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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Data-Driven Model for Photovoltaic Generation: Comparison with Physical Models Using a Microgrid in Puerto Rico

Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model’s performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.

R pesante colón, Marcos↗
DOE OSTI2026

Advancing Agrivoltaic Modeling With ADAM

Designing an agrivoltaic system presents a complex set of tradeoffs around PV system configuration, resulting performance, agricultural needs, and system economics. Developing tools for agrivoltaic analysis can assist system designers when making decisions related to these tradeoffs. We are developing the Agrivoltaics Design and Analysis Model (ADAM) as a free, publicly available web tool for agrivoltaics economics analysis. Features of ADAM include automated calculations for available agrivoltaic cropland, user-friendly configuration changes, inter-row irradiance calculations, and integrated economic modeling of energy and non-energy revenues. We will discuss the iterative process of agrivoltaic tool development including tradeoffs between accuracy and uncertainty, feature prioritization, and ease of use driven by our multi-disciplinary stakeholder design process. Finally, we will share modeling results from existing agrivoltaics systems as a preliminary case study.

14 SOLAR ENERGY↗
NASA NTRS1982

Reliability and performance experience with flat-plate photovoltaic modules

Statistical models developed to define the most likely sources of photovoltaic (PV) array failures and the optimum method of allowing for the defects in order to achieve a 20 yr lifetime with acceptable performance degradation are summarized. Significant parameters were the cost of energy, annual power output, initial cost, replacement cost, rate of module replacement, the discount rate, and the plant lifetime. Acceptable degradation allocations were calculated to be 0.0001 cell failures/yr, 0.005 module failures/yr, 0.05 power loss/yr, a 0.01 rate of power loss/yr, and a 25 yr module wear-out length. Circuit redundancy techniques were determined to offset cell failures using fault tolerant designs such as series/parallel and bypass diode arrangements. Screening processes have been devised to eliminate cells that will crack in operation, and multiple electrical contacts at each cell compensate for the cells which escape the screening test and then crack when installed. The 20 yr array lifetime is expected to be achieved in the near-term.

Ross, R. G., Jr.↗
DOE OSTI2020

Viscoelastic Material Characterization and Modeling of Photovoltaic Module Packaging Materials for Direct Finite-Element Method Input

Numerical tools, such as the finite-element method, are increasingly used to design and evaluate the photovoltaic (PV) modules, providing for the reduction of development time and improved performance and reliability. However, high-fidelity material models are necessary to accurately model the complex structural behavior of the involved packaging materials. A common simplification used in recent years is to model the polymer materials (i.e., encapsulant and backsheet) as linear elastic, which will lead to inaccurate results. Therefore, in this work, we present a thorough characterization of the time- and temperature-dependent mechanical response of predominant PV module encapsulant and backsheet materials. Based on this material characterization, we developed and experimentally validated generalized Maxwell models to describe each material's viscoelastic response. In addition, we included measurements of the coefficient of thermal expansion and presented all material models in such a fashion for direct input into commercial finite-element method modeling software.

FEM↗
DOE OSTI2020

LID and LeTID Impacts to PV Module Performance and System Economics: Draft Analysis

This webinar is a technical review of the measurements and causes of BO LID, UV LID and LeTID, as well as modeling their impacts to PV project financial metrics. We will first review how Boron-Oxygen complexes form and detail the range of impacts to PV module power ratings over time and across climates. Similarly, we will also discuss the measurements and extent of UV LID over time and across climates. Then, we will demonstrate a methodology for translating these effects to pro forma cash flow models and their impacts to PV project financial returns and levelized cost of electricity (LCOE). We will conclude with estimates of the value of solutions to these degradation mechanisms.

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

DEPRECATED EMeRGE (Emerging technologies Management and Risk evaluation on distribution Grids Evolution) [SWR-20-40]

DEPRECATED This repository was archived by the owner on Jun 30, 2026. It is now read-only. EMeRGE (Emerging technologies Management and Risk evaluation on distribution Grids Evolution) is a collection of mini-tools to help users develop openDSS feeder model from GIS (.shp) file and perform risk analysis at various PV scenarios and visulize results in an interactive dashboard made using Dash.

