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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Bookending the Opportunity to Lower Wind’s LCOE by Reducing the Uncertainty Surrounding Annual Energy Production

Reducing the performance risk surrounding a wind project can potentially lead to a lower weighted-average cost of capital (WACC), and hence a lower levelized cost of energy (LCOE), through an advantageous shift in capital structure, and possibly also a reduction in the cost of capital. Specifically, a reduction in performance risk will move the 1-year P99 annual energy production (AEP) estimate closer to the P50 AEP estimate, which in turn reduces the minimum debt service coverage ratio (DSCR) required by lenders, thereby allowing the project to be financed with a greater proportion of low-cost debt. In addition, a reduction in performance risk might also reduce the cost of one or more of the three sources of capital that are commonly used to finance wind projects: sponsor or cash equity, tax equity, and/or debt. Preliminary internal LBNL analysis of the maximum possible LCOE reduction attainable from reducing the performance risk of a wind project found a potentially significant opportunity for LCOE reduction of ~$10/MWh, by reducing the P50 DSCR to its theoretical minimum value of 1.0 (Bolinger 2015b, 2014) and by reducing the cost of sponsor equity and debt by one-third to one-half each (Bolinger 2015a, 2015b). However, with FY17 funding from the U.S. Department of Energy’s Atmosphere to Electrons (A2e) Performance Risk, Uncertainty, and Finance (PRUF) initiative, LBNL has been revisiting this “bookending” exercise in more depth, and now believes that its earlier preliminary assessment of the LCOE reduction opportunity was overstated. This reassessment is based on two new-found understandings: (1) Due to ever-present and largely irreducible inter-annual variability (IAV) in the wind resource, the minimum required DSCR cannot possibly fall to 1.0 (on a P50 basis), and (2) A reduction in AEP uncertainty will not necessarily lead to a reduction in the cost of capital, meaning that a shift in capital structure is perhaps the best that can be expected (perhaps along with a modest decline in the cost of cash equity as new investors enter the market).

17 WIND ENERGY↗
DOE OSTI2017

Subalpine and alpine species range shifts with climate change: temperature and soil moisture manipulations to test species and population responses (Final Report)

The Alpine Treeline Warming Experiment (ATWE) was established in 2008-2009 to experimentally test how climate change would affect species ranges at and beyond the cold edge limits of forest growth. One key finding was that warming can hinder upslope tree migration by increasing seedling water stress and reducing seedling survival. We found evidence for more conservative traits in seedlings from high elevation tree populations, which may further slow tree expansion into the current alpine. In the alpine, warming may only increase productivity if summer rain increases, as dominant alpine life forms were negatively affected by heating unless water was added. The ATWE experimental design and focus on dynamic vegetation were novel, and the setting at the mountain treeline transition was important to understanding effects of climate variability and change on water resources, carbon cycling and biodiversity. We ended climate manipulations and removed associated infrastructure from the field in 2016 following 8 years of data collection. To date, 5 MS theses and 18 peer-reviewed papers have been published or accepted (including one that yielded the cover image in Global Change Biology, left), and 1 PhD thesis and at least 5 papers are in review or active development. Numerous past seasonal and long-term field staff have gone on to graduate school, and subsequently to research positions as university faculty or agency scientists. PI Kueppers continues collecting data on soil microclimate and seedling survival for long-lived tree species to fill a critical gap in knowledge regarding the seedling-to-sapling transition across species and elevations. Primary project data is housed on the project’s virtual server, organized according to topic. Published data and model code is available either through the project website or via other permanent archives. Our results are influencing understanding and modeling of climate and genetic constraints on dynamic vegetation change across the landscape, particularly in the mountains of the Western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Methods for Translating ReEDS Solutions to Production Cost Modeling Tools

