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At least 127 records · Page 7

Comparison of Mesoscale Model Setups for Offshore Wind Resource Assessment: A New Jersey Case Study

As the offshore wind energy industry begins to emerge on the U.S. northeast coast, there is an urgent need for an accurate characterization of the offshore wind resource. The offshore regime features various resource characteristics not seen onshore, including frequent coastal low-level jets, non-steady surface conditions (e.g. waves), complex air-sea and land-sea interactions, and coastal mesoscale circulations (e.g. sea breeze). An accurate characterization of these phenomena in mesoscale models is critical to support accurate energy production estimates and forecasting for future offshore wind farms. However, a robust validation of mesoscale model performance in the offshore U.S. has been limited due to sparse data and difficulty obtaining offshore measurements. Consequently, it is unclear how mesoscale models perform offshore and which suite of parameterizations and input data are best suited for accurate resource characterization. To begin addressing this knowledge gap, the National Renewable Energy Laboratory, in collaboration with the Rutgers Center for Ocean Observing Leadership, conducted a mesoscale model validation campaign in the offshore environment. The Weather Research and Forecasting (WRF) model was assessed and the New Jersey offshore coastal area served as the validation domain. An ensemble of WRF simulations conducted over a one-year period sampled from a range of model setups, including different reanalysis and forecast data inputs, sea surface temperature data inputs, planetary boundary layer parameterizations, and WRF versions. The performance of each ensemble member was validated against a network of observations including offshore floating lidars, coastal met towers, offshore platform measurements, and offshore buoys. Model performance was assessed on annual, seasonal, and diurnal scales with particular emphasis on the vertical wind speed profile and temperature stratification. Preliminary results from this validation work will be presented here. The key WRF inputs and parameters driving changes in the modeled wind resource will be discussed and preliminary recommendations on WRF setups for offshore wind resource modeling will be proposed. The application of this validation exercise to other proposed offshore wind energy areas in the U.S. will also be discussed. mesoscale circulations (e.g. sea breeze). An accurate characterization of these phenomena in mesoscale models is critical to support accurate energy production estimates and forecasting for future offshore wind farms. However, a robust validation of mesoscale model performance in the offshore U.S. has been limited due to sparse data and difficulty obtaining offshore measurements. Consequently, it is unclear how mesoscale models perform offshore and which suite of parameterizations and input data are best suited for accurate resource characterization. To begin addressing this knowledge gap, the National Renewable Energy Laboratory, in collaboration with the Rutgers Center for Ocean Observing Leadership, conducted a mesoscale model validation campaign in the offshore environment. The Weather Research and Forecasting (WRF) model was assessed and the New Jersey offshore coastal area served as the validation domain. An ensemble of WRF simulations conducted over a one-year period sampled from a range of model setups, including different reanalysis and forecast data inputs, sea surface temperature data inputs, planetary boundary layer parameterizations, and WRF versions. The performance of each ensemble member was validated against a network of observations including offshore floating lidars, coastal met towers, offshore platform measurements, and offshore buoys. Model performance was assessed on annual, seasonal, and diurnal scales with particular emphasis on the vertical wind speed profile and temperature stratification. Preliminary results from this validation work will be presented here. The key WRF inputs and parameters driving changes in the modeled wind resource will be discussed and preliminary recommendations on WRF setups for offshore wind resource modeling will be proposed. The application of this validation exercise to other proposed offshore wind energy areas in the U.S. will also be discussed.

17 WIND ENERGY↗

High-resolution Earth-based lunar radar studies: Applications to lunar resource assessment

The lunar regolith will most likely be a primary raw material for lunar base construction and resource extraction. High-resolution radar observations of the Moon provide maps of radar backscatter that have intensity variations generally controlled by the local slope, material, and structural properties of the regolith. The properties that can be measured by the radar system include the dielectric constant, density, loss tangent, and wavelength scale roughness. The radar systems currently in operation at several astronomical observatories provide the ability to image the lunar surface at spatial resolutions approaching 30 m at 3.8 cm and 12.6 cm wavelengths and approximately 500 m at 70 cm wavelength. The radar signal penetrates the lunar regolith to a depth of 10-20 wavelengths so the measured backscatter contains contributions from the vacuum-regolith interface and from wavelength-scale heterogeneities in the electrical properties of the subsurface material. The three wavelengths, which are sensitive to different scale structures and scattering volumes, provide complementary information on the regolith properties. Aims of the previous and future observations include (1) analysis of the scattering properties associated with fresh impact craters, impact crater rays, and mantled deposits; (2) analysis of high-incidence-angle observations of the lunar mare to investigate measurement of the regolith dielectric constant and hence porosity; (3) investigation of interferometric techniques using two time-delayed observations of the same site, observations that require a difference in viewing geometry less than 0.05 deg and, hence, fortuitous alignment of the Earth-Moon system when visible from Arecibo Observatory.

Stacy, N. J. S.↗

Wind Energy Resource Assessment on Alaska Native Lands in Cordova Region of Prince William Sound

The Native Village of Eyak (NVE) has been monitoring wind resources around Cordova, Alaska in order to determine whether there is a role for wind energy to play in the city’s energy scheme, which is now supplies entirely by two run-of-the-river hydro plants and diesel generators. These data are reported in Appendices A and B. Because the hydro resources decline during winter months, and wind resources increase, wind is perhaps an ideal counterpart to round out Cordova’s renewable energy supply. The results of this effort suggests that this is the case, and that developing wind resources makes sense for our small, isolated community.

17 WIND ENERGY↗

A Physics-Based DNI Model for Advancing Solar Resource Assessment and Forecasting: Preprint

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.

DNI↗

Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

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.

15 GEOTHERMAL ENERGY↗

Integration of GOES Data for Solar Resource Assessment of the Contiguous United States

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.

14 SOLAR ENERGY↗

Carbon Storage Technical Viability Approach (CS TVA): An Integrated Approach for Feasibility and Data Resource Assessment

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.

Rodriguez, Neyda Cordero↗

Resource assessment of ocean thermal energy conversion in Puerto Rico and U.S. Virgin Islands

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.

OTEC↗

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

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.

17 WIND ENERGY↗

Causes of and Solutions to Wind Speed Bias in NREL’s 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

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.

17 WIND ENERGY↗

Model Test Bed for Evaluating Unstructured-Grid Wave Models for Resource Assessment and Characterization

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.

16 TIDAL AND WAVE POWER↗

The Holy Grail of Resource Assessment: Low Cost Ground-Based Measurements with Good Accuracy

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.

14 SOLAR ENERGY↗

The Holy Grail of Resource Assessment: Low Cost Ground-Based Measurements with Good Accuracy

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.

14 SOLAR ENERGY↗

The Holy Grail of Resource Assessment: Low Cost Ground-Based Measurements with Good Accuracy: Preprint

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.

14 SOLAR ENERGY↗

Economic, Greenhouse Gas, and Resource Assessment for Fuel and Protein Production from Microalgae: 2022 Algae Harmonization Update

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.

09 BIOMASS FUELS↗

Solar Resource Assessment Using Micro-Inverter Data: Preprint

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.

14 SOLAR ENERGY↗