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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data on temporal complementarity of hybrid renewable energy systems [SWR-23-09]

These datasets describe multiple facets of the temporal complementarity of co-located hybrid renewable energy systems throughout the United States. Several metrics characterizing the complementarity of generation profiles are provided on an annual and monthly basis (for both hourly and daily aggregations). These generation profiles are underpinned by hourly resource data (e.g., the WIND Toolkit and National Solar Radiation Database (NSRDB)) spanning the multi-year period 2007-2013. The data include complementarity results for greater than 1.76 million individual locations within the continental United States (CONUS). The data are intended to accompany two publications on the topic of temporal complementarity: 1) Harrison-Atlas, Dylan, Caitlin Murphy, Anna Schleifer, and Nicholas Grue. "Temporal complementarity and value of wind-PV hybrid systems across the United States." Renewable Energy 201 (2022): 111-123, doi:10.1016/j.renene.2022.10.060; and 2) Murphy, Caitlin, Harrison-Atlas, Dylan, Nicholas Grue, Vahan Gevorgian, Juan Gallego-Calderon, Shiloh Elliot and Thomas Mosier. “A Resource Assessment for FlexPower”. NREL Technical Report.

Harrison-Atlas, Dylan↗
DOE OSTI2023

Analysis of hydrogen infrastructure for the feasibility, economics, and sustainability of a fuel cell powered data center

Data centers used for internet data services, cloud computing, and/or data storage consume vast amounts of electricity and are increasing rapidly in capacity. Consequently, their power consumption has raised concerns about energy sustainability and environmental impacts. Large-scale, on-site renewable energy could help reduce data centers’ carbon footprint; however, wind and solar power alone cannot provide an uninterrupted power supply to computer servers due to their natural variability. Instead, reliable power integration can be achieved by using fuel cells powered by hydrogen from sustainable resources (e.g., wind and solar energy). Establishing a hydrogen infrastructure will be critical for realizing these benefits and establishing fuel cells as a viable power source for data centers. Here, to facilitate the development of novel carbon-free fuel cell data enters, this paper presents renewable power integrated with hydrogen infrastructures in four scenarios to provide reliable hydrogen supply from production to storage. Various paths were analyzed toward a hydrogen supply infrastructure by determining the proper component sizes and calculating the cost of meeting the server load. We used a microgrid modeling software, Hybrid Optimization of Multiple Energy Resources (HOMER), and studied the feasibility of fuel cell powered data centers employing renewable hydrogen. The modeling results show various renewable integration configurations to meet reliable and sustainable power requirement under four scenarios for a carbon-free data center.

08 HYDROGEN↗
DOE OSTI2021

Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems

Marine renewable energy (MRE) resources are highly predictable and persistent sources of energy, when compared to other renewable sources like wind and solar. These lend them favorably for potential grid applications, particularly for coastal/island power systems where their generation potential is high. Island power systems, on the other hand, are either supported by onsite generation or by transported energy from the mainland grid. Therefore, robustness of grid operations depend heavily on the diversity of onsite generation resources and the reliability of the power transportation medium. Issues relating to either of these two factors may lead to impediments in smooth and reliable operation of the power system. Analyzing and quantifying operational risks for such island power systems with diverse non-conventional generation portfolios through conventional techniques can also prove to be cumbersome, often requiring multiple different inputs. Therefore, in this paper, we firstly present a novel, purely data-driven formulation which quantifies the operational reliability of such island power systems through minimal input data. Specifically, our proposed methodology only relies on historical knowledge of typical hourly load and generation profiles to quantify associated operational risks. Subsequently, we use our proposed formulation to evaluate the effectiveness of MRE resources (over other renewable resources like wind and solar) in providing resilience benefits to island power systems. The proposed formulation is demonstrated with a case study for an island power system in Nantucket, MA.

Chalishazar, Vishvas H.↗
DOE OSTI2024

MODAQ (Modular Offshore Data Acquisition System) [SWR-24-58]

The National Renewable Energy Laboratory (NREL) has developed a data acquisition system to support laboratory (component) and in-water validation and testing of Marine Renewable Energy (MRE) technologies. The Modular Ocean Data AcQuisition System (MODAQ) is a robust system built on industrial grade hardware that can acquire high-quality and summary data from many sensor and instrument types. MODAQ includes modules to perform real-time QC screening, system health monitoring, and send alerts. MODAQ is also part of a larger data management, processing, visualization, and archiving system.

