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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 OSTI2022

Lista de Verificacion Antes de la Tormenta: Sistemas Solares Fotovoltaicos (PV) Montados en el Suelo

Through funding provided by the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has used subject matter experts to compile a set of checklists to help Puerto Rico and other communities prepare for storms. Renewable energy and distributed energy systems have the potential to provide power to neighborhoods, vulnerable residents, and certain facilities within a community, if those systems are designed to provide power during a grid disruption. The storm-hardening checklists provide storm preparation actions that can increase the chances that solar photovoltaic (PV) systems are available when communities need them most. This resource was translated from English to Spanish for greater acessibility.

distributed solar PV↗
DOE OSTI2017

Utility-Scale Solar 2016: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States

The utility-scale solar sector has led the overall U.S. solar market in terms of installed capacity since 2012. In 2016, the utility-scale sector installed more than 2.5 times as much new capacity as did the residential and commercial sectors combined, and is expected to maintain its dominant position for at least another five years. This report—the fifth edition in an ongoing annual series—provides data-driven analysis of the utility-scale solar project fleet in the United States. We analyze not just installed project prices, but also operating costs, capacity factors, and power purchase agreement ("PPA") prices from a large sample of utility-scale PV and CSP projects throughout the United States. Highlights from this year's edition include the following: Installation Trends: The use of solar tracking devices dominated 2016 installations, at nearly 80% of all new capacity. In a reflection of the ongoing geographic expansion of the market beyond California and the Southwest, the median long-term average insolation level at newly built project sites declined again in 2016. While new fixed-tilt projects are now seen predominantly in less-sunny regions, tracking projects are increasingly pushing into these same regions. The median inverter loading ratio has stabilized in 2016 at 1.3 for both tracking and fixed-tilt projects. Installed Prices: Median installed PV project prices within a sizable sample have fallen by two-thirds since the 2007-2009 period, to $2.2/WAC (or $1.7/WDC) for projects completed in 2016. The lowest 20th percentile of projects within our 2016 sample were priced at or below $2.0/WAC, with the lowest-priced projects around $1.5/WAC. Overall price dispersion across the entire sample and across geographic regions decreased significantly in 2016. Operation and Maintenance (“O&M”) Costs: What limited empirical O&M cost data are publicly available suggest that PV O&M costs were in the neighborhood of $18/kWAC-year, or $8/MWh, in 2016. These numbers include only those costs incurred to directly operate and maintain the generating plant. Capacity Factors: The cumulative net AC capacity factors of individual PV projects range widely, from 15.4% to 35.5%, with a sample median of 26.3%. This project-level variation is based on a number of factors, including the strength of the solar resource at the project site, whether the array is mounted at a fixed-tilt or on a tracking mechanism, the inverter loading ratio, degradation, and curtailment. Changes in at least the first three of these factors drove mean capacity factors higher from 2010- to 2013-vintage projects, where they’ve remained fairly steady among both 2014- and 2015-vintage projects as an ongoing increase in the prevalence of tracking has been offset by a build-out of lower resource sites. Meanwhile, several of the newer CSP projects in the United States are struggling to match long-term performance expectations. PPA Prices: Driven by lower installed project prices and improving capacity factors, levelized PPA prices for utility-scale PV have fallen dramatically over time. Most recent PPAs in our sample are priced at or below $50/MWh levelized, with a few priced as aggressively as ~$30/MWh. Though impressive in pace and scale, these falling PPA prices have been offset to some degree by declining wholesale market value within high penetration markets like California, where in 2016 a MWh of solar generation was worth just 83% of a MWh of flat, round-the-clock generation. At the end of 2016, there were at least 121.4 GW of utility-scale solar power capacity within the interconnection queues across the nation. The growth within these queues is widely distributed across all regions of the country: California and the Southeast each account for 23% of the 83.3 GW of solar that first entered the queues in 2016, followed by the Northeast (17%), the Southwest (16%), the Central region (12%), Texas (6%) and the Northwest (3%). The widening geographic distribution of solar projects is a clear sign that the utility-scale market is maturing and expanding outside of its traditional high-insolation comfort zones.