Capacity expansion modeling tools are increasingly being utilized to investigate a wide range of potential future scenarios, particularly with high penetrations of variable and energy-constrained resources that may be operated differently than current dispatch paradigms. While capacity expansion models are particularly adept at making investment decisions for future years, they must make compromises in operational aspects to maintain computational tractability. However, it is of high interest to determine if such future systems would be able to maintain reliability during a variety of grid conditions, and to identify any potential operational challenges for these systems at finer temporal resolution than is typically captured in CEMs. As such, there is value in having an automated tool that can convert many CEM investment pathways into inputs for a more detailed production cost model, in this case the PLEXOS commercial software package. This paper describes the methodology used to make that translation for the NREL-developed ReEDS model, making use of two internally developed tools - PIDG and beetle - along with a set of processing scripts. We describe the current assumptions, data sets, and important operational characteristics of these translations along with an example of the extended analyses that may be done through this connection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Autonomous Energy Grids

Current frameworks to monitor, control, and optimize large-scale energy systems are becoming increasingly inadequate because of significantly high penetration levels of variable generation and distributed energy resources being integrated into electric power systems; the deluge of data from pervasive metering of energy grids; and a variety of new market mechanisms, including multilevel ancillary services. This paper outlines the concept of autonomous energy grids (AEGs). These systems are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, are extremely secure and resilient (self-healing), and can self-optimize in real time to ensure economic and reliable performance while systematically integrating energy in all forms. AEGs rely on cellular building blocks that can self-optimize when isolated from a larger grid and participate in optimal operation when interconnected to a larger grid. This paper describes the key concepts and research necessary in the broad domains of optimization theory, control theory, big data analytics, and complex system theory and modeling to realize the AEG vision.

autonomous energy grids↗
NASA NTRS2021

Cloud Detection over Sunglint Regions with Observations from the Earth Polychromatic Imaging Camera (EPIC)

With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤ 25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764nm to 780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4~10% and 4~6% in the glint center and granule edges respectively. The false positive rate is reduced by 10~17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.

EPIC↗
DOE OSTI2010

Evaluation of PV performance models and their impact on project risk.

Photovoltaic systems are often priced in $/W{sub p}, where Wp refers to the DC power rating of the modules at Standard Test Conditions (1000 W/m{sup 2}, 25 C cell temperature) and $ refers to the installed cost of the system. However, the true value of the system is in the energy it will produce in kWhs, not the power rating. System energy production is a function of the system design and location, the mounting configuration, the power conversion system, and the module technology, as well as the solar resource. Even if all other variables are held constant, the annual energy yield (kWh/kW{sup p}) will vary among module technologies because of differences in response to low-light levels and temperature. Understanding energy yield is a key part of understanding system value. System performance models are used during project development to estimate the expected output of PV systems for a given design and location. Performance modeling is normally done by the system designer/system integrator. Often, an independent engineer will also model system output during a due diligence review of a project. A variety of system performance models are available. The most commonly used modeling tool for project development and due diligence in the United States is probably PVsyst, while those seeking a quick answer to expected energy production may use PVWatts. In this paper, we examine the variation in predicted energy output among modeling tools and users and compare that to measured output.

14 SOLAR ENERGY↗
DOE OSTI2020

Probabilistic Assessment of High-Renewables Power Systems: Current Work and Future Directions

In a future with higher penetrations of variable and energy-limited resources, the dominant sources of resource adequacy risk begin to shift from (assumed) independent thermal unit outages to spatially- and temporally-correlated weather-driven phenomena. Traditional resource adequacy assessment and planning methods can be ill-suited to such scenarios. This presentation will highlight aspects of NREL's work in adapting and developing new probabilistic methods and tools in anticipation of the needs of the power system planning exercises of tomorrow.

17 WIND ENERGY↗
DOE OSTI2016

Assessment of high enthalpy geothermal resources and promising areas of Chile

This work aims to assess geothermal power potential in identified high enthalpy geothermal areas in the Chilean Andes, based on reservoir temperature and volume. In addition, we present a set of highly favorable geothermal areas, but without enough data in order to quantify the resource. Information regarding geothermal systems was gathered and ranked to assess Indicated or Inferred resources, depending on the degree of confidence that a resource may exist as indicated by the geoscientific information available to review. Resources were estimated through the USGS Heat in Place method. A Monte Carlo approach is used to quantify variability in boundary conditions. Estimates of total Indicated resource are confined to 3 geothermal systems; Apacheta, El Tatio and Tolhuaca, yielding a total value of 228 ± 154. MWe. The estimates of the total Inferred resources for Chile include 6 geothermal systems and yield a total value of 431 ± 321. MWe. Standard deviation reflects the high variability of reservoir specific parameters for each system. A set of 65 favorable geothermal areas are proposed as the most likely future development targets. Finally, eight of them have initial exploration results that suggest they are highly favorable targets as potential geothermal resources.

