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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Photovoltaics module reliability for the terawatt age

Photovoltaics (PV), or solar electricity generation, has become the cheapest form of energy in many locations worldwide and, combined with energy storage, has the potential to satisfy most of our electricity needs. PV has grown at an annual compounded growth rate of approximately 30% in the last three decades. Solar energy systems will continue their impressive growth in distributed energy, microgrids, and utility scale, as efforts are made for dependable electricity in an age with increasing extreme weather. However, within this remarkable success lies a new challenge. The growth curve, combined with rapid product innovation and scale up, means that the majority of PV systems are new, without the three years of performance data that have been required in the past to estimate product lifetime. PV reliability has to address this challenge. In this review we present a brief summary of PV reliability starting with brief historical synopsis, detailing some of the technological challenges and present a framework required for long lifetime.

14 SOLAR ENERGY↗
DOE OSTI2024

Multi-year analysis of physical interactions between solar PV arrays and underlying soil-plant complex in vegetated utility-scale systems

Concerns over the land use changes impacts of solar photovoltaic (PV) development are increasing as PV energy development expands. Co-locating utility-scale solar energy with vegetation may maintain or rehabilitate the land's ability to provide ecosystem services. Previous studies have shown that vegetation under and around the panels may improve the performance of the co-located PV and that PV may create a favorable environment for the growth of vegetation. While there have been some pilot-scale experiments, the existence and magnitude of these benefits of vegetation has not been confirmed in a utility-scale PV facility over multiple years. In this study we use power output data coupled with microclimatic measurements in temperate climates to assess these potential benefits. Here this study combines multi-year microclimatic measurements to analyze the physical interactions between PV arrays and the underlying soil-vegetation system in three utility-scale PV facilities in Minnesota, USA. No significant cooling of PV panels or increased power production was observed in PV arrays with underlying vegetation. Fine soil particle fraction was the highest in soils within PV arrays with the vegetation which was attributable to the lowest wind speeds from the compounding suppression of wind by vegetation and PV arrays. Soil moisture and soil nutrient response to re-vegetation varied between PV facilities, which could be attributed to differing soil texture. No statistically significant vegetation-driven panel cooling was observed in this climate. This finding prompts a need for site-specific studies to identify contributing factors for environmental co-benefits in co-located systems.

14 SOLAR ENERGY↗
DOE OSTI2023

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.

degradation↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2025

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗
DOE OSTI2022

SolarAPP+ Performance Review: 2021 Data

Accelerating rooftop solar photovoltaic (PV) deployment has strained the capacity of local authorities responsible for permitting, inspection, and interconnection (PII). Given the ongoing expansion of rooftop PV, a growing number of authorities having jurisdiction (AHJs) and utilities are reforming PII processes to reduce delays. AHJs could significantly reduce PII timelines through reforms such as expedited reviews for small-scale systems, online customer portals, and over-the-counter permitting. However, independent reforms do not resolve issues associated with PII variability across AHJs, and many AHJs lack the resources to implement reforms. In response to these challenges, the National Renewable Energy Laboratory (NREL) developed the Solar Automated Permit Processing Plus (SolarAPP+) platform, in collaboration with local governments, code development organizations, and industry stakeholders. This report shows SolarAPP+ performance in 2021 across AHJs.

14 SOLAR ENERGY↗
DOE OSTI2020

Chapter 1: Introduction - Motivation of Polycrystalline Thin-Film Solar Cells

Thin-film solar cell development may be conceptualized as improving processes to lower costs and improve performance, with characterization as a guide along the way. Yet, pushing the boundaries of characterization is as much at the core of thin-film solar cell development as material synthesis. As PV continues to mature, there is an endless drive to improve performance to compete. Hence, PV technology is striving for increasing levels of perfection, requiring a corresponding sophistication in characterization and understanding. Consequently, the topic of this book -advanced characterization-is timely and critical to develop the potential of thin-film solar technology.

material synthesis↗
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↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2021

Cost Assessment of Future Tandem Perovskite-Silicon Modules and LCOE Comparison to Silicon Technologies in Europe

Tandem perovskite-silicon (TPS) photovoltaic (PV) technology is a promising pathway for achieving higher efficiencies than those of single-junction crystal-silicon (c-Si) technologies. In this work, a techno-economic assessment of a TPS module is performed considering a viable large-scale manufacturing process for two future scenarios in 2025 and 2030 which allowed us to identify the main cost drivers of the tandem module cost. Furthermore, to analyse the performance of tandem module-based utility-scale PV systems and understand their competitiveness in Europe, the levelized cost of electricity was compared to passivated emitter and rear cell as well as silicon heterojunction based PV systems. The aim of this work is to provide a cost and performance guidance for TPS modules which could contribute to future PV energy systems.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The DESI DR1 Peculiar Velocity Survey: Mock Catalog