Duwadi, Kapil↗
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2021

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗
DOE OSTI2020

System-level performance and degradation of 21 GWDC of utility-scale PV plants in the United States

We assess the performance of a fleet of 411 utility-scale (i.e., >5 MWAC and ground-mounted) photovoltaic (PV) projects totaling 21.1 GWDC (16.3 GWAC) of capacity, which achieved commercial operations in the United States from 2007 to 2016. This fleet of projects contributed more than 50% of all solar electricity generated in the United States in 2017. Using detailed information on individual project characteristics, in conjunction with modeled irradiance data, we assess the extent to which actual first-year performance has lived up to both modeled and stated expectations. We then analyze system-level performance degradation in subsequent years by employing a “fixed effects” regression model to statistically isolate the impact of age on system performance. We find that this fleet of utility-scale PV projects has generally lived up to ex ante expectations for first-year performance but that subsequent system-level degradation—found to be −1.3%/year (±0.2%)—has, on average, been worse than both ex ante expectations (commonly −0.5%/year) and results from past studies (ranging from −0.8%/year to −1.0%/year). We emphasize that −1.3%/year is a system-level estimate that captures more than just module degradation (e.g., including soiling, balance of plant degradation, and downtime for maintenance and/or other events). A side analysis of a variety of project characteristics suggests that system-level degradation rates tend to be of lower magnitude among newer projects and larger projects and at sites with lower long-term average temperatures.

14 SOLAR ENERGY↗
DOE OSTI2022

SolarAPP+ Pilot Analysis: Performance and Impact of Instant, Online Solar Permitting

The National Renewable Energy Laboratory (NREL) led a collaborative effort to develop the Solar Automated Permit Processing Plus (SolarAPP+), a no-cost solar permitting software solution to address residential solar photovoltaic (PV) permitting resource constraints and streamline solar adoption processes among authorities having jurisdiction (AHJs). The SolarAPP+ is an online portal that automates permit plan review, enabling an instant permit approval process for code-compliant residential PV systems. Based on national model building, electrical, and fire codes, the SolarAPP+ automatically performs a compliance check of permit inputs against code requirements and produces an inspection checklist that can be used to verify installation practices, workmanship, and adherence to the approved design. NREL conducted a two-phase pilot with five participating AHJs and 16 solar contractors, spanning from November 2020 through December 2021, evaluating the ability of SolarAPP+ to deliver instant permits and its impact on four critical areas within each AHJ: permit review timelines; solar adoption timelines; AHJ time saved; and inspection results. The pilot confirmed that SolarAPP+ reduced permit review times to less than one day, reduced solar adoption timelines by an average of 12 days, saved an estimated 2,067 staff hours, and had comparable inspection results to those found in traditional permitting. The pilot further identified key lessons for improving SolarAPP+, including ways to streamline SolarAPP+ adoption, development of training resources, and expansion of supported products.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Optimal PV Inverter Control in Distribution Systems via Data-Driven Distributionally Robust Optimization

Distribution systems with high penetration of uncertain solar generation call for advanced control strategies of photovoltaics (PVs) inverters. This paper proposes a data-driven distributionally robust optimization (DDDRO) approach to optimally controlling the PV inverters to improve the system operation performance under solar power uncertainties. In the proposed DDDRO approach, a Wasserstein ball-based method is proposed to construct the distributional ambiguity set to model the uncertainties of PV generation through partial observations of historical data without knowing exact probability distributions. We further reformulate the computationally intractable DDDRO model to a mixed integer second order cone programming (MISOCP) problem. The effectiveness and out-of-sample performance of the proposed approach have been demonstrated on a modified IEEE 33-node system. We conduct a comparative study to compare the proposed method with traditional chance constrained programming (CCP). It shows that the proposed DDDRO approach can provide a less conservative yet robust solution to minimize the worse-case expectation of the total network loss while maintaining nodal voltages in a secure range.