14 SOLAR ENERGY↗
DOE OSTI2025

Graph-Learning-Assisted State and Event Tracking for Solar-Penetrated Power Grids with Heterogeneous Data Sources

Unlike transmission systems, distribution systems do not typically contain sufficient metering to enable real-time state estimation. The lack of sufficient real-time measurements prohibits accurate and timely monitoring of the state of distribution systems. As a result, control and optimal operation of distribution systems, especially those containing large numbers of renewable generation units are not possible without proper data and information about the current state of the system. The main motivation of this project is to address this shortcoming by developing an approach which provides “predicted” real-time measurements so that they can be used to execute a distribution system state estimator. Thus, the objective of the project is to make the distribution systems fully observable, such that the hosting capacity for solar generation can be accurately estimated, and unnecessary solar curtailments can be avoided. In order to accomplish this goal, the project investigated the use of a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams obtained from AMI meters, SCADA as well as PMU measurements and created synchronous measurement snapshots for the state estimator (SE); and developed a hybrid robust SE which provides not only accurate state estimates but also real-time feedback for the ML model refinement.

14 SOLAR ENERGY↗
NASA NTRS1981

The possible effect of 11 year solar cycle on the thermal structure of the stratosphere and ozone distribution investigated in a 2-D model

A two-dimensional time dependent radiative-photochemical-dynamical model of the stratosphere which takes into account the feedback effect of temperature on ozone concentration is used to investigate the possible changes in the seasonal and latitudinal variations of temperature, ozone, and other minor constituents associated with the UV flux variations from the solar minimum to the solar maximum. The transport model determines the zonal wind, non-zonal eddy transports, and eddy momentum fluxes, while the photochemical system includes the important reactions affecting the concentrations of ozone and other relevant trace constituents in the stratosphere. Experiments are carried out by changing the position of the sun every day and evaluating the daily average photodissociation integrals for the relevant minor constituents and ozone heating. Values are found for the ambient stratosphere and a stratosphere perturbed by solar cycle variations, and a rough comparison is made with observed ozone and temperature trends.

Vupputuri, R. K. R.↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Using concentrating-solar-power plants as economic carbon-free capacity resources

Deep decarbonization of electricity systems raises concerns about the need for generating capacity that can maintain reliable energy supply. Given this concern, we explore the ability of concentrating-solar-power plants with thermal energy storage to provide a carbon-free source of such capacity. We develop an approach to assessing the capacity contribution of concentrating-solar-power plants that considers future systems. Such considerations are important, because of potential differences in net-load patterns compared to today (e.g., due to higher renewable-energy penetrations). Using historical data spanning an 18-year period, we demonstrate that concentrating-solar-power plants with thermal energy storage can provide this necessary capacity with little impact on their financial viability. We examine the impact of myopic decisions and imperfect foresight of future system conditions on the operation of concentrating-solar-power plants and the resultant impact on their capacity contributions. We find that imperfect foresight can have limited impacts on such a use of concentrating-solar-power plants, so long as proper forecasting techniques are developed and used. Overall, our work shows that concentrating-solar-power plants with thermal energy storage may have a role to play in delivering reliable electricity supply in a decarbonized energy system.

