SEARCH · Search NASA

Results for “Solar”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24

All sources NASA NTRS DOE OSTI

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

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 OSTI2025

A two-fluid solar-wind model with intermittent Alfvénic turbulence

In one of the leading theories for the origin of the solar wind, photospheric motions launch Alfvén waves (AWs) that propagate along open magnetic-field lines through the solar atmosphere and into the solar wind. The radial variation in the Alfvén speed causes some of the AWs to reflect, and counter-propagating AWs subsequently interact to produce Alfveńic turbulence, in which AW energy cascades from long wavelengths to short wavelengths and dissipates, heating the plasma. In this paper we develop a one-dimensional two-fluid solar-wind model that includes Alfvénic turbulence, proton temperature anisotropy and a novel method for apportioning the turbulent heating rate between parallel proton heating, perpendicular proton heating and electron heating. We employ a turbulence model that accounts for recent observations from NASA’s Parker Solar Probe, which find that AW fluctuations in the near-Sun solar wind are intermittent and less anisotropic than in previous models of anisotropic magnetohydrodynamic turbulence. Our solar-wind model reproduces a wide range of remote observations of the corona and in-situ measurements of the solar wind, and our turbulent heating model consists of analytic equations that could be usefully incorporated into other solar-wind models and numerical models of more distant astrophysical plasmas.

astrophysical plasmas↗

DOE OSTI2020

Experimental demonstration of broadband solar absorption beyond the lambertian limit in certain thin silicon photonic crystals

The tantalizing possibility of 31% solar-to-electric power conversion efficiency in thin film crystalline silicon solar cell architectures relies essentially on solar absorption well beyond the Lambertian light trapping limit (Bhattacharya and John in Nat Sci Rep 9:12482, 2019). Up to now, no solar cell architecture has exhibited above-Lambertian solar absorption, integrated over the broad solar spectrum. In this work, we experimentally demonstrate two types of photonic crystal (PhC) solar cells architectures that exceed Lambertian light absorption, integrated over the entire 300–1,200 nm wavelength band. These measurements confirm theoretically predicted wave-interference-based optical resonances associated with long lifetime, slow-light modes and parallel-to-interface refraction. These phenomena are beyond the realm of ray optics. Using two types of 10-μm thick PhC’s, first an Inverted Pyramid PhC with lattice constant a= 2,500 nm and second a Teepee PhC with a = 1,200 nm, we observe solar absorption well beyond the Lambertian limit over λ = 950–1,200 nm. Our absorption measurements correspond to the maximum-achievable-photocurrent-density (MAPD), under AM1.5G illumination at 4-degree incident angle, 41.29 and 41.52 mA/cm2 for the Inverted Pyramid and Teepee PhC, respectively, in agreement with wave-optics, numerical simulations. Both of these values exceed the MAPD (= 39.63 mA/cm2) corresponding to the Lambertian limit for a 10-μm thick silicon for solar absorption over the 300–1,200 nm band.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Measurements of the time-dependent cosmic-ray Sun shadow with seven years of IceCube data: Comparison with the Solar cycle and magnetic field models

Observations of the time-dependent cosmic-ray Sun shadow have been proven as a valuable diagnostic for the assessment of solar magnetic field models. Here, seven years of IceCube data are compared to solar activity and solar magnetic field models. A quantitative comparison of solar magnetic field models with IceCube data on the event rate level is performed for the first time. Additionally, a first energy-dependent analysis is presented and compared to recent predictions. We use seven years of IceCube data for the moon and the Sun and compare them to simulations on data rate level. The simulations are performed for the geometrical shadow hypothesis for the moon and the Sun and for a cosmic-ray propagation model governed by the solar magnetic field for the case of the Sun. We find that a linearly decreasing relationship between Sun shadow strength and solar activity is preferred over a constant relationship at the 6.4σ level. We test two commonly used models of the coronal magnetic field, both combined with a Parker spiral, by modeling cosmic-ray propagation in the solar magnetic field. Both models predict a weakening of the shadow in times of high solar activity as it is also visible in the data. We find tensions with the data on the order of 3σ for both models, assuming only statistical uncertainties. The magnetic field model CSSS fits the data slightly better than the PFSS model. This is generally consistent with what is found previously by the Tibet AS-γ Experiment; a deviation of the data from the two models is, however, not significant at this point. Regarding the energy dependence of the Sun shadow, we find indications that the shadowing effect increases with energy during times of high solar activity, in agreement with theoretical predictions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Fast All-sky Radiation Model for Solar applications (FARMS) [SWR-16-18]

The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.

