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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 19 records
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DOE OSTI2024

2024 OES-Environmental 2024 State of the Science Report, Chapter 8: Marine Renewable Energy Data and Information Systems

As the marine renewable energy (MRE) sector grows, large amounts of environmental and technical data and information are being collected. When these data and information are openly available, they can be used to guide research and development, inform responsible siting and consenting of projects, and increase stakeholder understanding through transparency. For example, quality environmental data collected during the siting, consenting, construction, operation, and decommissioning of MRE projects can all play key roles in better characterizing baseline conditions, developing effective monitoring and mitigation strategies, and retiring environmental risks through data transferability (see Chapter 6). Ensuring that these data and information are easily discoverable and accessible will help the MRE sector make informed decisions and coexist in an increasingly busy ocean environment.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Development of Renewable Energy Data for Facilitating the Clean Energy Transition in Vietnam

Vietnam is transforming its power system with an increasing share of variable renewable energy (VRE) in the generation mix and plans to achieve 27% VRE by capacity in 2030 and 42% by capacity in 2045, as indicated by the draft Eighth Power Development Plan for 2020–-2045 (PDP8). Further increase in VRE penetration is possible with data-driven analysis where availability of data, specifically VRE data, becomes critical. NREL has worked with various stakeholders in Vietnam to create high-resolution multi-year VRE data publicly available to all. This paper reviews this new VRE data for Vietnam and how different stakeholders can use this data to inform VRE deployment decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Data Set and Analysis to Inform Equitable Investment Prioritization in the United States

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where disadvantaged community indicators and high generation potential from cost-effective renewable energy opportunities intersect and deployment could lead to economic development and job creation. This presentation will highlight several of our findings. This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.

community energy planning↗
DOE OSTI2023

Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Array↗
DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2024

Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine [Slides]

In this work we present a novel deep learning-based downscaling method, using generative adversarial networks (GANs), for generating high-resolution wind resource data from ECMWF Reanalysis v5 data (ERA5). We show that by training a GAN model on ERA5, as opposed to coarsened high-resolution data, we achieve results that are competitive with conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. All GANs are trained on data sampled from CONUS, selected to provide a diverse sampling of terrain conditions, and validated on observational data along with data held out from training. This cross-validation shows low error and high correlations with observations and excellent agreement with hold out data across physical distributions. Our approach is finally used to downscale 30km hourly ERA5 to 2-km 5-minute wind data, for January 2000 through December 2023, at multiple hub heights, over Ukraine, Moldova, and part of Romania. Comparisons against observational data from Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show the same level of performance as for CONUS validation. This 24 year data record is the first member of the "super resolution for renewable energy resource data with wind from reanalysis data" dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2023

RE Data Explorer is Expanding Public Access to High-Quality Data and Analytics

The Renewable Energy (RE) Data Explorer is an innovative and intuitive web platform from the U.S. Agency for International Development (USAID)-National Renewable Energy Laboratory (NREL) Partnership that enables users to easily access high-quality renewable energy resource data and applied analytics.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗
DOE OSTI2022

Reliability and Resiliency in South Asia's Power Sector - Pathways for Research, Modeling, and Implementation

Reliability and resilience are the core principles of power system planning and operations around the world. Power systems in South Asia are transforming with increasing penetration of clean energy generation resources, emerging technologies, increasing electricity demand and electrification. At the same time, these power systems are facing challenges posed by extreme weather events and climate change. All these factors would add furthermore importance to the reliability and resilience of future power systems in South Asia. This has motivated us to better understand the country specific challenges and chalk out the pathways for research, modelling and implementation in South Asia. Our research, experience in the region and feedback from key stakeholders indicate following as the key areas where more work is needed to improve reliability and resilience of power systems in the region: Renewable energy Data for power system studies, New Tools and Studies, Resilience Planning, Resource Adequacy, Advanced RE Forecasting, Cybersecurity, Load Forecasting, and Coordinated Planning and Operations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S.

Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.

NASA POWER↗
DOE OSTI2022

High-Fidelity Solar Irradiance Data: Simple Access to State-of-the-Art Information Accelerates Southeast Asia's Clean Energy Economic Transformation

High quality, robust, and reliable renewable energy resource data is foundational to climate-smart decision making, evidence-based policy planning, and clean energy investment mobilization. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to this critical resource data by providing free, high-fidelity solar resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights several ways the Southeast Asia solar resource data has been used for power system planning and project development in the region.

Advanced Energy Partnership for Asia↗
DOE OSTI2022

20 Year Daily Average Modeled Velocity and Discharge for Openwater Season at Five Communities on Kuskokwim River, Alaska

This dataset includes modeled velocity and discharge at five communities in the middle Kuskokwim River region: Aniak, Chuathbaluk, Crooked Creek, Red Devil and Stony River. Modeled velocities and discharge represent daily averages calculated for the openwater season (OWS) from June 1 - October 18 over the 20 year period 2000-2019 using the raw data described below and included in this archive; full details of methodology are described in (Brown et al. submitted to Renewable Energy). Raw data inputs to inform the modeling process include in-situ measurements of 1) discharge with an acoustic Doppler current meter (ADCP, 600kHz Workhorse Rio Grande by Teledyne RD Instruments) and a global positioning receiver (GPS, Trimble 5700, 5800 and R8) utilizing Real Time Kinematic (RTK) GPS mode over 1-2 days at each site in 2009 or 2010 (Ravens 2014), and 2) river stage with a water level logger (HOBO U20-001-01 by Onset) over 2-9 weeks at each site (Ravens 2014), 3) in addition to a 20 year long-term discharge record collected at the USGS stream gage site in Crooked Creek (USGS 2016). Raw data (discharge and stage) are included in this archive for two additional communities: Lower Kalskag and Sleetmute, where modeled velocities were not calculated due to equipment failure or loss. The USGS stream gage data at Crooked Creek (USGS 2016) and stream gage methodology (Turnipseed and Sauer 2010) are publicly available online, so the data are not duplicated here.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy

The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) comprehensively analyzes possible pathways for Puerto Rico to achieve its renewable energy goals while incorporating stakeholder perspectives and advancing energy resilience for all Puerto Ricans. PR100 is a wide-ranging and in-depth examination of Puerto Rico's energy system investment options. The findings are the culmination of two years of stakeholder engagement, scenario modeling, and impact analysis. The PR100 report and Implementation Roadmap contain a range of results and actions that reflect Puerto Rico's priorities around energy justice, resilience, and reliability. Led by the U.S. Department of Energy's Grid Deployment Office with funding from the Federal Emergency Management Agency, the PR100 study leveraged and integrated dozens of best-in-class models and in-depth analyses from researchers across six national laboratories: National Renewable Energy Laboratory (which led the study), along with Argonne National Laboratory, Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, Pacific Northwest National Laboratory, and Sandia National Laboratories (which conducted the study). For more information, please see the "PR100 Project Website" resource below.

Array↗