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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.
The electrification of U.S. federal, state, and municipal fleets is accelerating rapidly, driven by an increased availability of competitive electric vehicle (EV) options and supportive policies and targets. The dataset described in this report, accessible at data.nrel.gov/submissions/280, provides a critical foundation for identifying fleet electricity demand, projecting these future demands, and developing actionable strategies to support the widespread electrification of government fleets. The dataset incorporates available fleet data, including 54% of federal agency vehicles approved for analysis (notably, the U.S. Postal Service is absent). Additionally, it includes data from 50,000 state government vehicles and 94,000 local government vehicles. While this represents a small fraction of the 4.4 million vehicles owned by state and local governments reported by the Federal Highway Administration (2022), the framework supports future expansion as more fleet inventory data become available.
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.
Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO2 emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.
While the variations of tidal range are large and fairly well known across Australia (less than 1 m near Perth but more than 14 m in King Sound), the properties of the tidal currents are not. We describe a new regional model of Australian tides and assess it against a validation dataset comprising tidal height and velocity constituents at 615 tide gauge sites and 95 current meter sites. The model is a barotropic implementation of COMPAS, an unstructured-grid primitive-equation model that is forced at the open boundaries by TPXO9v1. The mean absolute error (MAE) of the modelled M2 height amplitude is 8.8 cm, or 12 % of the 73 cm mean observed amplitude. The MAE of phase (10°), however, is significant, so the M2 mean magnitude of vector error (MMVE, 18.2 cm) is significantly greater. The root sum square over the eight major constituents is 26 % of the observed amplitude. We conclude that while the model has skill at height in all regions, there is definitely room for improvement (especially at some specific locations). For the M2 major axis velocity amplitude, the MAE across the 95 current meter sites, where the observed amplitude ranges from 0.1 to 156 cm s −1 , is 6.9 cm s −1 , or 22 % of the 31.7 cm s −1 observed mean. This nationwide average result is encouraging, but it conceals a very large regional variation. Relative errors of the tidal current amplitudes on the narrow shelves of New South Wales (NSW) and Western Australia exceed 100 %, but tidal currents are weak and negligible there compared to non-tidal currents, so the tidal errors are of little practical significance. Looking nationwide, we show that the model has predictive value for much of the 79 % of Australia's shelf seas where tides are a major component of the total velocity variability. In descending order this includes the Bass Strait, the Kimberley to Arnhem Land, and southern Great Barrier Reef regions. There is limited observational evidence to confirm that the model is also valuable for currents in other regions across northern Australia. We plan to commence publishing “unofficial” tidal current predictions for chosen regions in the near future based on both our COMPAS model and the validation dataset we have assembled.
Community solar offers multiple benefits to providers and residents of multifamily affordable housing (MFAH), including access to affordable renewable energy for low- and moderate-income households, tax credits and other financial benefits for property owners, and increased community resilience when paired with energy storage. Even so, MFAH providers interested in realizing these benefits may need support to identify where to start or how to expand and accelerate solar deployment across their property portfolios once they have completed their first few installations. This fact sheet describes a portfolio screening approach for MFAH providers to evaluate their multi-state building portfolios for community solar project viability. The approach was developed by the National Renewable Energy Laboratory as part of its support for the National Community Solar Partnership (NCSP) MFAH Collaborative. By utilizing NREL's companion Policy Screening Workbook in conjunction with the web-based tools PVWatts® Calculator and REopt™ (Renewable Energy Integration and Optimization), MFAH providers can take a data-informed approach to identifying and prioritizing buildings in their portfolios that are most amenable to community solar development.
National Renewable Energy Laboratory's (NREL) high-performance computing (HPC), efficient data center operation, and state-of-the-art data visualization powered over 300 clean energy projects in Fiscal Year 2022 (FY22). 555 scientists and engineers utilized HPC resources to assess and integrate data into their research. The report highlights the arrival of new HPC Kestrel in 2023, the contributions of the Insight Center, and researchers looking into bias in artificial intelligence, alongside key research collaborations using advanced computing.
Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same field, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications. To address these challenges, the open source Time-Series Data Pipelines (Tsdat) software was developed by a joint collaboration between Pacific Northwest National Laboratory, the National Renewable Energy Laboratory, and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the Marine Energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat software and the data standards within which the framework operates. A beta version of the framework has been released and is currently being used by several projects in marine energy, wind energy, and building energy systems.
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This is a keynote presentation for a workshop highlighting the use of remote sensing and modeling products derived with NASA missions and Earth system modeling for solar energy system development. We broadly introduce NASA's fleet of satellite producing publicly available data products that would be useful for the information needed solar energy development. We also introduce the work in Earth system science modeling that provides additional global gridded data products that are also relevant. We identify that one issue is obtaining and using this data is the level of expertise needed to find it. NASA has developed a new Earth Action program to address this gap and bring the data closer to the user. Now under the Energy Resources element, the NASA LaRC's POWER (Prediction of Worldwide Renewable Energy) project has developed a web services platform that provides key information needed for solar energy development as analysis-ready. This means that the data for particular locations are immediately useful for help provide the information needed by engineers and architects to plan for solar systems. Thus, the presentation shows why NASA products would be relevant and then shows examples of finding the needed data through the POWER web portal.
The City of Satellite Beach, Florida, has committed to supplying 100% of its energy use from renewable energy, primarily solar, by the year 2050. The team created a methodology for estimating rooftop solar power potential using a high-resolution Light Detection and Ranging (LiDAR) dataset and the NASA Prediction of Worldwide Energy Resources (POWER) dataset to assist Satellite Beach in reaching their solar renewable energy goals. The POWER dataset provides information on direct and diffuse solar irradiation on horizontal surfaces, surface albedo, and effects of local meteorology, such as clouds. The team integrated the solar irradiance data with the LiDAR data to model slope, aspect, and shadowing in the 7 km2 study area to find suitable roof segments for solar panel installation and estimate the solar potential of each segment. This process was supplemented by an analysis of land surface temperature and urban greenness measured through the Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) observations. These metrics serve to target areas for cooling initiatives aimed at reducing Satellite Beach’s overall energy consumption. The team found the total rooftop solar potential throughout the city to be 221,919,330KWh per year with an average annual rooftop photovoltaic, or PV, potential of 55,647KWh per building. As such, the average building could generate over five times the annual energy needs for an average household if PV panels were installed on all viable areas of its roof.
The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of downscaling NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.
This paper presents preliminary results from a subset of work carried out as part of a multinational research project entitled DErisking Exploration for multiple geothermal Plays in magmatic ENvironments (DEEPEN), supported by the U.S. Department of Energy (DOE) and Geothermica, a joint effort by EU member states and associated countries. The DEEPEN project will develop a customized approach to exploration for supercritical and superhot geothermal plays in magmatic systems, which will be applied to two demonstration sites. This paper summarizes field activities carried out at the U.S. demonstration site, Newberry Volcano in central Oregon. The objective of this work effort is to refine the subsurface model of Newberry Volcano, with special focus on deeper zones including the magmatic plumbing system and other key geologic elements. New data collection included gravity and wideband magnetotelluric (MT) surveys, as well as reinstallation of a seismic network. The National Renewable Energy Laboratory (NREL) and Enthalpion Energy LLC (Enthalpion) worked with the Deschutes National Forest Fort Rock District to use a low ground disturbance method of MT deployment to collect MT data inside the caldera and other restricted areas inside the National Volcanic Monument. This opened these areas to geophysical exploration for the first time in decades. Sites along and adjacent to the south rim of the volcano constituted the primary survey objectives. A team from Lawrence Berkeley National Laboratory (LBNL), the U.S. Geological Survey (USGS), and AltaRock also began the process of reinstalling the seismic network from the AltaRock enhanced geothermal system (EGS) demonstration in anticipation of further development activities at the site. The data ingestion, reduction, and analysis phase of the project is ongoing. We are currently processing the MT and gravity data and are developing a new, highly GPU-accelerated, 3D joint MT and gravity inversion to better localize the south rim/south flank conductive target and better understand its relationship to deep heat, fluid sources, and surface extrusive features. Joint inversions, which have not yet been undertaken at Newberry, will allow us to obtain constraints on the geologic model that cannot be determined from each method in isolation, improving our ability to image key geologic features at depth.
