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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Distributed Wind Aeroelastic Modeling (dWAM)

Aeroelastic modeling is the primary method for the structural and performance assessment of any wind turbine. Despite the advantages afforded by aeroelastic modeling tools, their use in the distributed wind energy industry is limited. dWAM started from the NREL Aeroelastic Modeling for Distributed Wind Turbines project with Damiani & Davis (2022) researching current needs, including input from an industry workshop. NREL's efforts will focus on OpenFAST code improvements, validation using research turbines at NREL's Flatirons Campus, code-to-code verification activities, and development of guidance documents and improved user manuals. Partner lab, Sandia National Laboratories, will focus their efforts on vertical axis wind turbine (VAWT) modeling including modeling code development, validation, and user-experience improvements.

aeroelastic↗
DOE OSTI2023

DuraMAT Technology Scouting Report: Assessing Module Reliability Risks Associated with Projected Technological Changes

Maintaining the reliability of photovoltaic (PV) modules in the face of rapidly changing technology is critical to maximizing solar energy's contribution to global decarbonization. Our presentation describes expected changes in PV technology and their impacts on performance and reliability. We leverage PV market reports, interviews with PV researchers and other industry stakeholders, and peer-reviewed literature to narrow the multitude of possible changes into a manageable set of 11 impactful trends likely to be incorporated in near-term crystalline-silicon module designs. We group the trends into four categories (module architecture, interconnect technologies, bifacial modules, and cell technology) and explore the drivers behind the changes, their interactions, and associated reliability risks and benefits. Our analysis identifies specific areas that would benefit from accelerating the PV reliability learning cycle, to assess emerging module products and designs more accurately. We recommend that researchers continue tracking module technologies and their reliability implications so efforts can be focused on the most impactful trends. As the rapid technological turnover continues, it is also critical to incorporate fundamental knowledge into models that can predict module reliability. Predictive capabilities complete the PV reliability learning cycle-reducing the time required to assess new designs and mitigating the risks associated with large-scale deployment of new products.

bifacial↗
DOE OSTI2023

High-Resolution Southeast Asia Wind Resource Data Set

Well informed decision-making is a key part of integrating variable renewable energy into the global energy marketplace. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to critical resource data by providing free, high-fidelity time-series wind resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights the development of the Southeast Asia wind resource data set and discusses the impacts of this data.

Advanced Energy Partnership for Asia↗
DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States

Geothermal resources at temperatures below 150 degrees C have great potential as energy sources for various direct-use applications including heating and cooling in residential and commercial buildings. This study geospatially quantifies U.S. heating and cooling demand in residential, commercial, and manufacturing sectors; heating demand in the agricultural sector; and cooling demand in data centers at the county level through end-use energy consumption, expenditure, and commissioned power analyses. Heating and cooling demand in the residential sector was estimated using energy consumption data obtained from the U.S. Energy Information Administration accounting for different U.S. climate zones. For commercial sector analysis, the end-use major fuel energy intensity at the census division level was disaggregated to the county level with respect to principal building activities. Heating and cooling demand analysis for the manufacturing sector was based on end-use energy consumption for direct-use total process categorized by the North America Industry Classification System. Fuel expenditures in the U.S. Department of Agriculture Farm Production Expenditures were examined for heating demand analysis in the agricultural sector, particularly for the greenhouse, nursery, and floriculture production category. Lastly, commissioned power for data centers in the United States were explored for cooling demand analysis. Results indicated a significant fraction of U.S. primary energy consumption is used for low-temperature heating and cooling applications. Heating and cooling demand in residential and commercial sectors is significantly affected by the number of housing units and climate zone designations, while heating and cooling demand in manufacturing and agricultural sectors and data centers are mainly dependent on the number of facilities and their locations. Maps were generated visualizing where heating and cooling demand is high and, overlain with geothermal resource maps, can indicate locations where geothermal energy can supply this heating and cooling demand.

cooling demand↗
DOE OSTI2023

National Distributed Wind Energy Deployment Network

The National Distributed Wind Deployment Network seeks to broaden the types of stakeholders considering distributed wind deployment, document the barriers and opportunities they have identified, and develop resources and supports to help them deploy the technology.

deployment↗
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 OSTI2023

Distributed PV Hosting Capacity Evaluation Considering Equitable PV Accommodation: Preprint

In distribution systems, distributed photovoltaic (DPV) hosting capacity is widely used by system operators to evaluate how much DPV the distribution networks can accommodate without violating operational constraints. With the proliferation of DPV deployment, if managed properly, it offers a great opportunity to mitigate the persistent energy inequity issue in electric grid. Otherwise, energy inequity can be exacerbated with unequal DPV access when hosting capacity improvement plans ignore the equity factor. To consider energy equity in distribution network planning for enhancing DPV accommodation, the hosting capacity evaluation should be improved to consider equity constraints endogenously. In this paper, a DPV hosting capacity evaluation method is proposed considering equitable DPV access among sub-regions of a distribution feeder partitioned by social-economic indicators such as the income levels. The results demonstrate that considering equity in DPV hosting capacity evaluation can provide valuable reference in the equitable energy transition. This work provides a valuable guidance for future distribution network upgrade planning for equitable DPV deployment.

distributed PV↗
DOE OSTI2023

Photovoltaic (PV) Quick Reference Guide

This Photovoltaic (PV) and Energy Storage System (EES) Quick Reference Guide is for inspectors inspecting projects permitted with SolarAPP+.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2023

Gearing Up for 2030: Building the Offshore Wind Supply Chain and Workforce Needed to Deploy 30 GW and Beyond

This webinar addresses the current state and future needs of the U.S. offshore wind energy workforce and supply chain. During this webinar, National Renewable Energy Laboratory researchers Matt Shields and Jeremy Stefek will present new research on the challenges of producing thousands of offshore wind energy technology components while creating tens of thousands of U.S. jobs - and the solutions that can help meet those challenges. Topics covered in the webinar include port facilities, vessels, component manufacturing, trades and roles in the industry, workforce development and training, and the coordination of supply chain and workforce efforts between states and regions.

