CO2 Storage Potential of the MAFLA Shelf, Eastern Gulf of Mexico
Southeast Offshore Storage Resource Assessment (SOSRA): Storage Potential
Pashin, Jack↗
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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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Geological characterization and CO2 Storage Potential of Cretaceous sandstone on the eastern Gulf of Mexico continental shelf
Southeast Offshore Storage Resource Assessment (SOSRA): EGOM characterization
Chandra, Avinash↗
SOSRA Eastern Gulf of Mexico Region
Southeast Offshore Storage Resource Assessment (SOSRA): Eastern Gulf of Mexico
Pashin, Jack↗
Geological characterization of Cretaceous sandstone on the eastern Gulf of Mexico continental shelf: CO2 sequestration potential of Cretaceous Formations
Southeast Offshore Storage Resource Assessment (SOSRA): Geological characterization
Chandra, Avinash↗
Offshore CO2 storage potential of the MAFLA continental shelf, eastern Gulf of Mexico
Southeast Offshore Storage Resource Assessment (SOSRA): MAFLA shelf potential
Pashin, Jack↗
Accomplishments and Mid-Year Performance Report: Wind Energy Program Fiscal Year 2020
Through transformative science and innovation, the U.S. Department of Energy's (DOE's) National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory's (NREL's) Flatirons Campus helps lead the way to a sustainable energy future that powers America with significant levels of reliable, low-cost, and accessible wind energy. Since 1976, NREL has provided an ideal environment for the research and development (R&D) of advanced energy technologies through: Wind energy resource assessments, World-class research facilities, High-performance computing and modeling, Data and technology analysis, Manufacturing breakthroughs, Environmental analysis and wildlife conservation efforts, Education and training programs for a future workforce. These activities enable the innovations needed to advance U.S. wind systems, address market and deployment barriers, and drive down the cost of wind energy with more efficient, more reliable, and more predictable wind energy systems. This report provides an overview of the achievements NREL delivered on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during the first and second quarters of Fiscal Year (FY) 2020.
49 EE - Wind and Water Power Program - Wind (EE-4W↗
Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services
Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.
numerical weather prediction↗
1.2.2.405 HydroWIRES Topic D1: Capacity Expansion Model (CEM) Enhancements
Long-term grid planning tools have difficulty representing detailed hydropower operating characteristics, which depend not only on technological specifications but also on water management practices and regulations. As a result, the value of hydropower is incompletely characterized, and the potential role of hydropower in the performance and resiliency of the future electric grid is not fully understood. This work will fill that gap by developing new ways to represent hydropower resource, technology, and operational characteristics in electric sector capacity expansion models and implementing them in the open-source version of the National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS) model. ReEDS is a well-established national scale grid planning tool used since 2003 by the U.S. Department of Energy and others to explore the evolution of the U.S. electric sector. Improvements will include a comprehensive national resource assessment for pumped storage hydropower and methods for modeling multiple hydropower technology categories characterized by technical, regulatory, and economic characteristics. The project will provide guiding principles and strategies for improving hydropower modeling in capacity expansion models and deliver a first-of-its kind versatile PSH dataset. All data, code, and methods will be publicly available, allowing the industry to better identify the value of hydropower in the future electricity system and make more informed planning decisions.
capacity expansion↗
Ahtna Tribal Energy Planning and Technical Assistance Project (Final Report)
Ahtna Intertribal Resource Commission (AITRC) provided technical assistance and capacity building activities for eight (8) tribal communities in the Ahtna region to: 1. reduce energy costs; 2. improve community wildfire readiness and biomass utilization; 3. provide local food security; and 4. reduce unemployment, poverty and outmigration in the Ahtna villages. AITRC provided community education on energy needs, conservation and efficiency, issues and options, assisted the tribes in identifying appropriate renewable energy systems, and increased their capacity to implement successful energy projects that employed local tribal members. AITRC also successfully assisted the member Tribes and Alaska Native Corporations in pursuing additional funding to advance their energy goals and projects, including Tribal Resiliency Planning, community biomass heating and wood resource assessment projects, and hydroelectric.
09 BIOMASS FUELS↗
Evolution of a Geological Model for Co-Producing Electricity at the Blackburn Oil Field, Nevada: Preprint
Oil fields characterized by significant proportions of hot water in the reservoir offer a unique opportunity for geothermal energy co-production and utilization. Development of an accurate geological model is essential in understanding the subsurface. However, this is a challenge at the preliminary stages of resource assessment when data on the subsurface parameters and subterranean fault network is scarce. Our focus in this paper is to explore a methodology for developing an accurate conceptual geological model of the complexly faulted reservoir at the Blackburn Field, Nevada, to create a numerical simulation that will eventually be used to assess the potential of co-producing electricity. There are four steps to the evolution of this model: (1) collection of public data and research on the geological setting of the system; (2) digitization of seismic time-interpretation and cross-sections to create the most representative model of the subsurface; and (3) creation of a natural-state numerical model to simulate the thermal gradient through the subsurface. Utilizing publicly available data of the geological system and Leapfrog conceptual modeling software, we refined a preliminary model to be more representative of the subsurface geology of an active geothermal exploration stage project. The conceptual geological model developed has been imported into VOLSUNG reservoir modeling software to create a natural-state model, further characterizing the Blackburn Field reservoir's thermal gradient. In the future, new seismic data will be utilized to further evolve the conceptual geological model for numerical simulations.
