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This short report provides a time-sensitive update on the 20-year wind resource data set (named "CA20") produced in 2020 by the National Renewable Energy Laboratory (NREL) for the outer continental shelf (OCS) off the coast of California.
This short report provides a time-sensitive update on the 20-year wind resource data set (named “CA20”) produced in 2020 by the National Renewable Energy Laboratory (NREL) for the outer continental shelf (OCS) off the coast of California.
Direct Normal Irradiance (DNI) is one of the most used quantities to quantify the magnitude of solar energy resource. The concept of DNI is often interpreted differently for ground measurements and solar forecasting by numerical weather prediction (NWP) models, leading to substantial bias during evaluation of DNI forecasts especially under cloudy-sky conditions. To eliminate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to provide a physics-based solution of solar radiation in the circumsolar region. The FARMS-DNI is implemented in the Weather Research and Forecasting model with solar extensions (WRF-Solar) to forecast day-ahead DNI in the north America. By comparing with conventional predictions from WRF-Solar and satellite observations from the National Solar Radiation Data Base (NSRDB), we found significant improvements in our prediction of DNI.
A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.
The National Solar Radiation Database (NSRDB), produced by the National Laboratory of the Rockies (NLR), provides high-resolution solar resource data for the contiguous United States (CONUS) using Geostationary Operational Environmental Satellite (GOES) East and West observations. This study evaluates the integration of multi-satellite data within the GOES-East/West overlap regions, where conventional longitude-based selection methods often produce an artificial boundary seam. Our results demonstrate that an advanced blending algorithm, which incorporates sun-satellite scattering angles and satellite viewing zenith angles, improves NSRDB accuracy and creates a spatially continuous dataset. Validation against ground-based irradiance measurements reveals reductions in both percentage error (PE) and normalized Root Mean Square Error (nRMSE), particularly in the central United States. The dynamical integration of multi-satellite data provides a robust foundation for more precise modeling of solar resource and improved spatiotemporal analysis of solar ramp across the CONUS.
There is currently a poor understanding and lack of workflow to understand the technical viability of carbon storage spatially. To address this gap, the multi-faceted Carbon Storage Technical Viability Approach (CS TVA) is being developed to incorporate CO2 storage resources, environmental and socio-economic justice (EJ/SJ) factors to enable more comprehensive assessments. The CS TVA includes a (1) matrix framework, (2) an integrated and labeled database, (3) a data availability assessment workflow, and (4) spatial data availability assessment results. This approach leverages spatial and data science analytics to communicate data density, uncertainty, and gaps. The workflow can be applied in whole or in part, based on user needs.
Island communities often struggle to establish and maintain traditional electric grids and are therefore heavily reliant on costly imported fossil fuels. In the case of Puerto Rico, these challenges are enhanced by extreme weather and other natural hazards that threaten the local electricity generation and transmission infrastructure. Ocean thermal energy conversion (OTEC) could play an important role in establishing a more resilient electrical grid in the region. Here, in this study, a detailed analysis is conducted to characterize the ocean thermal resource and power potential of OTEC in Puerto Rico based on a 14-year dataset of modeled ocean temperature. The assessment considers seasonal and interannual variability in the region's thermal resource and examines the operational limitations associated with minimal thermal gradients required to run a typical OTEC heat engine. Notably, the local thermal resource is found to be sensitive to El Niño-Southern Oscillation (ENSO) climate patterns, with La Niña conditions linked to greater OTEC power availability. Seven areas of opportunity are identified based on their resource potential and proximity to existing electrical distribution lines, including two that could benefit the nearby U.S. Virgin Islands. The greatest OTEC power potential is observed to the south of the main island of Puerto Rico in the Caribbean Sea with an estimated capacity of 138 MW for a plant pumping cold water from a depth of 1,000 m, or the equivalent amount of electricity required to power 219,000 households.
This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.
This report provides the results of a detailed analysis of the causes of high wind speed bias in the 20-year wind resource data set for offshore California that the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California Outer Continental Shelf, near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a 1-year period.
Recent development of unstructured-grid wave models demonstrates the advantage of using an unstructured-grid modeling framework in wave modeling, especially in coastal areas that have complex coastlines, islands, shallow-water bays, and estuaries. This study evaluated the performance of unstructured-grid models in comparison to structured-grid models for predicting the IEC wave energy resource parameters at a common test bed site. Model skill was assessed using a set of model performace error statistics. Sensitivity of open boundary conditions and ]wind forcing on model results, computational efficiency, and the advantage and limitation of the unstructured-grid model are summarized in the report.
Using performance data from some of the millions of installed photovoltaic (PV) modules with micro-inverters may afford the opportunity to provide ground-based solar resource data critical for developing PV projects. The method used back-solves for the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the micro-inverter ac production data. When the derived values of DNI and DHI were then used to model the performance of other PV systems, the annual mean bias deviations were within +/-4%, and only 1% greater than when the PV performance was modeled using high quality irradiance measurements. An uncertainty analysis shows the method better suited for modeling PV performance than using satellite-based global horizontal irradiance.
Using performance data from some of the millions of installed photovoltaic (PV) modules with micro-inverters may afford the opportunity to provide ground-based solar resource data critical for developing PV projects. The method used back-solves for the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the micro-inverter ac production data. When the derived values of DNI and DHI were then used to model the performance of other PV systems, the annual mean bias deviations were within +/- 4%, and only 1% greater than when the PV performance was modeled using high quality irradiance measurements. An uncertainty analysis shows the method better suited for modeling PV performance than using satellite-based global horizontal irradiance.
