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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Unsupervised Azimuth Estimation of Solar Arrays in Low-Resolution Satellite Imagery through Semantic Segmentation and Hough Transform

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm.

14 SOLAR ENERGY↗
DOE OSTI2021

Clear Sky Irradiance Year-to-Year Variations and Trends

Solar radiation data for seven SURFRAD stations for the period 1996-2018 show an increase in irradiance under clear skies over the period, but the increase was not constant and shorter periods may have even experienced decreases in irradiance. A popular implementation of a clear sky model provided modeled irradiances for comparison with the SURFRAD data under conditions screened for clear skies. The use of the clear sky model, which did not consider year-to-year variances in atmospheric turbidity, was found problematic for resolving small changes in irradiance important for determining PV system degradation over three or five years, but less problematic for a period of ten years.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Clear Sky Irradiance Year-to-Year Variations and Trends: Preprint

Solar radiation data for seven SURFRAD stations for the period 1996-2018 show an increase in irradiance under clear skies over time, with an interannual variability of the increase. A popular implementation of a clear sky model provided modeled irradiances for comparison with the SURFRAD data under conditions screened for clear skies. The use of the clear sky model, which did not consider year-to-year variances in atmospheric turbidity, was found problematic for resolving small changes in irradiance important for determining PV system degradation over three or five years, but less problematic for a period of ten years.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Hierarchical Control of Utility-Scale Solar PV Plants for Mitigation of Generation Variability and Ancillary Service Provision

This paper presents a hierarchical control system to mitigate the variability of solar photovoltaic (PV) power plant and provide ancillary services to the electric grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a small fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up production from the overall power plant, for a service such as regulation reserve. This control system is practical for continuously changing cloud cover conditions in partially cloudy days. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The proposed control algorithm is subsequently compared with the alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants.

14 SOLAR ENERGY↗
DOE OSTI2023

Universal Utility Data Exchange (UUDEX): Information Exchange Models for Solar DER

This report describes how the Universal Utility Data Exchange (UUDEX) can encapsulate and exchange information about distributed energy resources (DER) within UUDEX-formatted JSON documents. It proposes to use the existing extensible markup language (XML) information exchange format described in IEEE Std 2030.5, IEEE Standard for Smart Energy Profile Application Protocol but not other information exchange provisions of IEEE 2030.5, rather, relying on equivalent mechanisms in UUDEX. This will allow adoption of the UUDEX exchanges with minimal changes to the structure of the information exchanged and will not require a modification to the UUDEX documentation if IEEE 2030.5 makes changes to the information models.

42 ENGINEERING↗
DOE OSTI2020

Where and When Does Solar Plus Storage Make Sense for Commercial Buildings?

As the capital cost of battery energy storage systems (BESS) declines, opportunities for commercial buildings to achieve net savings through peak demand management and energy arbitrage are emerging. National Renewable Energy Laboratory researchers modeled energy storage project economics - with and without accompanying solar photovoltaic systems - using local utility rates, ASHRAE climate zones, and solar intensity data to identify regions where these systems deliver life cycle savings now and in future cost scenarios.

battery energy storage systems↗
DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗
DOE OSTI2020

Constraints on flavor-diagonal non-standard neutrino interactions from Borexino Phase-II

The Borexino detector measures solar neutrino fluxes via neutrino-electron elastic scattering. Observed spectra are determined by the solar-$\nu_{e}$ survival probability $P_{ee}(E)$, and the chiral couplings of the neutrino and electron. Some theories of physics beyond the Standard Model postulate the existence of Non-Standard Interactions (NSI's) which modify the chiral couplings and $P_{ee}(E)$. In this paper, we search for such NSI's, in particularly neutral-current-like interactions that modify the $\nu_e - e$ and $\nu_\tau - e$ couplings, using Borexino Phase II data. Standard Solar Model predictions of the solar neutrino fluxes for both high- and low-metallicity assumptions are considered. No indication of new physics is found at the level of sensitivity of the detector and constraints on the parameters of the NSI's are placed. In addition, with the same dataset the value of $\sin^2\theta_W$ is obtained with a precision comparable to that achieved in reactor antineutrino experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