Xue, Yaosuo↗
NASA NTRS2009

Effect of Pharmacologically-Induced Hypovolemia on Aerobic Capacity

Decreased peak oxygen consumption (VO2pk) and an elevated exercise heart rate (HR) response are associated with a reduction in plasma volume (PV) after space flight and bed rest, a space flight analog. Reduced VO2pk and submaximal exercise tolerance would negatively impact an astronaut s ability to perform near maximal work that would be required in the event of an emergency. We previously have administered IV furosemide followed by a low salt diet to model PV loss and orthostatic intolerance observed after spaceflight. Purpose: To determine whether a pharmacologically-induced reduction in PV results in decreased VO2pk and elevated exercise HR response. Methods: Six subjects (5M, 1F) performed two graded peak cycle tests (work rate increased by 35 or 50 W every 3 min), once while normovolemic and once while hypovolemic. HR and expired respiratory gases were continuously measured. To induce hypovolemia, subjects were administered a single dose of IV furosemide (0.5 mg.kg-1) 30 hr before exercise testing and then consumed a low-salt diet (10 mEq.d(sup -1)). PV was measured using carbon monoxide rebreathing. Exercise HR and VO2 responses were quantified as the area under the curve (AUC) calculated over each quartile of the peak test, based on test time in the hypovolemia condition. Paired t-tests were used to test for differences in PV, VO2pk, and peak HR between conditions. Repeated-measures ANOVAs were used to test for differences in AUC between conditions. Results: PV (3.32+/-0.12 vs. 2.77+/-0.16 L, p<0.05) and VO2pk (3.30+/-0.67 vs. 2.90+/-0.57 L.min(sup -1), p<0.05) were lower during hypovolemia than during normovolemia, but peak HR was not different (187+/-5 vs. 187+/-5 bpm). The AUC for VO2 and HR was different (p<0.05) between conditions only in the highest quartile: HR was 4% higher and VO2 was 5% lower during the hypovolemia condition. Conclusion: The mean difference in VO2pk (-12%) between normovolemia and hypovolemia was similar to the mean difference in PV (-17%). Similar decreases in PV and VO2pk have been observed following short duration space flight, suggesting that pharmacologically-induced PV loss can be used to model microgravity-induced reductions in VO2pk.

Everett, Meghan E.↗
DOE OSTI2020

A Model for Evaluating the Configuration and Dispatch of PV Plus Battery Power Plants

An open-source model was developed to optimize energy storage operation for photovoltaic- (PV-) plus-battery systems with AC-coupled and DC-coupled configurations. It includes the ability to use forecast energy prices to optimize battery charge and discharge on a rolling time horizon. The model allows for exploration of different configurations, including capital costs, inverter performance, dispatch flexibility, and capturing otherwise clipped energy for the DC-coupled system. The model can run 20 full years of hourly data in approximately two seconds, allowing comparison of a large number of configurations. We applied the model in a test case demonstrating reduced inverter clipping for DC-coupled systems and yielded slightly higher overall value than AC-coupled systems, with an approximately 2 percent increase in internal rate of return or benefit/cost ratio. Our results show that at current estimated prices for lithium-ion battery systems, large-scale PV-plus-battery plants are economically viable under the right conditions, with the configuration playing a role in system flexibility and performance. This model provides the ability for project developers, industry professionals, and researchers to use readily available software to quickly evaluate and design these systems.

