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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

AquaPV: Regulatory and Environmental Considerations for Floating Photovoltaic Projects Located on Federally Controlled Reservoirs in the United States

To meet the nation's decarbonization goals, the U.S. Department of Energy's Solar Futures study forecasts that installed solar photovoltaic (PV) capacity must increase nearly tenfold, from 80 gigawatts (GW) in 2020 to approximately 760 GW cumulative installed capacity by 2035. Ground-mounted PV is expected to dominate future solar deployment and will require more than 3.5 million acres of land to meet annual demand projections (of nearly 45 GW) by 2030. However, various competing demands for land (e.g., agricultural production, conservation) and high land acquisition costs in specific locations could be challenges to meeting future PV demand solely with ground-mounted PV deployment. Floating photovoltaics (FPV) may be an alternative in locations where ground-mounted PV is not feasible and aid in reaching the nation's PV deployment and decarbonization goals. FPV is a newer siting approach in which a PV array is affixed to a floating apparatus and sited on a water body like a reservoir behind a dam. FPV systems may be stand-alone or co-located at new or existing hydroelectric facilities or pumped storage hydropower (PSH) facility reservoirs. Co-located FPV systems may or may not be operationally paired and work in tandem with the hydroelectric or PSH facility. This report provides novel analysis to understand the opportunities and challenges associated with developing stand-alone and co-located FPV projects on reservoirs in the United States. Specifically, the report explores potential environmental and energy benefits and environmental impacts associated with the siting, construction, and operation of FPV projects. The report also identifies and analyzes U.S. federal- and state-issued permits and authorizations required by federal laws to understand the licensing pathways and regulatory requirements for FPV projects sited on reservoirs licensed by the Federal Energy Regulatory Commission and on powered and non-powered reservoirs owned by the Bureau of Reclamation or U.S. Army Corps of Engineers.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2024

Photovoltaic and Cost Analysis for Winston-Salem, North Carolina

This study assesses the feasibility of solar installations at various sites in Winston-Salem, focusing on factors such as solar resource availability, electricity costs, and rooftop area for photovoltaic systems. The analysis begins by estimating daily energy requirements based on annual electricity use, followed by adjusting for seasonal and operational fluctuations. Using regional solar data from the National Solar Radiation Database (NSRDB), we calculate average peak sun hours to determine effective system sizing. A parametric approach using the System Advisor Model (SAM) refines this sizing process, incorporating a safety margin of 1.2 to address demand peaks. Each PV system is designed to fit available rooftop space, and a coverage threshold of 70% is identified as optimal for maximizing cost savings and sustainability. This threshold allows installations to meet substantial energy demands, supporting energy resilience and enhancing economic returns.

14 SOLAR ENERGY↗
NASA NTRS1992

Crustal deformation along the San Andreas, California

The goal is to achieve a better understanding of the regional and local deformation and crustal straining processes in western North America, particularly the effects of the San Andreas and nearby faults on the spatial and temporal crustal deformation behavior. Construction of theoretical models based on the mechanics of coupled elastic plate, viscoelastic foundation and large scale crack mechanics provide a rational basis for the interpretation of seismic and aseismic anomalies and expedite efforts in forecasting the stability of plate boundary deformation. Special focus is placed on the three dimensional time dependent surface deformation due to localized slippage in a elastic layer coupled to a visco-elastic substrate. The numerical analysis is based on a 3-D boundary element technique. Extension to visco-elastic coupling demands the derivation of 3-D time dependent Green's function. This method was applied to analyze the viscoelastic surface displacements due to a dislocated embedded patch. Surface uplift as a function of time and position are obtained. Comparisons between surface uplift for long and short dislocated patches are made.

Li, Victor C.↗
NASA NTRSDate not supplied

The GeoGLOWS Project: Essential Water Variables and Observations – Expected Value Chain, Products, Examples

Essential Water Variables (EWV) were developed over the last decade to ensure that key datasets were made available for closing the global water budget, and addressing the many international actions that require water data. Satellite, surface, and numerical model data all have a role in providing EWVs, and it is a key point that these data must be merged and then fed into decision support applications to answer all the needs of the panoply of user communities. The more specialized the applications become, the higher the demand for development and maintenance resources. Some examples of applications include the NASA Goddard Giovanni, which provides on-line data access and simple analysis; the Global Flood Monitor, which focuses on precipitation-driven flooding; the NASA Landslide Hazard Assessment for Situational Awareness, which similarly treats landslides; and the Global Streamflow Forecasting Project, which provides multiple decision support interfaces, as well as a general toolkit for building new interfaces.