14 SOLAR ENERGY↗
DOE OSTI2018

Proposal and evaluation of subordinate standard solar irradiance spectra for applications in solar energy systems

Reference solar irradiance spectra are needed to specify key parameters of solar technologies such as photovoltaic cell efficiency, in a comparable way. The IEC 60904-3 and ASTM G173 standards present such spectra for Direct Normal Irradiance (DNI) and Global Tilted Irradiance (GTI) on a 37 degrees tilted sun-facing surface for one set of clear-sky conditions with an air mass of 1.5 and low aerosol content. The IEC/G173 standard spectra are the widely accepted references for these purposes. Hence, the authors support the future replacement of the outdated ISO 9845 spectra with the IEC spectra within the ongoing update of this ISO standard. The use of a single reference spectrum per component of irradiance is important for clarity when comparing and rating solar devices such as PV cells. However, at some locations the average spectra can differ strongly from those defined in the IEC/G173 standards due to widely different atmospheric conditions and collector tilt angles. Therefore, additional subordinate standard spectra for other atmospheric conditions and tilt angles are of interest for a rough comparison of product performance under representative field conditions, in addition to using the main standard spectrum for product certification under standard test conditions. This simplifies the product selection for solar power systems when a fully-detailed performance analysis is not feasible (e.g. small installations). Also, the effort for a detailed yield analyses can be reduced by decreasing the number of initial product options. After appropriate testing, this contribution suggests a number of additional spectra related to eight sets of atmospheric conditions and tilt angles that are currently considered within ASTM and ISO working groups. The additional spectra, called subordinate standard spectra, are motivated by significant spectral mismatches compared to the IEC/G173 spectra (up to 6.5%, for PV at 37 degrees tilt and 10-15% for CPV). These mismatches correspond to potential accuracy improvements for a quick estimation of the average efficiency by applying the appropriate subordinate standard spectrum instead of the IEC/G173 spectra. The applicability of these spectra for PV performance analyses is confirmed at five test sites, for which subordinate spectra could be intuitively selected based on the average atmospheric aerosol optical depth (AOD) and precipitable water vapor at those locations. The development of subordinate standard spectra for DNI and concentrating solar power (CSP) and concentrating PV (CPV) is also considered. However, it is found that many more sets of atmospheric conditions would be required to allow the intuitive selection of DNI spectra for the five test sites, due in particular to the stronger effect of AOD on DNI compared to GTI. In conclusion, the matrix of subordinate GTI spectra described in this paper are recommended to appear as an option in the annex of future standards, in addition to the obligatory use of the main spectrum from the ASTM G173 and IEC 60904 standards.

14 SOLAR ENERGY↗
DOE OSTI2020

Impact of dopant-induced optoelectronic tails on open-circuit voltage in arsenic-doped Cd(Se)Te solar cells

Fluctuations refer to inhomogeneity in the distribution of donors and acceptors at the nanometer scale and occur in many compound solar cell materials such as Cu(In,Ga)Se 2 , Cu 2 ZnSn(S,Se) 4 , and CdSe x Te 1-x . In this work, numerical simulations show that these fluctuations produce not only electrostatic potential variation, but also, local changes in the carrier density and effective bandgap. For a CdSe x Te 1-x absorber doped with arsenic, simulations and cathodoluminescence data within single grains demonstrate how donor and acceptor densities—consistent with capacitance-voltage and secondary-ion mass-spectrometry data—produce tails in photoluminescence, quantum efficiency, and absorption measurements. Using multiple theoretical approaches, we demonstrate that the fluctuations can hinder expected performance gains from increased carrier density, and we describe the significant open-circuit voltage deficit observed in the CdSe x Te 1-x :As solar technology. Our results demonstrate that it is critical to characterize and reduce carrier compensation to realize a higher efficiency.

14 SOLAR ENERGY↗
DOE OSTI2021

Atmospheric neutrinos in next-generation xenon and argon dark matter experiments

In this work, we study the sensitivity of future xenon- and argon-based dark matter and neutrino detection experiments to low-energy atmospheric neutrinos. Not accounting for experimental backgrounds, the primary obstacle for identifying nuclear recoils induced by atmospheric neutrinos in xenon is the tail of the electron recoil distribution due to pp solar neutrinos. We use the NEST code to model the solar and atmospheric neutrino signals in a xenon detector and find that an exposure of 700 tonne-years will produce a 5σ detection of atmospheric neutrinos. We explore the effect of different detector properties and find that a sufficiently long electron lifetime is essential to the success of such a measurement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Joint Probabilistic Forecasts of Temperature and Solar Irradiance

In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabilistic forecasts of both temperature and irradiance are provided and the performance of the forecasting technique highlights the efficacy of the proposed approach. Results are indicative of the fact that the underlying correlation between temperature and irradiance is well captured and will therefore be useful to produce future scenarios of temperature and irradiance while approximating the underlying sample space appropriately.

