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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Do solar panels contain PFAS?

The presence and potential leaching of PFAS (Per- and Polyfluoroalkyl Substances) from solar panels are increasingly mentioned in news articles, raising public concerns. Such concerns may slow the adoption of photovoltaic (PV) technology, despite its central role in the renewable energy sector. The limited transparency from manufacturers about fluorinated materials used in PV modules, along with the scarcity of publicly available testing data, contributes to uncertainty and speculation. This perspective aims to clarify the current state of PFAS presence in solar PV. Although certain fluoropolymers are used in PV manufacturing, the scientific consensus on their toxicity indicates they should not be classified as PFAS. Portraying fluoropolymers as toxic PFAS unnecessarily amplifies concerns and unfairly undermines the perceived environmental sustainability of PV technology.

environmental impact↗
DOE OSTI2026

PV Systems Modelling with Python

PVSC tutorial demonstrating how to download irradiance data and sun position for a specific location, determine the orientation of a single-axis tracker system, calculate Plane of Array irradiance, and estimate the power output of a PV system.

14 SOLAR ENERGY↗
DOE OSTI2020

Distributed Solar 2020 Data Update [Slides]

Berkeley Lab’s Tracking the Sun report summarizes installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. This report is now being published on a biannual cycle. In 2020, Berkeley Lab has released a more limited Distributed Solar 2020 Data Update, which consists of the same data otherwise published in Tracking the Sun report. The update includes data on more than 1.9 million systems installed through 2019, covering 82% of all distributed PV systems installed nationally through that timeframe.As in prior years, the data update focuses to a large degree on installed prices reported for distributed PV projects, describing both historical trends and variability in pricing across projects.With respect to the historical price trajectory, national median installed prices fell, from 2018 to 2019, by roughly 1% for residential systems, remained essentially flat for small non-residential systems, and fell by 4% for large non-residential systems. Across all three customer segments, these are the slowest annual percentage declines since 2006-2008.Pricing continues to vary widely across individual projects, reflecting, among other things, differences in system sizing and design, installer-level pricing strategies, and local market conditions. For example, among residential systems installed in 2019, the lowest 20% were priced below $3.1/W, while the highest 20% were above $4.5/W. The distributions for non-residential systems exhibit similarly wide spreads.In addition to data on installed prices, the data update also covers a broad range of trends related to distributed PV system design, including: system sizing, module efficiency, module-level power electronics, inverter-loading ratios, solar+storage installations, mounting configuration, panel orientation, third-party ownership, and customer segmentation.

14 SOLAR ENERGY↗
DOE OSTI2022

Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

classification↗
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

SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool (SoDa) v0.1

SoDa is an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant's 30-min power output, based on the configuration of the panel. We use a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, with parameters for the cloudy states trained on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU).

Carreno, IgnacioLosada↗
DOE OSTI2022

Measuring and Modeling Bifacial Technologies

Measuring and modeling bifacial technologies is of interest for new system design, capacity testing, and performance evaluation. The bifacial PV field at NREL has been gathering data for different bifacial technologies and sensors for the last 2.5 years, making it possible to compare bifacial performance with monofacial counterparts, as well as experimental demonstrations of best locations to place sensors to match modeled and perceived irradiance non-uniformity on the rear of the bifacial modules. This presentation summarizes performance, degradation, sensor position for module edge effects and the impact of row edge effects, and the roadmap for NREL bifacial modeling tools.

bifacial PV↗
DOE OSTI2022

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Finally, our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Experimentation in Exploring Photovoltaic Inverter Dynamics Under Different Irradiance Levels Through a Data-Driven Approach

As conventional direct connections of synchronous generators are being phased out, inverter-based resources (IBRs) with grid support functions are increasingly being integrated into power systems. This transition requires the development of accurate dynamic models for IBRs to predict how power systems will adapt to varying levels of IBRs penetration, establish grid code requirements, and ensure compliance. Here, this study introduces an active probing signal-based data-driven modeling technique to accurately derive the dynamics model of a smart photovoltaic inverter operating in Volt-Watt and Freq-Watt modes, in compliance with the IEEE 1547–2018 standard. The paper focuses on investigating how the dynamics of the PV inverter model respond to fluctuations in solar irradiance, utilizing real-time digital simulator experimentation. The experimental analysis demonstrates that the amplitude of dynamics fluctuates with changes in irradiance across both operational modes and confirms the active power’s dependence on irradiance levels. Furthermore, the nature of inverter dynamics varies distinctly between the different modes of activation. Critically, our findings indicate that dynamic models require DC-gain adjustments to accommodate contrasting irradiance levels, highlighting a negative gradient linear relationship between the DC-gain of each model and the irradiance.

14 SOLAR ENERGY↗
DOE OSTI2021

Quasi-Static Time Series Fatigue Simulation for PV Inverter Semiconductors with Long-Term Solar Profile

Power system simulations with long-term data tend to have large time steps varying from one second to a few minutes. However, for PV inverter semiconductors, the minimum thermal stresses cycle is with line frequency. This requires the time step of the fatigue simulation to be much smaller than the line period. This small time step results in poor simulation speed, especially for long-term simulations. This paper proposes a fast fatigue simulation for inverter semiconductors using the quasi-static time series (QSTS) simulation concept. The fatigue analysis typically focuses on the peak and valley values of a strain and neglect the transients from peaks to valleys. The proposed simulation utilizes this property of fatigue analysis and calculates the steady state of the semiconductor junction temperature only. The resulting time step of the fatigue simulation is 15 minutes, which is consistent with the solar dataset without losing accuracy.

fast simulation↗
DOE OSTI2023

pvOps: a Python package for empirical analysis of photovoltaic field data

The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

14 SOLAR ENERGY↗
DOE OSTI2024

Solar PV Transmission Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from transmission. A setback requirement is a minimum distance from transmission infrastructure that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗
DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗
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 OSTI2021

Weather impacts on solar PV operations summary of the current body of knowledge and implications for further investigation

There is growing concern that severe weather events are negatively affecting solar photovoltaic (PV) systems and pose ongoing and unrecognized risks. Clearly research is needed to quantify and characterize the risks so that properly calibrated improvements can be implemented. To begin this process, this report is a collection of information of severe weather impacts on solar PV available today taken from research, insurance data, and field inspections. While it is not comprehensive enough to draw conclusions, this information provides a baseline for action and indicates an urgent need to investigate further.

14 SOLAR ENERGY↗
DOE OSTI2024

Solar PV Structure Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from structures based on county ordinances as of April 2022. A setback requirement is a minimum distance from a structure that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 61 meters was applied in counties without specific structure setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