Essential Water Variables↗
DOE OSTI2024

helios: An R package to process heating and cooling degrees for GCAM

helios is an open-source R package that estimates population-weighted heating and cooling degree-hours (HDH and CDH) and degree-days (HDD and CDD) at various temporal (e.g., energy dispatch segments, monthly, yearly) and spatial scales (e.g., U.S. states, global political regions, countries). The degree hour and degree day outputs from helios are used to inform electricity demand load in the Global Change Analysis Model (GCAM) as well as in GCAM-USA (which is the version of GCAM with U.S. state-level details). helios uses a workflow with four steps: processing raw data; calculating heating and cooling degrees; visualizing performance diagnostics; and outputing results in various formats. There are two sources of widely-used climate data compatible with helios: (1) hourly climate data with 12-km resolution that are dynamically downscaled with the Weather Research and Forecasting (WRF) model and projected using a thermal global warming (TGW) approach; and (2) daily climate data with 0.5-degree resolution from the Coupled Model Intercomparison Project (CMIP) that is bias-adjusted and statistical downscaled by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In summary, helios is a model that standardizes methodology of heating and cooling degrees-hours and degree-days using publicly available data and advance the understanding of the impact of spatial and temporal temperature variability on building energy services.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Reforestation and Crop Land Conversion Impacts on Future Regional Air Quality in the Southeastern U.S.

Land in the southeastern U.S. is expected to change, e.g., given the potential demand to develop forest-to-fuel technologies or, conversely, cropification of current forests to increase food production. Possible future PM(sub 2.5) and O3 air quality for two land use/land cover change (LULCC) scenarios, reforestation and cropland conversion, are compared to a reference case scenario for the year 2050 using the Weather Research and Forecasting (WRF) and Community Multi-scale Air Quality (CMAQ) models. Changes in air quality driven by changes in climate, deposition and emissions relating to the LULCC are investigated. Reforestation in the Southeast tends to decrease the ambient O3mixing ratio while slightly increasing summertime PM(sub 2.5) in the Southeastern U.S. Results of a climate and deposition (CD) sensitivity simulation are provided for the two alternative LULCC scenarios to isolate the impact of changing climate and deposition on PM(sub 2.5) and O3 air quality. The sensitivity results indicate that deposition and emissions changes associated with reforestation impact O3 and PM(sub 2.5concentrations as much as, and in most cases more than, changes in meteorology. Conversion of forest to cropland in the Southeast, on the other hand, tends to increase O3and increase PM(sub 2.5) year-round. Cropland conversion leads to increased NOX emissions and increases in the 4th highest maximum daily 8-h O3(MDA8) of the year by up to 10 ppb despite the tendency for increased deposition and decreased temperature to reduce the MDA8 mixing ratio. The results of this study show that O3and aerosol concentrations are sensitive to reforestation and cropland conversion in the Southeast and these land use changes should be considered in air quality management plans. Further, they show the sensitivity of such calculations to land cover properties.

deposition↗
NASA NTRS2024

How Can Future Climate Change Affect the Corn Production System in Nebraska, USA?

The increasing temperatures and changing precipitation patterns have affected both crop yields and water requirements challenging agricultural systems in sustaining high crop yields and ensuring water and food security. In the present study, we evaluated the impact of climate change on the corn production under irrigated and rainfed cropping systems in Nebraska considering four climate zones until 2100 as a case study for the US Corn Belt. Additionally, suggests ways to reduce the impact of climate change on corn yield and water resources. Five global circulation model datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) interconnected to Shared Socioeconomic Pathways (SSPs), were selected. Three future periods: near (2020–2039), mid (2040–2069), and far future (2070–2100) under SSP226, SSP370, and SSP585 scenarios were used to predict the climate changes, crop cycle length, crop yield, and crop water requirement. Additionally, we input annual CO2 concentration scenarios for each SSP scenario to model changes from 2015 to 2100 using the CERES-Maize model. Temperatures are forecasted to rise, precipitation to decline, and crop cycle length to decrease, particularly for SSP370 (up to 49% yield decrease) and SSP585 (up to 67% yield decrease) by 2070–2100. Yields are expected to decline across all scenarios and periods for irrigated and rainfed fields due to the reduction in crop cycle length. Crop water demand is projected to increase, with rainfed systems facing heightened impact due to reduced precipitation. Addressing climate change in Nebraska requires actions like developing new corn varieties to longer the crop season, transitioning to efficient irrigation systems, improving irrigation management for water and food security, and reducing the risk of conflicts over its use. These results open possibilities for discussion about the actions that can be taken to cope with climate change and guarantee water and food security in the US Corn Belt.