The Baseline Performance Reference for Irradiance in PV System Applications

This report proposes the definition of a new baseline performance reference (BPR). The definition goes beyond existing standards pertaining to photovoltaic (PV) reference cells and devices to define the response under all possible operating conditions in the field. Field evaluations using BPR devices will be more sensitive to performance anomalies than pyranometers because they track PV system power output more closely. At the same time, they will be able to detect a broader range of performance anomalies than traditional matched reference devices, which might have matching defects. The BPR definition also opens the door to new practices in resource assessment and yield prediction. Solar resource data can be collected or modeled and validated directly as BPR irradiance, and PV system simulations based on BPR irradiance need fewer assumptions and less processing to obtain the effective irradiance on modules. As a result, lower uncertainty in yield assessments can be expected.

14 SOLAR ENERGY↗
DOE OSTI2025

Five Years of Dissolved Oxygen, Temperature, Salinity, Depth, Weather Data from a Transitioning Wetland at Beaver Creek, Washington, USA

Groundwater dissolved oxygen (DO) variability in coastal system remains poorly understood despite its importance for biogeochemical cycling and ecosystem modeling. Here we investigate the temporal variability in groundwater DO and its hydro-climatic drivers across hourly to seasonal timescales in a transitioning wetland at Beaver Creek, Washington, USA. The site is transitioning from a freshwater forest to a brackish tidal wetland following removal of a barrier in 2014 that prevented tides from accessing the freshwater creek. By utilizing novel optical dissolved oxygen instrumentation (Opti O2, LLC) we obtained continuous, high-frequency (5-minute), in-situ measurements of DO from the flood-plain from June 26th, 2019 through September 30th, 2024. This 63 month dataset is comprised of groundwater dissolved oxygen, temperature, water level and salinity timeseries from the floodplain. This dataset also includes rainfall, air pressure, air temperature, and solar radiation data collected with a co-located Campbell ClimaVUE50 weather sensor. All data is contained within a single csv (2019-06-26 to 2024-09-30 Beaver Creek DO, saln, BGS, temp, weather.csv) that can easily be viewed either using software such as Excel or using any text editor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB

Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018-2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.

data models↗
DOE OSTI2020

Evaluation of the PV Resource Dataset in Central and North America: Preprint

A recent effort from the National Renewable Energy Laboratory (NREL) has led to a new radiative transfer model, Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT), to efficiently compute plane-of-array (POA) irradiance. This model has been implemented by the National Solar Radiation Data Base (NSRDB) to provide photovoltaic (PV) resource over Central and North America in both narrow- and broad wavelength bands. This study conducts a comprehensive evaluation of the PV resource dataset using surface-based observations in various climate zones. The results demonstrate the PV resource has an excellent agreement with the cloudy-sky observations from thermopiles and reference cells.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size: Preprint

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear- and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute timescale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

irradiance↗
DOE OSTI2024

Volatile Organic Compounds and Meteorological Conditions in the Missouri Ozark AmeriFlux (MOFLUX) Site, 2023

This data set contains measurements of atmospheric components and meteorological conditions of central Missouri, United States during the summer of 2023. Data serve to examine the impact of meteorological conditions relevant to future climate on the emission and transformation of volatile organic compounds (VOCs). During the field campaign, researchers were also able to incorporate opportunistic analyses of the long-range transport of smoke plumes generated from extreme forest fire activities in Canada. VOC measurements were conducted at the Missouri Ozark AmeriFlux (MOFLUX) site (latitude 38.7441, longitude −92.2000) using a proton transfer reaction time of flight mass spectrometer (PTR-ToF-MS 6000 X2). The sampling campaign was conducted during the summer of 2023 (2023-06-25 to 2023-08-12) with measurements being taken at high temporal resolution (1 hour). Particular VOCs analyzed include: Methanol, Acetonitrile, Acetone, Isoprene, Methylvinyl ketone (MVK) and methacrolein (MACr), Benzene, Toluene, Catechol, and Monoterpene. Meteorological parameters included in this dataset were collected from a nearby Columbia Regional Airport (~10 km). Global solar radiation data were measured at a weather site in Ashland, MO, 5.22 km from the MOFLUX tower. The data were accessed using the MesoWest online website (https://mesowest.utah.edu/) provided by the Department of Atmospheric Sciences, University of Utah. Discussion of the data providers, database, and dissemination were highlighted in prior studies (Horel et al., 2002a; Horel et al., 2002b) . Smoke mixing ratios (in mg m−3) were estimated from the High-Resolution Rapid Refresh (HRRR) 3 km weather model for Missouri at 6-hour intervals (Dowell et al., 2022). This dataset contains three data files in comma separate (*.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