Xie, Yu↗

DOE OSTI2023

Fast All-sky Radiation Model for Solar applications (FARMS) 2023 [SWR-16-18]

Xie, Yu↗

DOE OSTI2024

Evaluation of Rooftop Solar Potential in Chernihiv and Lviv, Ukraine, and Efficacy of High-Resolution 3D Data Digital Twins [Slides]

NREL evaluated rooftop solar photovoltaic (PV) siting opportunities in the cities of Chernihiv and Lviv, Ukraine, leveraging very-high-resolution 3D elevation data to calculate technical potential. The study assessed total rooftop solar capacity and annual energy production. In Chernihiv and Lviv, 116,503 buildings were analyzed for their rooftop solar potential. The buildings in the study areas include a mixture of residential, commercial, and industrial buildings, characterized by diverse roof shapes and sizes. The total estimated rooftop solar capacity is 332 MWDC in Chernihiv and 873 MWDC in Lviv with annual energy production up to 376.2 GWhDC in Chernihiv and 995.5 GWhDC in Lviv. This accounting provides a clear estimate of the potential rooftop solar installations that could be realized under optimal conditions, considering both technical constraints and the geographic distribution of available rooftop space. Related to Russia's invasion of Ukraine, NREL estimates loss from buildings damaged or destroyed of 2,754 buildings, 20.15 MWDC of capacity, and 22,869 MWhDC of annual energy production in Chernihiv as well as a loss of 1,316 buildings, 34.49 MWDC of capacity, and 39,369 MWhDC of annual energy production in Lviv. The study also assessed the feasibility of adapting this methodology on a national scale using either simulated digital surface models (DSMs) or a digital twin approach. While the very-high-resolution DSM provided more precise results, the simulated DSM demonstrated reasonable accuracy for broader applications in modeling aggregated distributed solar supply. The feasibility of using a digital twin for Ukraine's national rooftop solar potential is considered promising, with certain limitations in areas with highly variable building stock and heavy war damage.

14 SOLAR ENERGY↗

DOE OSTI2020

A simple and fast algorithm for estimating the capacity credit of solar and storage

Energy storage is a leading option to enhance the resource adequacy contribution of solar energy. Detailed analysis of the capacity credit of solar energy and energy storage is limited in part due to the data intensive and computationally complex nature of probabilistic resource adequacy assessments. This paper presents a simple algorithm for calculating the capacity credit of energy-limited resources that, due to the low computational and data needs, is well suited to exploratory analysis. Validation against benchmarks based on probabilistic techniques shows that it can yield similar insights. The method is used to evaluate the impact of different solar and storage configurations, particularly with respect to the strategy for coupling storage and solar photovoltaic systems. Furthermore, application of the method to a case study of utilities in Florida, where solar is rapidly growing and demand peaks in the winter and summer, demonstrates that it can improve on rules of thumb used in practice by some utilities. If storage is required to charge only from solar, periods of high demand driven by cold weather events accompanied by lower solar production can result in a capacity credit of solar and storage that is less than the capacity credit of storage alone.

14 SOLAR ENERGY↗

DOE OSTI2022

Supply sunspots and shadows: Business siting patterns and inequitable rooftop solar adoption in the United States

Entrepreneurs in certain industries tend to locate new businesses in relatively affluent areas. Preferences to site businesses in affluent areas can reduce low-income household access to certain products. Some argue that business siting patterns could explain inequitable consumption patterns, though such causal claims are often empirically weak. Here, we explore whether business siting patterns partly explain inequitable adoption of rooftop solar photovoltaics in California. We show that solar business formation drives an immediate and sustained increase in local solar adoption, including in low-income areas. However, solar business siting patterns have only weak impacts on solar adoption equity. The data show how solar businesses headquartered in low-income areas nonetheless install solar for relatively affluent customers. Customer-level adoption inequity partly offsets the potential equity gains of siting more businesses in low-income areas. We discuss how the emergent nature and unique business model of rooftop solar help explain these nuanced results.