CMIP6↗
DOE OSTI2023

BSM (Bioenergy Scenario Model) 2023 FKA: Biomass Scenario Model [SWR-09-09]

The U.S. Department of Energy's (DOE's) Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.

Bush, Brian↗
NASA NTRS1998

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗
DOE OSTI2024

VPP Participation Models: What is the Objective?

Virtual power plants (VPPs) can provide grid services by aggregating and controlling many distributed energy resources. However, their design, operation, level of participation, and outcomes depend on the regulatory and market environments they operate in, DER owner preferences, grid service value, and technical capabilities (e.g., communications, forecasting, controls, user interfaces). Although VPPs are inherently complex, they could enable high shares of behind-the-meter solar plus storage and reduce the cost of electrifying, e.g., personal vehicles and space heating.

demand response↗
DOE OSTI2023

USAID Colombia Young Leaders Workforce Training Program Action Plans: Forecasting Distributed Photovoltaic Adoption in Barranquilla, Colombia

As part of the U.S. Agency for International Development (USAID)-National Renewable Energy Laboratory (NREL) Young Leaders Workforce Training Program in Colombia, the Association of Renewable Energies Colombia (SER) participants leveraged their training and professional experience to develop an action plan for modeling the projected adoption of distributed solar photovoltaics (PV) out to 2050 for the city of Barranquilla, Colombia. This case study provides an overview of the key activities and outcomes of the Distributed Generation Market Demand Model (dGen™) Colombia project.

14 SOLAR ENERGY↗
DOE OSTI2023

A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. Here, this paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Distribution Capacity Expansion Planning: Current Practice, Opportunities, and Decision Support

The distribution utility industry and its engineers are experiencing monumental shifts in consumer needs and expectations. Characterizing future native loads as compared to net load demand for long-term capacity planning is especially difficult, as consumers are increasingly adopting prosumer technologies. This paper is the culmination of 5 months of utility interviews coordinated by the National Renewable Energy Lab (NREL) and Kevala, Inc. (Kevala) to better understand distribution capacity planning challenges. The interviews covered all aspects of capacity planning including load and DER forecasting, criteria for assessing system constraints, solution types, and organizational and decision-making structures. Our intent is to provide insight into distribution capacity planning decision support needs for utilities and the increasing number of stakeholders involved, from state and regulatory agencies to community and solution providers with interest in increasing their understanding in the distribution capacity planning process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Online energy consumption forecast for battery electric buses using a learning-free algebraic method

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.

14 SOLAR ENERGY↗
DOE OSTI2026

Powered by dGen Webinar [Slides]

NLR's Powered By Webinar Series featuring NLR's dGen Modeling Tool. The Distributed Generation Market Demand (dGenTM) model simulates customer adoption of distributed energy resources for residential, commercial, and industrial entities in the United States or other countries through 2050. The model enables analysis at multiple geographic levels (national, state, and utility, or below) and offers sophistication in representation of decision-making regarding economic and behavioral considerations. Analysts have used dGen to answer questions about load forecasting and integrated resource planning, policy analysis, locational value of distributed energy resources, and more. dGen is open source, and various energy organizations - including independent system operators, regional transmission organizations, and the California Energy Commission - use the model internally.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Introduction to Data Assimilation

Atmospheric data assimilation is a class of techniques used for producing descriptions of fields of air temperature, pressure, humidity, wind, etc. on a spatial grid or in terms of a finite functional representation. These are then used to initialize numerical weather forecasts or to analyze the atmosphere for other purposes. The techniques combine past, present, and even future observations in an approximate statistically optimal way. Various types of statistical or physically-based models and their corresponding adjoints are employed to relate diverse fields in both time and space and to relate what is observed to what is being analyzed. Computationally, the problem is very demanding and onstraining on the techniques that can be employed on a routine basis.

Atmospheric data assimilation↗