Acetone↗
DOE OSTI2023

Impact of system parameters and geospatial variables on the reliability of residential systems with PV and energy storage

A reliable power supply is the foundation of modern society, enabling technologies used to function within a society. Residential systems are places where the end users directly consume power, enabling technologies to sustain life. With the emergence of behind-the-meter resources, the end-users have some control over power supply reliability. The intermittency and variability of these resources impact residential system reliability. In this work, we study the reliability performance of a grid-supplemented residential system with behind-the-meter Distributed Energy Resources(DER) subject to various system parameters and geospatial variables. We propose a multistate reliability model for the behind-the-meter microinverter-based Photo Voltaic (PV) system and integrated inverter-based energy storage (ES) system. A sequential Monte Carlo method is then presented to evaluate the reliability indices for the residential system with behind-the-meter DERs as the main supply and the grid as the backup. The sequential Monte Carlo method is used to analyze the reliability performance of the residential model at the top 100 populous counties of the United States, where actual load and solar irradiance data at the counties is used. In the analysis, the sensitivity of indices to system parameters such as DER size and the sensitivity of the indices to climate zone and gross horizontal irradiation (GHI), which affects the load conditions and the PV output of the residential system, is performed. The analysis results show that the system parameters and the geospatial variables significantly impact the residential system’s reliability. The insights from this analysis will be of immense value to the distribution system planners to provide zone-specific guidelines for DER system sizing and toward the evolution of utility business models. Finally, the methodology developed can be used to extend the analysis to other locations.

14 SOLAR ENERGY↗
DOE OSTI2026

A Reverse Logistics Tool For Ev Battery Recycling And Repurposing,

The demand for electric vehicles (EVs) in the United States is projected to rise significantly, with sales expected to reach approximately 4.1 million units by 2030. However, the U.S. remains heavily reliant on imports for the batteries and critical raw materials—such as lithium, cobalt, and nickel—that power these vehicles. As of 2024, around 70% of these imports originate from China. This dependency has become even more precarious following China’s imposition of export restrictions in April 2025, a retaliatory move against U.S. tariffs. These developments highlight the strategic vulnerabilities posed by China’s dominant position in the critical materials market. Compounding the issue, decades of intensive extraction have severely depleted global reserves of critical materials, widening the gap between supply and growing demand. This situation underscores the urgent need for the U.S. and other nations to diversify their sources of critical materials and enhance domestic capabilities to secure these resources—an essential step toward ensuring long-term energy security. At the end of their lifecycle—whether due to the battery’s degradation or the retirement of the vehicle—EV batteries are often improperly disposed of or sent to landfills. However, many of these batteries still retain usable capacity and can follow one of three alternative pathways: (a) Re-used: deployed in another vehicle with a shorter driving range, (b) Re-purposed: utilized act as a backup storage/power for data centers, solar panels, and e-scotters or (c) Recycled: broken down to recover the critical materials. To that end, the proposed tool (REBORN) is designed to optimize the reverse logistics network for battery repurposing and recycling. Its goal is to minimize associated costs while identifying optimal locations for battery collection and processing. Ultimately, REBORN ensures that each battery is used to its fullest potential.

Srinivas, SrikarV. [Idaho National Laboratory (INL↗