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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessment of Climate Change Impacts on Renewable Energy Resources in Western North America

We examine a 25 km resolution climate model dataset to evaluate how regional climate change impacts solar and wind energy under a high-emission scenario. Our study considers the Western Electricity Coordinating Council (WECC) region, which covers the western United States and southwestern Canada, focusing specifically on locations with existing solar and wind infrastructure. First, we conduct a historical model comparison of solar and wind energy capacity factors to highlight model uncertainties across the study area. Using future climate projections, we then assess the seasonal patterns of solar and wind capacity factors for three timeframes: historical, mid-century, and end of century. Additionally, we estimate the frequency of solar and wind resource droughts during these periods for the entire WECC and its five operational subregions, finding that certain subregions are more susceptible to energy droughts due to limited renewable resources. Finally, we present day-ahead capacity factor forecasts to support energy storage planning and provide estimates of offshore wind energy capacity within the WECC. Our results indicate that offshore wind capacity factors are nearly twice as high as onshore values, with less seasonal variation, which suggests that offshore wind could offer a more consistent renewable energy supply in the future.

climate change↗
DOE OSTI2023

Power Supply Options for the Marpi Landfill, Saipan: Feasibility Study

The Marpi Landfill, located on the northern end of the island of Saipan in the Commonwealth of the Northern Mariana Islands (CNMI), is powered by an on-site diesel generator that only operates when the landfill is open and staffed. The CNMI Office of Planning and Development (OPD) aspires to provide the Marpi Landfill with 24-hour power availability despite its remote location and to increase the use of sustainable energy within the CNMI. CNMI has a 20% target for renewable energy consumption, as documented in Sustainable Development Goal #7 in the Comprehensive Sustainable Development Plan (OPD 2021) and the renewable portfolio standard (GPO 2014). To accomplish these goals, the U.S. Department of Energy, through its Interagency Reimbursable Work Agreement with the Federal Emergency Management Agency, funded this feasibility study to assess and prioritize power supply options for the landfill. The availability of solar and wind resources varies seasonally, as does the load. A BESS can help to balance mismatches between generation and load on short (hourly or daily) timescales, but not across seasons. The microgrid scenarios evaluated for Marpi consider options for technology combinations that will both meet the load and utilize available resources, despite the challenge presented by higher loads and lower solar and wind availability during the rainy season, depicted in Figure ES-2. The seven scenarios evaluated are summarized in Table ES-1. Each scenario’s configuration was optimized to include component capacities that reduce capital and operating costs, meet the load, and minimize carbon emissions, as feasible. The costs and levelized cost of energy (LCOE) shown do not assume the use of any grant funding or incentives, although these options were also evaluated. To assist with decision-making, a prioritization matrix (Table ES-3) was created to compare the microgrid scenarios evaluated in this feasibility study according to various stakeholder priorities. The prioritization metrics were chosen based on discussions with OPD and will be finalized through stakeholder feedback. The scenarios were given a score between 1 and 7 for each prioritization metric (the lower the score, the higher the priority), and total scores were calculated using assigned weights based on the relative priority of each metric. The total scores were then ranked to produce a prioritized list of microgrid scenarios based on the metrics most important to the project stakeholders. As shown, scenario 4 (100 kW of solar PV, a 75 kW/300 kWh BESS, and 160 kW of diesel generation) ranks highest.

14 SOLAR ENERGY↗
DOE OSTI2021

Sodar - Brady, TX - Processed Data

This dataset was produced from the raw sodar .txt files from the Brady, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - Jayton, TX - Processed Data

This dataset was produced from the raw sodar .txt files from the Jayton, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - Reagan, TX - Processed Data

This dataset was produced from the raw sodar .dat files from the Reagan, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - Colorado City, TX - Processed Data

This dataset was produced from the raw sodar .txt files from the Colorado City, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - Lubbock, TX - Processed Data

This dataset was produced from the raw sodar .mnd files from the Lubbock, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - Cleburne, TX - Processed Data

This dataset was produced from the raw sodar .dat files from the Cleburne, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - Ozona, TX - Processed Data

This dataset was produced from the raw sodar .dat files from the Ozona, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2021

Sodar - St. James, MN - Processed Data

This dataset was produced from the raw sodar .wtb files from the St. James, MN site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location.