In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals, multiple computational frameworks are used to locate suitable land for renewable infrastructure development, and to quantify spatiotemporal variations in the potential energy generation and financial viability of development sites across the Unites States. First, the Oak Ridge Siting Analysis for power Generation Expansion tool (OR-SAGE) is used to assess the suitability of land for potential wind and solar energy development across the contiguous U.S. From there, resource data from the National Solar Radiation Database (NSRDB) and the Wind Integration National Dataset (WIND) are used in concert with the National Renewable Energy Laboratory (NREL) Renewable Energy Potential (ReV) model to calculate the variation in potential generation capacity for each resource. Additionally, the capital and operational expenditures are calculated for an example configuration of each renewable technology. These measures are then used to calculate the levelized cost of energy (LCOE) of potential sites. All of these results are then processed and analyzed to determine where in the U.S. solar and wind energy are most viable. This viability is based on available generation potential, consistency and stability of energy generation over time, and economic viability with respect to LCOE.
The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.
The effectiveness of climate change impact assessments and the development of adaptation strategies depend on the availability of high-quality future weather data. However, significant gaps exist between the needs of the energy research community and the focus of the climate modeling community, primarily due to a historical lack of communication and collaboration between the two groups. Here, to address this issue, this work provides a comprehensive overview of the critical aspects involved in creating future weather data for building and energy system modeling, including emissions scenarios, general circulation models, downscaling methods, categories of future weather data, and uncertainties in climate simulations. Moreover, it critically evaluates the applicability and suitability of various types of future weather data in five key application scenarios: energy use analysis, resilience analysis, HVAC design, utility-scale analysis, and renewable energy analysis. Finally, this work presents recommendations for high-level actions and research directions to foster collaboration between the energy research and climate modeling communities and to promote the integration of future weather data into energy codes and the design practices of buildings and energy systems.
Urban heat is a growing concern, particularly in dense metropolitan areas where high temperatures increase the risk of heat-related illness and drive energy expenses for cooling. Estimating the effects of urban heat remains a challenge due to limitations in describing the built environment, computational constraints, and the need for high-resolution data. This software presents open-source, computationally efficient machine learning methods that enhance the accuracy of urban temperature estimates compared to historical reanalysis data. Models trained using this software have been applied to urban microclimates in Los Angeles and Seattle showing greater accuracy and less bias when compared to low-resolution reanalysis datasets like ERA5 and even when compared to high-resolution mesoscale numerical weather models like WRF with an urban canopy model. Initial findings highlight how machine learning can support urban heat resilience planning by enabling improved assessments of local heat islands, mitigation strategies, and their energy implications. This software is an extension of (sup3r). This software supports the following publication: Buster, Grant, et al. Tackling Extreme Urban Heat: A Machine Learning Approach to Assess the Impacts of Climate Change and the Efficacy of Climate Adaptation Strategies in Urban Microclimates. arXiv:2411.05952, arXiv, 8 Nov. 2024. arXiv.org, https://doi.org/10.48550/arXiv.2411.05952. And has related public data records available at: Buster, Grant, Cox, Jordan, Benton, Brandon, and King, Ryan. Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI). United States: N.p., 16 Oct, 2024. Web. https://data.openei.org/submissions/6220.
The Prediction Of Worldwide Energy Resources (POWER) Project 25th year of providing global solar and meteorology data products to the world. The goals of this event were to inform users of new, enhanced features, gather feedback on services, identify new requests and requirements, and capture more thorough user stories while identifying new user communities and partnership opportunities. The event was “virtual” and had over 580 registrants. Over the two-day event, the Summit was attended by 162 unique attendees representing 24 countries.