58 GEOSCIENCES↗
DOE OSTI2014

PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application’s throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based look-ahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from public and private IaaS clouds. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Commercial and Industrial Base Intermittent Resource Management Pilot

This scoping study summarizes the challenges with integrating wind and solar generation into the California's electricity grid. These challenges include: Smoothing intra-hour variability; - Absorbing excess renewable energy during over-generation periods; - Addressing morning and evening ramping periods. In addition, there are technical challenges to integrating retail demand response (DR) triggered by the wholesale conditions into the CAISO markets. The study describes the DR programs available to the consumers through the utilities in California and CAISO's ancillary services market because an integration of the wholesale and retail DR requires an understanding of these different offerings and the costs associated with acquiring them. Demand-side active and passive storage systems are proposed as technologies that may be used to mitigate the effects of intermittence due to renewable generation. Commercial building technologies as well as industrial facilities with storage capability are identified as targets for the field tests. Two systems used for ancillary services communications are identified as providing the triggers for DR enablement. Through the field tests, issues related to communication, automation and flexibility of demand-side resources will be explored and the performance of technologies that participate in the field tests will be evaluated. The major outcome of this research is identifying and defining flexibility of DR resources and optimized use of these resources to respond to grid conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Long-Term Resource Adequacy, Long-Term Flexibility Requirements, and Revenue Sufficiency

Variable generation (VG) can reduce market prices over time and also the energy that other suppliers can sell in the market. The suppliers that are needed to provide capacity and flexibility to meet the long-term reliability requirements may, therefore, earn less revenue. This chapter discusses the topics of resource adequacy and revenue sufficiency - that is, determining and acquiring the quantity of capacity that will be needed at some future date and ensuring that those suppliers that offer the capacity receive sufficient revenue to recover their costs. The focus is on the investment time horizon and the installation of sufficient generation capability. First, the chapter discusses resource adequacy, including newer methods of determining adequacy metrics. The chapter then focuses on revenue sufficiency and how suppliers have sufficient opportunity to recover their total costs. The chapter closes with a description of the mechanisms traditionally adopted by electricity markets to mitigate the issues of resource adequacy and revenue sufficiency and discusses the most recent market design changes to address these issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Drivers of the Resource Adequacy Contribution of Solar and Storage for Florida Municipal Utilities

Solar’s variable generation limits its contribution to reliably meeting peak demand, or its resource adequacy contribution. Energy storage is a leading option to increase solar’s resource adequacy contribution, yet the contribution from different configurations of solar and storage is not widely understood. We develop methods for exploring the primary drivers of an estimate of the resource adequacy contribution of solar and storage, and we apply the methods to a case study in Florida, where demand peaks in winter and summer. We find that the portion of solar nameplate capacity that contributes to resource adequacy—its capacity credit—is less than 50% and that it declines with increasing solar penetration. The capacity credit of storage, even though it is fully controllable by the system operator, strongly depends on the duration of storage. The capacity credit of 1 hour of storage can be less than the capacity credit of solar. Achieving a 90% capacity credit requires at least 4–5 hours of storage when storage capacity is small relative to the system peak. As storage deployment increases to 20% of the peak demand, 9 hours—and sometimes more than 10 hours—of storage are needed to achieve a 90% capacity credit. Increased solar deployment at the system level can increase storage’s capacity credit. Directly pairing solar and storage can also impact the capacity credit. Storage with a power rating similar to the solar inverter rating loses capacity credit when coupled with solar if its duration is more than 1–2 hours, because storage competes with solar for use of the inverter. On the other hand, there is no reduction in capacity credit when the storage is small relative to the solar inverter. The approach and tools developed here for exploratory analysis can be useful for many other utilities and regions grappling with similar preliminary questions, prior to evaluation of specific cases using more detailed and resource-intensive modeling.