Basin and Range province↗
From Subsurface to System: Offshore CCS Development for the Northeastern U.S. Atlantic Shelf
This study evaluated offshore carbon sequestration potential and infrastructure design along the mid-north Atlantic outer continental shelf, addressing the limited onshore storage options in the northeastern U.S. The objective was to define viable CCS pathways by integrating geological characterization, reservoir modeling, and system-level engineering for decarbonizing regional industrial sources. The work was conducted under US DOE grant FE0032407 for the Regional Initiatives and builds on a previous prospective resources assessment project by Battelle.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Fast All-sky Radiation Model for Solar applications (FARMS) 2023 [SWR-16-18]
The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.
Xie, Yu↗
Hawaii Play Fairway Analysis Results
Probability grid of Hawaii Play Fairway Analysis results. This analysis was aimed at assessing the geothermal resource potential throughout Hawaii.
15 GEOTHERMAL ENERGY↗
Fisheries resource identification and assessment studies
Program coordination activities were shifted from the Space Oceanography Program of NAVOCEANO to the National Environmental Satellite Service as part of NOAA in October 1970. Program activities in remote sensing continued in the development of low-light-level image intensifiers, spectrometers, aerial photography, and lasers for the location, identification, and quantification of living marine resources at or near the sea surface. Other studies included the development of a biologically controlled impoundment for remote sensor investigations and limited activities in fish oil film research. In addition to these remote sensing studies, the National Marine Fisheries Service (NMFS) program at the Mississippi Test Facility is participating in space oceanography studies related to fisheries and in the ERTS-A and Skylab experiments. Aspects of the NMFS program related to fisheries resource identification and assessment during the period 1970 and 1971 are discussed.
Stevenson, W. H.↗
Central Valley Water Resources: Improving California Groundwater Assessments using GRACE and InSAR Datasets for Water Resource Management
California’s Central Valley is one of the most productive agricultural areas in the world, producing approximately $20 billion in crops annually. The recent California droughts of 2007-2010 and 2012-2019 resulted in increased groundwater pumping in the Central Valley to adequately irrigate farmland. Overdrafting of the Central Valley aquifer results in groundwater depletion, land subsidence, and permanent loss of groundwater storage. In 2014, depletion of groundwater led the state of California to enact the Sustainable Groundwater Management Act (SGMA),requiring critically overdrafted, high, and medium priority sub-basins to reach sustainable levels of groundwater pumping and recharge by 2042. SGMA allows local Groundwater Sustainability Agencies (GSAs) the authority to create Groundwater Sustainability Plans (GSPs) at the sub-basin level. To assist California’s Department of Water Resources (DWR), this project quantified groundwater change and land subsidence in Central Valley sub-basins with sparse or unreliable well and Geographic Positioning Systems (GPS) data. This was done using NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE FollowOn (GRACE-FO), and interferograms derived from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Advanced Land Observing Satellite 2 (ALOS-2)Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2). Time series of the GRACE and InSAR data were compared with well and GPS data in data-dense sub-basins to determine the feasibility of these datasets for groundwater storage and subsidence monitoring. We found that GRACE and InSAR data are effective tools for determining groundwater change and land subsidence and can be used on their own to monitor sub-basins in the absence of well and GPS data
Water Resources↗
Central Valley Water Resources: Improving California Groundwater Assessments using GRACE and InSAR Datasets for Water Resource Management
California’s Central Valley is one of the most productive agricultural areas in the world, producing approximately $20 billion in crops annually. The recent California droughts of 2007-2010 and 2011-2017 resulted in increased groundwater pumping in the Central Valley to adequately irrigate farmland. Overdrafting of the Central Valley aquifer results in groundwater depletion, land subsidence, and permanent loss of groundwater storage. In 2014, depletion of groundwater led the state of California to enact the Sustainable Groundwater Management Act (SGMA), requiring critically overdrafted, high, and medium priority sub-basins to reach sustainable levels of groundwater pumping and recharge by 2042. SGMA allows local Groundwater Sustainability Agencies the authority to create Groundwater Sustainability Plans at the sub-basin level. To assist California’s Department of Water Resources, this project quantified groundwater change and land subsidence in Central Valley sub-basins with sparse or unreliable well and GPS data. This was done using NASA’s Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and interferograms derived from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2). Time series of the GRACE and InSAR data were compared with well and GPS data in data-dense sub-basins to determine the feasibility of these datasets for groundwater storage and subsidence monitoring. We found thatGRACE and InSAR data are effective tools for determining groundwater change and land subsidence and can be used on their own to monitor sub-basins in the absence of well and GPS data.
Water Resources↗
Joint Resource Modeling and Assessment for Hybrid Distributed Solar and Wind Systems
The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.
joint modeling↗
Simulating competition in the US bioeconomy to produce hard‐to‐electrify transportation fuels using limited biomass resources
This study presents a novel bioeconomy optimization framework, BiOpt, designed to address critical questions regarding the strategic use of limited US biomass resources for biofuel production. By integrating detailed techno-economic analyses, life cycle assessments, and resource assessment data, BiOpt optimizes resource distributions across competing technologies to maximize economic performance and/or minimize greenhouse gas emissions. Using feedstock scenarios from the 2023 Billion Ton Study, the analysis explores optimal biomass allocations across sustainable aviation fuel, diesel, and marine biofuel conversion pathways given varying production targets and policy incentives. Results demonstrate distinct feedstock preferences and pathway utilizations when prioritizing economic returns vs. emissions reductions. For instance, fats, oils, and greases were highly favored in cost-optimized scenarios, while low-carbon feedstocks such as wet waste dominated greenhouse gas-minimized strategies. The findings underscore the pivotal role of policy incentives and technological advances in shaping biofuel supply chains and provide actionable insights for scaling sustainable biofuel production to decarbonize hard-to-electrify sectors. This framework offers a robust tool for policymakers and stakeholders to evaluate biofuel strategies that balance energy output, economic viability, and environmental impact.
09 BIOMASS FUELS↗