14 SOLAR ENERGY↗

DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗

DOE OSTI2023

19.31% binary organic solar cell and low non-radiative recombination enabled by non-monotonic intermediate state transition

Non-fullerene acceptors based organic solar cells represent the frontier of the field, owing to both the materials and morphology manipulation innovations. Non-radiative recombination loss suppression and performance boosting are in the center of organic solar cell research. Here, we developed a non-monotonic intermediate state manipulation strategy for state-of-the-art organic solar cells by employing 1,3,5-trichlorobenzene as crystallization regulator, which optimizes the film crystallization process, regulates the self-organization of bulk-heterojunction in a non-monotonic manner, i.e., first enhancing and then relaxing the molecular aggregation. As a result, the excessive aggregation of non-fullerene acceptors is avoided and we have achieved efficient organic solar cells with reduced non-radiative recombination loss. In PM6:BTP-eC9 organic solar cell, our strategy successfully offers a record binary organic solar cell efficiency of 19.31% (18.93% certified) with very low non-radiative recombination loss of 0.190 eV. And lower non-radiative recombination loss of 0.168 eV is further achieved in PM1:BTP-eC9 organic solar cell (19.10% efficiency), giving great promise to future organic solar cell research.

14 SOLAR ENERGY↗

DOE OSTI2023

Greenhouse gas emissions embodied in the U.S. solar photovoltaic supply chain

Abstract Solar photovoltaic (PV) electricity is considered to be an important source of electricity generation in the quest for net-zero carbon emissions. However, the growth of solar electricity is creating both increased material demands and increased greenhouse gas (GHG) emissions from silicon and PV manufacturing (also referred to as embodied GHG emissions of solar electricity). Here we analyze the silicon and solar PV supply chain for the United States (U.S.) market and find that the embodied GHG emissions of solar PV panel materials (such as silicon), manufacture, logistics, and installation in the U.S. given the current supply chain are 36 g CO 2 e kWh −1 of solar electricity generated. Eighty-five percent of the embodied GHG emissions are from PV panel production processes in China and other Asia–Pacific countries. Moving the silicon and PV manufacturing to the U.S. would reduce the embodied GHG emissions of solar electricity by 16% from its current level, primarily because of the lower GHG emission intensity of the U.S. electrical grid and the lower GHG emissions for aluminum electrolysis in North America. Future scenario analysis shows that by 2030, with the U.S. PV domestic supply chain and its decarbonized grid electricity and aluminum production, as well as improving PV conversion efficiency, the embodied GHG emissions of solar electricity in the U.S. will be reduced to 21 g CO 2 e kWh −1 .

14 SOLAR ENERGY↗

DOE OSTI2025

The Rise of US Photovoltaics: An Exploration of Land Use Density Trends in Large‐Scale Solar Facility Attributes, 2012–2021

An energy transition is underway in the United States; renewable energy generation is now on par with coal and nuclear generation. The number of large-scale solar photovoltaic facilities increased approximately tenfold between 2012 and 2021, with an associated 25-fold increase in cumulative installed capacity. With ambitious decarbonization and renewable energy deployment goals at both the federal and state levels, deployments of large-scale solar photovoltaic facilities will continue apace. This growth is likely to be complex with ripples of impacts felt throughout different aspects of society, and thus accurate solar land use metrics allowing more accurate predictions are of value to policymakers, planners, and other stakeholders in the future photovoltaic build-out. In this paper, we leverage data from the newly released US Large-Scale Solar Photovoltaic Database to examine recent trends in large-scale solar photovoltaic land use. We analyze the relationships between solar array capacity density (W/acre) and a range of facility attributes to better understand the future land requirements of solar capacity expansion over the coming years. Installed capacity was the single strongest determinant of solar array area. However, we found substantial variation in capacity density across facility attributes, including mount type, latitude, urbanicity, time, and prior land use.