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

Improvement in PV Plant LCOE from Convection Heat Transfer Changes from Altered Plant Layout

Heat transfer modeling that accounts for how convective cooling changes with PV array layout has been found to improve system LCOE in certain climates conditions. Analysis of fixed tilt systems performed using the System Advisor Model reveals that reducing system ground coverage ratio from 0.46 to 0.35 can lead to as much as a 1.7% increase in module annual energy output in Phoenix. Depending on climate conditions, these energy increases due to changing convective cooling flow can lead to LCOE improvements for systems with increased row spacing despite the increased wiring and land costs associated with increased module row spacing. While the energy gain from decreasing system ground coverage ratio can be largely attributed to increased plane of array irradiance, the convection cooling considerations presented here can have a non-negligible impact on PV power plant energy output and economic viability depending on climate conditions and array spacing parameters.

heat transfer↗
DOE OSTI2025

Validation of Photovoltaic Modeling Tool Solargraf Against Measured Data

Solargraf is a cloud-based 3D design tool by Enphase Energy that allows users to design solar and storage systems with a variety of elements. Through a Cooperative Research and Development Agreement (CRADA), Enphase Energy collaborated with the National Renewable Energy Laboratory (NREL) to validate Solargraf's 3D design simulation against measured PV system performance. This study follows the same methodology of similar validation studies completed at NREL. The predicted performance results from simulations in both Solargraf and NREL's System Advisor Model (SAM) tool were compared with measured data to evaluate performance predictions.

14 SOLAR ENERGY↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2026

Gaps in PV-Coupled Battery Modeling

With the rapid increase in battery attachment rates for PV systems and the continued qualification of storage for the ITC under OBBB, accurately characterizing PV-coupled batteries is becoming increasingly critical for the PV industry. This presentation will explore the key gaps in current best practices for modeling PV-coupled battery storage, including challenges such as battery thermal conditioning, the influence of dispatch strategies on battery value, the limitations of oversimplified degradation and performance models, and the absence of a centralized database containing sufficient battery parameters for accurate modeling. For behind-the-meter applications, we will also address issues like mismatched solar resource and load profiles, as well as uncertainties in future utility rates. Where applicable, we will quantify the impacts of these gaps on the accuracy and uncertainty of modeling efforts and highlight ongoing research aimed at addressing these challenges.

14 SOLAR ENERGY↗
NASA NTRS1998

EOS Interdisciplinary Investigation: Observational and Modeling Studies of Radiative, Chemical, and Dynamical Interactions in the Earth's Atmosphere

A 3-dimensional model of atmospheric dynamics and photochemistry has been developed from the primitive equations in isentropic coordinates. The model extends from the upper troposphere through the middle atmosphere and is driven by observed dynamical and chemical structure in the troposphere. The model's formulation is entirely spectral: Horizontal structure is represented in terms of vector Hough functions, which explicitly partition the motion into its rotational and divergent components. This formulation enables the model's computational performance to be increased dramatically by filtering high-frequency gravity waves, which do not affect PV conservation. Vertical structure is represented in terms of eigenfunctions that follow from the primitive equations in isentropic coordinates. The model's fully spectral formulation enables scale-selective dissipation, necessary for numerical stability, to be applied at 6th order-in all 3 coordinate directions. This feature leaves all but the shortest vertical scales undamped and, consequently, allows potential vorticity and chemical tracers to be conserved quite accurately, distinctly better than in the model's layered counterpart. These dynamical features are complemented by a basic but fairly complete treatment of gas phase photochemistry, which accounts for some 48 chemical species, diurnally-varying SW absorption by O2 and O3, and LW cooling calculated via a detailed band calculation of radiative transfer. Driven by observed tropospheric structure, the 3D model reproduces observed structure in the middle atmosphere, inclusive of transport by the Brewer-Dobson circulation and accompanying chemical variations. Calculated residual motion is consistent with diabatic cooling rates, poleward transport, and descent in the polar night derived from satellite measurements of chemical tracers made from LIMS, SAGE, and UARS. The model also reproduces the observed structure of chemical species like O3, HNO3, and ClO. At the same time, it provides a respectable 3D simulation of the wintertime increase of midlatitude total ozone, as well as its geographical structure observed by Nimbus-7 TOMS. The 3D model has been used to explore the origin of diabatic upwelling in the tropical lower stratosphere, which has been related to planetary wave absorption in the winter- time stratosphere, as well as to influences from tropospheric convection at the tropical tropopause.

Salby, Murry L.↗