14 SOLAR ENERGY↗
DOE OSTI2022

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗
DOE OSTI2024

H2Integrate [SWR-23-31]

H2Integrate(H2I) is an open-source Python package for hybrid systems engineering design and technoeconomic analysis. It models and optimizes hybrid energy plants that produce electricity, hydrogen, ammonia, steel, and other products. H2Integrate is designed to be flexible and extensible, allowing users to create their own components and models for various energy systems. The tool currently includes distributed energy generation (wind, solar, wave, tidal), battery storage, hydrogen, ammonia, methanol, and steel technologies. Other elements such as desalination systems, pipelines, compressors, and storage systems can also be included as developed by users. Some modeling capabilities in H2Integrate are provided by integrating existing tools, such as HOPP, PySAM, ORBIT, and ProFAST. The H2Integrate tool is built on top of NASA's OpenMDAO framework, which provides a powerful and flexible environment for modeling and optimization.

King, Jennifer [National Renewable Energy Laborato↗
DOE OSTI2020

Iquitos Solar Energy Integration Study

The National Renewable Energy Laboratory performed an evaluation of the technical and economic feasibility of incorporating solar photovoltaic (PV) resources into the Iquitos, Peru power system at the request of Peru's Ministerio de Energia y Minas (MINEM). The Iquitos power system is electrically islanded due to its remote location; heavy fuel oil is used to generate electricity in the system leading to high electricity generation costs from high fuel prices. Solar PV has the potential to provide locally derived energy to ensure a reliable, affordable, and equitable power grid for the Iquitos region.

14 SOLAR ENERGY↗
DOE OSTI2017

Assessing Opportunities and Challenges for Streamlining Interconnection Processes

As part of the Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, Electric Power Research Institute conducted a survey of existing utility interconnection practices and evaluated opportunities for streamlining them. The report highlights opportunities for electric utilities to improve transparency and automate administrative processes with options to fit the level of interconnections that utilities are experiencing.

14 SOLAR ENERGY↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2023

Data Analytics for Residential PV from Permit to Interconnect (Final Technical Report)

The main objective of this research is to provide novel insights into the effects of permitting, inspection, and interconnection (PII) processes on PV system installations—and in particular, into the relationship between PII processes and adoption timelines. This research can then be used to clarify the potential effect of various process changes on reducing PII timelines, customer cancellation rates, and related costs nationwide. NREL completed this research by assembling a data set of distributed, largely residential rooftop solar systems less than 50 kilowatts in size from participating solar installers. NREL produced five publications describing the effects that PII processes can have on adoption timelines nationwide, in addition to publishing an interactive data viewer with five years of PII cycle time data. This tool can be used by stakeholders to identify potential adoption timelines by local government.

14 SOLAR ENERGY↗
DOE OSTI2023

Federal Aviation Administration Vertiport Electrical Infrastructure Study

In this detailed analysis, the authors assess the charging infrastructure needed for the deployment of advanced air mobility involving electrified vertical take-off and landing technologies. The report covers four research areas: (1) Identifying charging infrastructure requirements for existing facilities based on flight operational parameters, potential use cases, charging strategy, and other constraints. (2) Assessing sites on power availability to meet charging demand, the impact on grid infrastructure, potential hazards, and cybersecurity needs, and using technoeconomic analysis to identify opportunities for onsite distributed energy resources, primarily solar photovoltaics and battery energy storage systems. (3) Calculating greenhouse gas emission based on total energy consumption attributable to each site. (4) Analyzing the job and economic development impact for sites adopting new infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