Using performance data from some of the millions of installed photovoltaic (PV) modules with micro-inverters may afford the opportunity to provide ground-based solar resource data critical for developing PV projects. The method used back-solves for the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the micro-inverter ac production data. When the derived values of DNI and DHI were then used to model the performance of other PV systems, the annual mean bias deviations were within plus or minus 4 percent, and only 1 percent greater than when the PV performance was modeled using high quality irradiance measurements. An uncertainty analysis shows the method better suited for modeling PV performance than using satellite-based global horizontal irradiance.
This report presents an updated “harmonization study” documenting the collaborative analysis of saline microalgae cultivation and conversion to fuels and products. Four national laboratory modeling teams reconvened to investigate the resource, economic, and environmental sustainability implications of integrated systems encompassing large-scale algae farms and conversion biorefineries. Relative to prior harmonization analyses conducted by these partners, the present effort focuses on more near-term technology potential based on the use of nutrient-replete, high-protein algal biomass compositions (more readily achievable today without sacrificing cultivation productivity) coupled with individual algae farms varying in size but generally smaller at 3,900 acres on average (more realistic in practice than a fixed 5,000-acre farm scale previously considered). Additionally, the present assessment adds further granularity around carbon dioxide (CO 2 ) sourcing and transport via carbon capture of nearby point sources, as well as handling of high-saline cultivation media and resultant blowdown/disposal processing. Finally, this assessment focuses on conversion opportunities to produce both fuel (prioritizing sustainable aviation fuel [SAF], in this case via hydrothermal liquefaction) and protein products for the food and feed markets, recognizing growing needs for such products.
The AC production data from Enphase Energy Inc. micro-inverters for 100 locations in five metropolitan areas were used to derive the location's direct normal irradiance and diffuse horizontal irradiance resource. These data were then used to model the energy production of nearby photovoltaic (PV) systems. Results had small bias errors, and for most locations the results were significantly better than when using satellite-based National Solar Radiation Data Base irradiance data to model PV system energy production. Using performance data from some of the millions of installed PV modules with micro-inverters offers the opportunity to provide ground-based solar resource data critical for developing PV projects and for the site adjustments of satellite-based irradiance data.
This project seeks to carry out a multifaceted analysis combining buoy observations, machine learning, turbulence, satellite data and high-resolution modeling. Our analyses will investigate air–sea interaction physics governing the variation of the winds with height and influence of clouds, uncertainty in coupled ocean-wave-atmosphere mesoscale models to capture certain key atmospheric phenomenon observed over the U.S. West Coast, impact of climate change, and the fidelity with which resource characterization models describe the range of observed offshore wind conditions. This project will focus its efforts on characterizing and assessing the atmospheric and oceanographic conditions along the U.S. West Coast.
North American beavers (Castor canadensis) are returning to Massachusetts after overhunting decimated their populations in the 1700s. Current regulations have allowed this species to recolonize, resulting in increasingly prevalent human-beaver conflicts. These ecosystem engineers can quickly change their environment through the creation of dams, leading to floods that can adversely affect human infrastructures, such as basements, roads, or septic systems. Conversely, beaver dams can positively influence their environment, modifying the physical and chemical properties of streams and providing crucial habitats to a variety of wildlife. The 2020 Spring Boston NASA DEVELOP team collaborated with the Massachusetts Audubon Society to support their efforts in monitoring beaver impacts and managing human-beaver conflicts. The project-utilized data from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 8 Operational Land Imager to map the spectral signature created from beaver-induced flooding. The team created a tool called Beaver-Flood Event Detector (B-FED) in Google Earth Engine using imagery from 1985 to 2019. Ancillary datasets were incorporated into B-FED that allow the tool to highlight flood events in wetland areas and in situ observations of beaver presence. Beaver observations in or near flooded areas indicated likelihood that the flood was beaver induced. Time series and animations were also produced to display key regions of landscape change across Massachusetts. B-FED will allow the partner to identify and assess potential ecosystem changes and infrastructural impacts from beavers across Massachusetts and inform future management practices.
North American beavers (Castor canadensis) are returning to Massachusetts after overhunting decimated their populations in the 1700s. Current regulations have allowed this species to recolonize, resulting in increasingly prevalent human-beaver conflicts. These ecosystem engineers can quickly change their environment through the creation of dams, leading to floods that can adversely affect human infrastructures, such as basements, roads, or septic systems. Conversely, beaver dams can positively influence their environment, modifying the physical and chemical properties of streams and providing crucial habitats to a variety of wildlife. The 2020 Spring Boston NASA DEVELOP team collaborated with the Massachusetts Audubon Society to support their efforts in monitoring beaver impacts and managing human-beaver conflicts. The project-utilized data from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 8 Operational Land Imager to map the spectral signature created from beaver-induced flooding. The team created a tool called Beaver-Flood Event Detector (B-FED) in Google Earth Engine using imagery from 1985 to 2019. Ancillary datasets were incorporated into B-FED that allow the tool to highlight flood events in wetland areas and in situ observations of beaver presence. Beaver observations in or near flooded areas indicated likelihood that the flood was beaver induced. Time series and animations were also produced to display key regions of landscape change across Massachusetts. B-FED will allow the partner to identify and assess potential ecosystem changes and infrastructural impacts from beavers across Massachusetts and inform future management practices