17 WIND ENERGY↗
DOE OSTI2017

Surface wind energy trends near Taiwan in winter since 1871

The tropical surface wind speed in boreal winter reaches a maximum near Taiwan. This stable wind resource may be used for future clean energy development. How this surface wind energy source has changed in past 141 years is investigated using the 20 th century reanalysis dataset and CMIP5 models. Our observational analysis shows that the surface wind speed experienced a weakening trend in the past 141 years (1871 - 2010). The average decreasing rate is around -1.4 m s -1 per century. The decrease is primarily attributed to the relative sea surface temperature (SST) cooling in the subtropical North Pacific, which forces a large-scale low-level anti-cyclonic circulation anomaly in situ and is thus responsible for the southerly trend near Taiwan. The relative SST trend pattern is attributed mainly to the greenhouse gas effect associated with anthropogenic activities. The southerly trend near Taiwan is more pronounced in the boreal winter than in summer. Such seasonal difference is attributed to the reversed seasonal mean wind, which promotes more efficient positive feedback in the boreal winter. The CMIP5 historical run analysis reveals that climate models capture less SST warming and large-scale anti-cyclonic circulation in the subtropical North Pacific, but the simulated weakening trend of the surface wind speed near Taiwan is too small.

17 WIND ENERGY↗
DOE OSTI2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

17 WIND ENERGY↗
DOE OSTI2017

Wind and solar resource data sets

The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental-scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research.

14 SOLAR ENERGY↗
DOE OSTI2018

Analyzing storage for wind integration in a transmission-constrained power system

High levels of energy from variable generation sources such as wind and solar photovoltaics (PV) can result in significant curtailment, in which the wind and PV energy cannot be used to serve demand. Adding transmission and energy storage can assist in reducing renewable curtailment, but the relative merits of each enabling technology individually or combined is not well understood. Thus, we compare the role of transmission and storage in reducing curtailment, as well as reducing generation costs from conventional sources. Using a high-fidelity model of the electric power grid, we examine a scenario in which the western portion of the U.S. and Canada reaches 37% energy from wind and 12% energy from solar PV. In the case studied, we find that transmission is generally more effective than energy storage in reducing curtailment, due to the curtailment patterns of wind. However, the interaction between transmission and energy storage shows that the two technologies act symbiotically, meaning that their combined energy value is greater than that of each individually. This analysis demonstrates that fully realizing the benefits of wind resources located far from demand centers will require an effective method to deliver wind power at the right times to the right locations.

17 WIND ENERGY↗
DOE OSTI2025

Examining future changes in coastal low-level jet properties offshore California through dynamical downscaling

The coastal low-level jet, or coastal low-level jet (CLLJ), is a synoptically-forced meteorological feature frequently present offshore the western United States (U.S.). Characterized by a wind speed maximum that resides at the top of the marine boundary layer, the CLLJ is largely controlled by the location and strength of the North Pacific High (NPH) as well as the coastal geometry. Considering the rich wind resource available in this offshore region, the Bureau of Ocean Energy Management identified wind energy lease areas offshore California and supported the deployment of two U.S. Department of Energy wind lidar buoys near Morro Bay and Humboldt. Despite our relatively good understanding of the fundamental mechanisms responsible for large-scale CLLJ properties offshore the western U.S., future changes in CLLJ characteristics are less clear. To address this research challenge, and ultimately to better inform future wind turbine deployments, we use simulations driven by three global climate models (GCMs). We apply self-organizing maps to the model outputs for a historical and two future climate periods to show the range of NPH regimes that support CLLJ conditions during the warm seasons, as well as the subtle contribution from land-falling cyclones approaching the mainland during the cold seasons. Compared to the historical period, the three GCM-driven simulations agree that CLLJ conditions will become more (less) prevalent from central California northward (southward). They agree less with respect to future changes in maximum CLLJ wind speeds and CLLJ heights. However, after considering model biases present during the historical period, wind speeds between the models are actually more similar during the 2070–2095 period than during the historical period. The potential combination of more frequent CLLJ conditions characterized by relatively consistent wind speeds occurring at lower heights across northern California suggests that the Humboldt lease area may be ideal for a long-term wind turbine deployment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Wind/Wave Misalignment in the Loads Analysis of a Floating Offshore Wind Turbine: Preprint

