.
A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting
The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solar power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.
Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe
Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.
Modeling and designing multilayer 2D perovskite / silicon bifacial tandem photovoltaics for high efficiencies and long-term stability
A key challenge in photovoltaics today is to develop cell technologies with both higher efficiencies and lower fabrication costs than incumbent crystalline silicon (c-Si) singlejunction cells. While tandem cells have higher efficiencies than c-Si alone, it is generally challenging to find a low-cost, high-performance material to pair with c-Si. However, the recent emergence of 22% efficient perovskite photovoltaics has created a tremendous opportunity for high-performance, low-cost perovskite / crystalline silicon tandem photovoltaic cells. Nonetheless, two key challenges remain. First, integrating perovskites into tandem structures has not yet been demonstrated to yield performance exceeding commercially available crystalline silicon modules. Second, the stability of perovskites is inconsistent with the needs of most end-users, who install photovoltaic modules to produce power for 25 years or more. Making these cells viable thus requires innovation in materials processing, device design, fabrication, and yield. We will address these two gaps in the photovoltaic literature by investigating new types of 2D perovskite materials with n-butylammonium spacer layers, and integrating these materials into bifacial tandem solar cells providing at least 30% normalized power production. We find that an optimized 2D perovskite ((BA) 2 (MA) 3 (Sn 0.6 Pb 0.4 ) 4 I 13 )/silicon bifacial tandem cell, given a globally average albedo of 30%, yields a normalized power production of 30.31%, which should be stable for extended time periods without further change in materials or encapsulation.
Renewable Energy Potential Model: Geothermal Supply Curves
The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.
Industry Facing PV Degradation Prediction Tool and Database to Enable a 50-Year Life Module
The Photovoltaic (PV) industry constantly aims for lower costs, higher-efficiency cells, and improved module designs. These trends lead to using new materials, designs, and manufacturing processes, resulting in a continually changing technological landscape. These changes can potentially introduce new, unknown degradation mechanisms and failure modes that are difficult to diagnose, analyze, test, and model. This introduces uncertainty into the expected lifetime of PV modules of 25 years. Furthermore, research efforts aim for up to 50 years of service life while keeping performance degradation at a minimum for decades of outdoor weathering - putting additional pressure on improving the accuracy of long-term durability and reliability assessments. Here there is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. Because the core of this type of analysis involves calculations that are complicated but ubiquitous for many degradation processes, an enhanced predictive modeling framework will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this work, we present an online tool that can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. A graphical user interface will aid in the understanding of the results. The prediction tool will be built modular and published as open source, enabling users to expand on the existing framework. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database to perform geospatial degradation analysis in the US and worldwide. Our repository will contain various degradation models and material parameters suitable for the reliability and durability assessment of materials and components deployed outdoors. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry.
Durability of ionomer encapsulants in photovoltaic modules
Ethylene vinyl acetate (EVA) has for decades been the preferred material for photovoltaic (PV) module encapsulation. However, while it is relatively inexpensive and initially adheres well to module components, EVA discolors with age and - as interfacial adhesion degrades - becomes susceptible to delamination, ultimately resulting in reduced module efficiency and shortened service lifetimes. As potential replacements for EVA, ionomer thermoplastic materials cure faster, are more resistant to discoloration and potential induced degradation, and do not evolve corrosive acetic acid, making them compatible with new device materials such as perovskites. Since there is limited information on ionomer durability for PV module applications, a series of field and accelerated laboratory aging studies were conducted to assess ionomer interface stability in the presence of terrestrial environmental stressors. It is shown that adhesion to the glass and cell interfaces of PV modules is inferior to EVA, both before and after aging, rendering ionomers particularly susceptible to delamination after short timeframes. Potential solutions to improve ionomer adhesion are discussed.
Recent Advances on Carbon Nanospheres. Synthetic Routes and Applications
Carbon-based materials are the most popular material types in both fundamental research and industrial applications, partly because of their well-controlled nano-morphologies. In the past two decades, we have witnessed a number of breakthroughs in carbon research: fullerenes, carbon nanotubes, and more recently graphene. Nowadays, carbon nanospheres are attracting more and more attention worldwide due to their excellent performance in various fields: drug delivery, heterogeneous catalysis, encapsulation of support and electrode materials. Actually, spherical carbon is an old material, whereas controlling carbon spheres in the nanometer range is a recent story. In the past 5 years, it has become possible to precisely control the particle size, surface area, pore size, chemical composition, and dispersity of carbon nanospheres. Toward this end, a number of synthetic strategies are emerging, such as hydrothermal carbonization of biomass-based resources, extended Stöber synthesis, and organic–organic self-assembly via different binding methods. In this feature article, we summarize recent routes for carbon nanospheres and briefly touch on their applications to shed light on the potential of this field. Throughout this article, a special emphasis is placed on the possible modulation of spherical structures at the nanoscale, and we wish to inspire many more designs and applications of carbon nanostructures in the near future.
Cost and Potential of Monolithic CIGS Photovoltaic Modules
A bottom-up cost analysis of monolithic, glass-glass Cu(In,Ga)(Se,S)2 (CIGS) modules is presented, illuminating current cost drivers for this technology and possible pathways to reduced cost. At 14% module efficiency, for the case of U.S. manufacturing, a manufacturing cost of $0.56/WDC and a minimum sustainable price of $0.72/WDC were calculated. Potential for reduction in manufacturing costs to below $0.40/WDC in the long-term may be possible if module efficiency can be increased without significant increase in $/m2 costs. The levelized cost of energy (LCOE) in Phoenix, AZ under different conditions is assessed and compared to standard c-Si.
A Combined Computational and Mathematical Analysis of Interconnect Fatigue Potential in Photovoltaic Modules
A finite element model of a 60-cell monocrystalline silicon glass-polymer photovoltaic module was simulated with ±1.0 kPa and ±2.4 kPa loads applied to the glass to calculate the deformation under load. Cell-to-cell displacements were used to approximate interconnect strain and stress. A mathematical fatigue cycle life relation was fitted to data for the interconnect material (copper), to generate a life prediction at each interconnect location based on the local stress means, reversal extents, and amplitudes. Interconnect stress was found to be significantly asymmetric about zero despite symmetric positive and negative module loads due to laminate thickness offsets about the neutral plane and the effects of module framing. Cycle life results indicated that interconnect fatigue failure was unlikely to occur over a 30-year lifetime of conservative wind and snow load cycles since the typical cell design feature of leaving some unconstrained length between the cell edge and first solder pad increases the effective gauge length and decreases the stress levels below the material endurance limit. Follow-up analyses found that 3.6 mm and 6.4 mm were the minimum unconstrained lengths required to survive the assumed lifetime of wind and snow cycles, respectively, confirming that typical industrial module constructions with 8–15 mm unconstrained lengths should survive conservatively. Notably, large magnitude, low-cycle snow loading was consistently the limiting factor requiring a longer unconstrained interconnect length. Finally, insights and workflows from this study inform module interconnection design limits for survival against mechanical fatigue in deployment environments.