2030↗
DOE OSTI2023

Improving the Quality of Geothermal Data Through Data Standards and Pipelines Within the Geothermal Data Repository: Preprint

For machine learning outputs to be applicable to real world problems, high quality data are needed to ensure high quality results. With the more recent emphasis on machine learning in geothermal, there is an increasing need for greater focus on the quality of the data available for use in these projects. For example, Geothermal Operational Optimization Using Machine Learning (GOOML) utilized large quantities of geothermal power plant operational data to inform power plant operational configurations to maximize power generation. High quality datasets result from dependable sensors or devices collecting data, high frequency of measurements, sufficient data points, adequate metadata, reliable storage of data, and sufficient data curation. Another component that contributes to high quality data is reusability, which can be enhanced through data standardization. Data Standardization creates consistency in formatting and contents of like datasets, lessening preprocessing requirements and ensuring adequate information provided by a given dataset. The Geothermal Data Repository (GDR) aims to help improve data quality through automated data standardization for high-value datasets through the implementation of data pipelines alongside reliable and accessible long-term storage for datasets. As such, the GDR has decided to shift away from recommending the use of Excel-based content models and towards the implementation of automated data pipelines. This takes the burden of data standardization off the user and project team and will increase the availability of standardized geothermal data available through the GDR. A set of recommendations, or a data standard for each data type will exist with each data pipeline in order to advise data collection for maximum usability for future research. This paper serves to describe the GDR's proposed transition towards data standardization through automated data pipelines, to discuss the need for and value of such a shift, and to call for suggestions from the community regarding the most useful data standards and pipelines.

data↗
DOE OSTI2023

Using an Absolute Cavity Pyrgeometer to Calibrate Pyrgeometers Outdoors with Respect to the International System of Units

Accurate measurement of the atmospheric longwave irradiance is important for renewable energy and atmospheric science applications. Pyrgeometers are deployed outdoors all over the world to measure the atmospheric longwave irradiance and presently are calibrated with traceability to the interim standards for atmospheric longwave radiation measurement, the standards are based on four pyrgeometers and their average irradiance is the World InfraRed Standard Group (WISG) which is developed and maintained by The Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC). Since 2013 the InfraRed Integrating Sphere (IRIS) developed by PMOD/WRC and the Absolute Cavity Pyrgeometer (ACP) developed by the National Renewable Energy Laboratory (NREL) have been compared outdoors six times at different locations and the difference between the measured atmospheric longwave irradiance by ACP and IRIS was less than 2 w/m2 with traceability to the International System of Units (SI). During the six comparisons the irradiance measured by the interim WISG was 5 w/m2 lower than the irradiance measured by the average irradiance measured by the ACP and IRIS [1]. Based on this discrepancy, the World Meteorological Organization's Commission for Instruments and Methods of Observation (CIMO) recommended that the interim WISG should be adjusted to be traceable to SI units [2]. In anticipation of CIMO's expert team agreement on establishing the world reference using the average irradiance measured by ACP and IRIS in this article we describe a procedure to calibrate pyrgeometers with traceability to SI. One Absolute Cavity Pyrgeometer (ACP95F3) was used to calibrate four pyrgeometers traceable to SI units. Three Eppley PIRs and one Kipp&Zonen CG4 were originally calibrated with traceability to the interim WISG. Using the described procedure below, the responsivity of each pyrgeometer was then adjusted to match the irradiance measured by ACP. Outdoor data was collected during one clear sky night monitored by the output thermopile voltage of ACP95F3. The irradiance measured by the PIRs and CG4 was calculated using NREL equation. The calculated uncertainty (U95) of the PIRs varied from 2.43 w/m2 to 2.67 w/m2 , and for the CG4 equals 1.97 w/m2 with respect to SI.

absolute cavity pyrgeometer↗
DOE OSTI2023

Community Solar Program Design and Subscription Models

This slide deck provides an overview of community solar program design and subscription models. It introduces and explains the different elements of community solar program and subscription design, and proves proven practices and considerations when designing a community solar program. This slide deck was originally developed as part of a working group under the National Community Solar Partnership's Municipal Utility Collaborative.

community solar↗
DOE OSTI2023

Wind as a Distributed Energy Resource

Distributed wind can be installed in a wide range of locations and wind conditions, supporting millions of systems and thousands of gigawatts of power production capacity. As a result, utilities, communities, and nations are looking to distributed generation as an effective way to meet future energy needs. This fact sheet provides an overview of distributed wind, including where distributed wind projects can be located, and how U.S. and international research supports distributed wind applications. This fact sheet was produced as a resource for the International Energy Agency Task 41 members to use as an educational resource.

behind-the-meter wind energy↗
DOE OSTI2023

Community Solar Program and Subscription Design

This slide deck provides an overview of community solar program design and subscription models. It introduces and explains the different elements of community solar program and subscription design, and proves proven practices and considerations when designing a community solar program. This slide deck was developed for a continuing education course hosted by the American Public Power Association (APPA).

community solar↗