Wind resources far from the shore and in deeper seas have encouraged the offshore wind industry to look into floating platforms. The International Electrotechnical Commission (IEC) is developing a new technical specification for the design of floating offshore wind turbines that extends existing design standards for land-based and fixed-bottom offshore wind turbines. The work summarized in this paper supports the development of best practices and simulation requirements in the loads analysis of floating offshore wind turbines by examining the impact of wind/wave misalignment on the system loads under normal operation. Simulations of the OC3-Hywind floating offshore wind turbine system under a wide range of wind speeds, significant wave heights, peak-spectral periods and wind/wave misalignments have been carried out with the aero-servo-hydro-elastic tool FAST [4]. The extreme and fatigue loads have been calculated for all the simulations. The extreme and fatigue loading as a function of wind/wave misalignment have been represented as load roses and a directional binning sensitivity study has been carried out. This study focused on identifying the number and type of wind/wave misalignment simulations needed to accurately capture the extreme and fatigue loads of the system in all possible metocean conditions considered, and for a down-selected set identified as the generic US East Coast site. For this axisymmetric platform, perpendicular wind and waves play an important role in the support structure and including these cases in the design loads analysis can improve the estimation of extreme and fatigue loads. However, most structural locations see their highest extreme and fatigue loads with aligned wind and waves. These results are specific to the spar type platform, but it is expected that the results presented here will be similar to other floating platforms.

17 WIND ENERGY↗
DOE OSTI2021

Climate Change Mitigation Potential of Wind Energy

Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewable electricity generation source is thus already playing a role in reducing greenhouse gas emissions from the energy sector. Here we document trends within the industry, examine projections of future installed capacity increases and compute the associated climate change mitigation potential at the global and regional levels. Key countries (the USA, UK and China) and regions (e.g., EU27) have developed ambitious plans to expand wind energy penetration as core aspects of their net-zero emissions strategies. The projected climate change mitigation from wind energy by 2100 ranges from 0.3–0.8 °C depending on the precise socio-economic pathway and wind energy expansion scenario followed. The rapid expansion of annual increments to wind energy installed capacity by approximately two times current rates can greatly delay the passing of the 2 °C warming threshold relative to pre-industrial levels. To achieve the required expansion of this cost-effective, low-carbon energy source, there is a need for electrification of the energy system and for expansion of manufacturing and installation capacity.

17 WIND ENERGY↗
DOE OSTI2023

Modeling Annual Electricity Production and Levelized Cost of Energy from the US East Coast Offshore Wind Energy Lease Areas

Offshore wind energy development along the East Coast of the US is proceeding quickly as a result of large areas with an excellent wind resource, low water depths and proximity to large electricity markets. Careful planning of wind turbine deployments in these offshore wind energy lease areas (LA) is required to maximize power output and to minimize wake losses between neighboring wind farms as well as those internal to each wind farm. Here, we used microscale wind modeling with two wake parameterizations to evaluate the potential annual energy production (AEP) and wake losses in the different LA areas, and we developed and applied a levelized cost of energy (LCoE) model to quantify the impact of different wind turbine layouts on LCoE. The modeling illustrated that if the current suite of LA is subject to deployment of 15 MW wind turbines at a spacing of 1.85 km, they will generate 4 to 4.6% of total national electricity demand. The LCoE ranged from $68 to $102/MWh depending on the precise layout selected, which is cost competitive with many other generation sources. The scale of the wind farms that will be deployed greatly exceed those currently operating and mean that wake-induced power losses are considerable but still relatively poorly constrained. AEP and LCoE exhibited significant dependence on the precise wake model applied. For the largest LA, the AEP differed by over 10% depending on the wake model used, leading to a $10/MWh difference in LCoE for the wind turbine layout with 1.85 km spacing.

58 GEOSCIENCES↗