14 SOLAR ENERGY↗
NASA NTRS1992

JIGSAW: Preference-directed, co-operative scheduling

Techniques that enable humans and machines to cooperate in the solution of complex scheduling problems have evolved out of work on the daily allocation and scheduling of Tactical Air Force resources. A generalized, formal model of these applied techniques is being developed. It is called JIGSAW by analogy with the multi-agent, constructive process used when solving jigsaw puzzles. JIGSAW begins from this analogy and extends it by propagating local preferences into global statistics that dynamically influence the value and variable ordering decisions. The statistical projections also apply to abstract resources and time periods--allowing more opportunities to find a successful variable ordering by reserving abstract resources and deferring the choice of a specific resource or time period.

Linden, Theodore A.↗
NASA NTRS1992

Possible biomedical applications and limitations of a variable-force centrifuge on the lunar surface: A research tool and an enabling resource

Centrifuges will continue to serve as a valuable research tool in gaining an understanding of the biological significance of the inertial acceleration due to gravity. Space- and possibly lunar-based centrifuges will play a significant and enabling role with regard to the human component of future lunar and martian exploration, both as a means of accessing potential health and performance risks and as a means of alleviating these risks. Lunar-based centrifuges could be particularly useful as part of a program of physiologic countermeasures designed to alleviate the physical deconditioning that may result from prolonged exposure to a 1/6-g environment. Centrifuges on the lunar surface could also be used as part of a high-fidelity simulation of a trip to Mars. Other uses could include crew readaptation to 1 g, waste separation, materials processing, optical mirror production in situ on the Moon, and laboratory specimen separation.

Cowing, Keith L.↗
DOE OSTI2017

Evaluation of the FIR Example using Xilinx Vivado High-Level Synthesis Compiler

Compared to central processing units (CPUs) and graphics processing units (GPUs), field programmable gate arrays (FPGAs) have major advantages in reconfigurability and performance achieved per watt. This development flow has been augmented with high-level synthesis (HLS) flow that can convert programs written in a high-level programming language to Hardware Description Language (HDL). Using high-level programming languages such as C, C++, and OpenCL for FPGA-based development could allow software developers, who have little FPGA knowledge, to take advantage of the FPGA-based application acceleration. This improves developer productivity and makes the FPGA-based acceleration accessible to hardware and software developers. Xilinx Vivado HLS compiler is a high-level synthesis tool that enables C, C++ and System C specification to be directly targeted into Xilinx FPGAs without the need to create RTL manually. The white paper [1] published recently by Xilinx uses a finite impulse response (FIR) example to demonstrate the variable-precision features in the Vivado HLS compiler and the resource and power benefits of converting floating point to fixed point for a design. To get a better understanding of variable-precision features in terms of resource usage and performance, this report presents the experimental results of evaluating the FIR example using Vivado HLS 2017.1 and a Kintex Ultrascale FPGA. In addition, we evaluated the half-precision floating-point data type against the double-precision and single-precision data type and present the detailed results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Statistical Characterization of Solar Photovoltaic Power Variability at Small Timescales: Preprint

Integrating large amounts of variable and uncertain solar photovoltaic power into the electricity grid is a growing concern for power system operators in a number of different regions. Power system operators typically accommodate variability, whether from load, wind, or solar, by carrying reserves that can quickly change their output to match the changes in the solar resource. At timescales in the seconds-to-minutes range, this is known as regulation reserve. Previous studies have shown that increasing the geographic diversity of solar resources can reduce the short term-variability of the power output. As the price of solar has decreased, the emergence of very large PV plants (greater than 10 MW) has become more common. These plants present an interesting case because they are large enough to exhibit some spatial smoothing by themselves. This work examines the variability of solar PV output among different arrays in a large ({approx}50 MW) PV plant in the western United States, including the correlation in power output changes between different arrays, as well as the aggregated plant output, at timescales ranging from one second to five minutes.

14 SOLAR ENERGY↗