14 SOLAR ENERGY↗

DOE OSTI2019

Economics of Solar with Storage for Municipal Buildings in the City of Orlando [Slides]

This analysis was conducted under the first round of the Solar Energy Innovation Network (SEIN), which is a program led by the National Renewable Energy Laboratory (NREL). The program assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. This analysis supported the efforts of the Orlando: Renewable and Resilient SEIN team, which consists of the City of Orlando, Orlando Utility Commission, University of Central Florida's Solar Energy Center, and the Greenlink Group. The City of Orlando aims to deploy solar and solar-plus-storage to support energy resiliency, environmental quality, and the continued affordability of the electricity supply. The team's efforts include identifying the total solar potential available city-wide, conducting more detailed analysis for municipal facilities and distribution grid modeling, and gathering insights on municipal-level solar policies and building guidelines to share with other municipalities. This analysis supports the team's work by analyzing the techno-economic potential of solar photovoltaics (PV) and lithium-ion battery energy storage at 14 municipal sites in the City of Orlando.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Affordable and Accessible Solar for All: Barriers, Solutions, and On-Site Adoption Potential

Solar energy technologies can be used as part of a suite of tools to reduce the energy burden of low-income customers, but to date, low- and moderate-income (LMI) customers have not adopted solar at the same rate as other income groups. This paper summarizes the barriers of LMI solar adoption related to finance and funding, community engagement, site suitability, policy and regulatory, and resilience and recovery and discusses existing and potential future solutions to address these barriers. In addition, we model future LMI on-site solar adoption, using the National Renewable Energy Laboratory's (NREL's) dGen model. We model future scenarios assuming no changes in the current LMI solar policy and program environment, and we add two incentives to low-income households for adopting solar: a $\$$3,000 incentive and a full incentive (i.e., the full cost of a PV system). While we model a financial incentive, this dollar reduction in cost could also come from other efforts, for example, reductions in solar soft costs. We find that by 2050, 48-49% of LMI households adopt solar, resulting in $\$$69- $\$$101 billion in first year utility bill savings to these consumers.

14 SOLAR ENERGY↗

DOE OSTI2021

Do Agrivoltaics Improve Public Support for Solar Photovoltaic Development? Survey Says: Yes!

Agrivoltaic systems allow for the simultaneous production of solar-generated electricity and agriculture. As the climate change related impacts of conventional energy and food production intensify, finding strategies to increase the deployment of solar photovoltaic systems, preserve agricultural land, and minimize competing land uses is urgent. Given the proven technical, economic, and environmental advantages provided by agrivoltaic systems, increased proliferation is anticipated, which necessitates accounting for the nuances of community resistance to solar development on farmland. Minimizing siting conflict and addressing agricultural communities’ concerns will be key in promoting public support for agrivoltaics, as localized acceptance of solar is a critical determinant of project success. This survey study assessed if public support for solar development increases when energy and agricultural production are combined in an agrivoltaic system. Results show that 81.8% of respondents would be more likely to support solar development in their community if it combined the production of both energy and agriculture. This increase in support for solar given the agrivoltaic approach highlights a development strategy that can improve local social acceptance and the deployment rate of solar photovoltaics. Survey respondents prefer agrivoltaic projects that a) are designed to provide economic opportunities for farmers and the local community b) are located on private property or existing agricultural land c) do not threaten local interests and d) ensure fair distribution of economic benefits. Proactively identifying what the public perceives as opportunities and concerns related to agrivoltaic development can help improve the design, business model, and siting of systems in the U.S.

14 SOLAR ENERGY↗

DOE OSTI2024

Achieving Scale: Community Solar Technical Potential and Meaningful Benefits in the United States

The slide deck was presented at the webinar on February 28th titled Achieving Scale: Community Technical Potential and Meaningful Benefits in the United States. It describes the National Community Solar Partnership program followed by methodology and results from a study on the technical potential of community solar and associated meaningful benefits. A key finding of the study suggests that the opportunity space for community solar to meet unmet demand for solar energy is not primarily constrained by technical potential, but by technological, market, and policy factors. NREL used rooftop and ground-mount photovoltaic siting data to model annual energy production from community solar based on various constraints and system performance. Given modeled results and community solar deployment, we discuss potential benefits including household savings, low-to-moderate income household access to solar, resilience and grid benefits, community ownership, workforce development and entrepreneurship as well as insights into community solar siting opportunities.

community solar↗