We describe the production of the official set of mock catalogs for the Dark Energy Spectroscopic Instrument Peculiar Velocity Survey (DESI-PV) Data Release 1 (DR1). Our mock catalogs reproduce the Bright Galaxy Survey number density and clustering at low redshift $(z<0.1)$ and the DESI PV samples of Fundamental plane and Tully-Fisher distances, from which we derive peculiar velocities. We carefully match mock and data properties and we mimic measurements of distance indicators and peculiar velocities, which follow the same statistical properties as real data. Mock samples of type-Ia supernovae also complement the other two distance indicators. Our 675 available mock realizations were used consistently by our three different methodologies described in our companion papers that measure the growth rate of structure $fσ_8$ with DESI PV DR1. Those mocks allow us to perform precise tests of clustering models and uncertainty estimation to an unprecedented level of accuracy, and compute correlations between methodologies. The consensus value for the DESI DR1 PV growth rate measurement is $fσ_8 = 0.450 \pm 0.055$. This sample of mock catalogs represents the largest and most realistic set for cosmological measurements with peculiar velocities to date.

Bautista, J. [Marseille, CPPM]↗
DOE OSTI2024

Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control

This paper validates the efficacy of an artificial intelligence (AI)-based photovoltaic (PV) plant control and optimization approach in enabling PV plants as accountable grid reliability service providers. The validation is performed in a realistic laboratory controller-hardware-in-the-loop environment, leveraging accurate PV plant modeling and standard industrial communication protocols. Through simulations that account for diverse weather conditions and active control scenarios, the results highlight the superior performance of the AI-based solution in comparison to a state-of-the-art reference-control grouping-based approach. Such a finding contributes to mitigating the risk of overcurtailment and uninstructed deviations of active PV plant controls, and offers practical guidance for its field deployment. Furthermore, it establishes a standardized testing framework for comparing various PV active control strategies.

hardware-in-the-loop↗
DOE OSTI2022

Influence of Photovoltaic Shading on Rooftop Heat Transfer, Building Energy Loads, and Photovoltaic Power Output

Photovoltaic (PV) modules on building rooftops provide shade from summer heating, leading to a reduction in cooling load during hot seasons. However, PV shading also reduces passive solar heating during winter months, leading to an increase in the building heating load during cold seasons. In this study, the heat transfer performance of an enclosure formed by adjacent PV modules is analyzed for three locations in the United States, comparing the daily heat flux through rooftops for the case of a PV-shaded rooftop as well as an unshaded roof. Several result metrics are developed as part of this work, including the saved energy load (SEL), or the energy conserved by adding PV-shading to the rooftop, and the additional energy load (AEL), or the supplemental building energy required to replace shaded solar heating. Lastly, this work calculates the utility factor, being the ratio of SEL and PV power output to AEL as a metric of PV effectiveness. SELs are 5.2, 6.2, and 11.7 kWh/m 2 · year for Dayton, Boise, and Phoenix, respectively, while the AELs for the same locations are 1.6, 1.5, and 2.1 kWh/m 2 · year. The utility factors for the same three locations are 61, 71, and 79. In general, locations with hot, non-cloudy summers and clear skies in winter see the largest utility factor. Further, it is shown that PV shading can conserve building energy during the winter months by preventing radiative losses to cold winter skies.

13 HYDRO ENERGY↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2024

Prefeasibility Assessment for Solar PV and Storage for Critical Community Facilities in Chernihiv, Ukraine [Slides]

A prefeasibility analysis is performed for integrating solar photovoltaics and battery energy storage at four critical facilities in Chernihiv, Ukraine. The facilities were identified by Chernihiv city officials and include Hospital No. 2, the Maternity Hospital, Secondary School No. 11, and Preschool No. 4. The analyses were performed using NREL's REopt decision-support software tool. The analysis identifies potential capacities for PV and battery energy storage to provide both economic and resilience benefits. The conceptual architecture and estimates of key summary financial and performance metrics are presented.

14 SOLAR ENERGY↗
DOE OSTI2023

Battery Energy Storage System Evaluation Method

This report describes development of an effort to assess Battery Energy Storage System (BESS) performance that the US DOE Federal Energy Management Program (FEMP) and others can employ to evaluate performance of deployed BESS or solar photovoltaic (PV) +BESS systems. The proposed method is based on actual battery charge and discharge metered data to be collected from BESS systems provided by federal agencies participating in the FEMP's performance assessment initiatives. Long-term (e.g., at least one year) time series (e.g., hourly) charge and discharge data are analyzed to provide approximate estimates of key performance indicators (KPIs).

25 ENERGY STORAGE↗
DOE OSTI2020

Design of Thermophotovoltaic Cells for Optimal System Efficiency, Accounting for Photon Reuse from Front and Back Contacts

We develop a model showing how reflection from both the front and back contacts of thermophotovoltaic (TPV) cells affects the system efficiency. From this foundation we determine the design rules for front grids for TPV cells, and assess the consequences of those rules on cell design and system performance. We show that these design rules are different and more complex than the corresponding design rules for solar photovoltaic (PV) cells, and can lead to substantially different grid design and system performance than if the simpler solar PV design rules were used.

14 SOLAR ENERGY↗