Raye, Robert↗
DOE OSTI2024

Heave Plate Hydrodynamic Coefficients for Floating Offshore Wind Turbines - A Compilation of Data

The National Renewable Energy Laboratory's OpenFAST software is utilized by academics and industry professionals alike to simulate offshore wind turbines. The software's modeling of hydrodynamic loads on heave plates attached to these structures relies on user-specified hydrodynamic coefficients. To guide the proper selection of these coefficients and potentially develop a new functionality within OpenFAST that automatically prescribes and/or adjusts the heave-plate hydrodynamic coefficients, we review past literature to examine the dependence of the added mass, damping, and drag coefficients on various relevant nondimensional parameters, including the Keulegan-Carpenter number, the frequency parameter, and the plate thickness ratio. Existing data in the literature show strong dependence of the hydrodynamic coefficients on the Keulegan-Carpenter number. We observe consistent trends across a range of different plate geometry, plate porosity, and flow conditions. Secondary dependence of the coefficients on the frequency parameter and plate thickness ratio is also present.

added mass↗
DOE OSTI2025

Assessment of BQ-9000 Biodiesel Properties for 2024

This is the eighth in a series of reports documenting the quality of biodiesel from U.S.- and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry's voluntary quality assurance program. Participants provided monthly data on critical quality parameters for calendar year 2024 with quality data provided to a team of experts, who removed any identifying company information and provided anonymized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. Similar to 2023, data on kinematic viscosity, sulfated ash, distillation temperature, carbon residue, and cetane were collected, as well as individual levels of sodium, potassium, calcium, and magnesium.

09 BIOMASS FUELS↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2023

Statistical Treatise on Critical Biodiesel (B100) Quality Properties in the United States from 2004-2022

The quality of neat biodiesel (B100) is critical for ensuring biodiesel blends used in diesel-powered vehicles do not adversely impact engine performance. In the United States, B100 is required to meet ASTM International's purity and fuel property requirements in D6751, "Standard Specification for Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels." Here we review the development of this standard for the different grades of B100. The BQ-9000 program, which currently covers over 90% of U.S. and Canadian production volumes, is also described. Engine and original equipment manufacturers have expressed a desire for credible, third-party data on values of various ASTM B100 properties in the commercial market to inform their efforts to address future emissions and durability requirements. To address this need, the National Renewable Energy Laboratory published data from analysis and testing of monthly samples from BQ-9000 producers from calendar years 2017-2022, resulting in 300-500 data points (i.e., individual fuel samples) per year. These data and a statistical evaluation of properties including the average, mean, standard deviation, and 95th or 5th percentile are presented. The results show very consistent and high-quality B100 produced in this time frame. Additionally, we review the results of earlier quality surveys conducted in the United States between 2004 and 2011, revealing how quality has changed over time in response to ASTM standard revisions, as well as the significant overall improvement in quality for modern biodiesel production.

ASTM standard↗
DOE OSTI2024

Assessment of BQ-9000 Biodiesel Properties for 2023

This is the seventh in a series of reports documenting the quality of biodiesel from U.S.- and Canadian-based producers that participate in the BQ-9000 program, the biodiesel industry’s voluntary quality assurance program. Participants provided monthly data on critical quality parameters for calendar year 2023 with quality data provided to a team of experts, who removed any identifying company information and provided anonymized data to the National Renewable Energy Laboratory (NREL) for statistical analysis. New for 2023, data on kinematic viscosity, sulfated ash, distillation temperature, carbon residue, and cetane were collected, as well as individual levels of sodium, potassium, calcium, and magnesium. Critical quality parameters analyzed are listed in Table ES-1 with descriptive statistics.

09 BIOMASS FUELS↗
DOE OSTI2024

A Data-Driven Exploration of the Impact of Renewable Energy on Inter-Area Oscillations in the U.S. Eastern Interconnection

As increasing amounts of renewable energy (RE) resources are incorporated into the bulk-power grid, power system oscillations are expected to change. This work investigates how RE generation impacts the frequency and damping ratio (DR) of two dominant inter-area modes in the U.S. Eastern Interconnection (EI) using regularly updated estimates collected over a 12-month period. Quantile regression is used to derive the correlation between operating conditions and mode properties, and a bootstrap method is used to quantify the uncertainty associated with the correlation estimates. Results show that with an increase in system load, the frequency of a mode decreases and DR increases. Evidence that increasing RE generation results in an increase in frequency and decline in DR was found for one of the two modes studied. This work shows that increasing RE levels will impact the properties of inter-area oscillations in the EI, but it does not indicate the presence of immediate threats to grid stability. The outlined approach can be used to periodically assess changing mode properties as RE levels continue to grow and flag stability concerns before they become serious reliability threats.

Inter-area oscillation, mode meters, quantile regr↗
DOE OSTI2022

Status and Trends in the U.S. Voluntary Green Power Market (2021 Data)

This data book provides certain data behind figures and tables found in the NREL presentation "Status and Trends in the Voluntary Market (2021 Data)." These data reflect estimates based on the best available data. Excluded Data This data book excludes data for certain figures using confidential survey data or purchased data for which NREL does not own the rights. In particular, renewable energy certificate (REC) price data are not included in this data book Rounding: Some estimates may be slightly inconsistent across tabs due to estimate rounding Citation for the data: E. O'Shaughnessy and J. Heeter. 2022. Status and Trends in the Voluntary Market. Golden, CO: NREL. For more information: contact Jenny Heeter (jenny.heeter@nrel.gov). For more information on voluntary green power markets see NREL's resources at https://www.nrel.gov/analysis/green-power.html. Acronyms CCA Community choice aggregation MWh Megawatt hour PPA Power purchase agreement RECs Renewable energy certificate

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Data and Tools for Energy Planning and Analysis

The National Renewable Energy Laboratory (NREL) creates widely used data and tools to facilitate energy system planning and analysis. These software tools have been developed for complex research problems and perfected over real-world applications and laboratory validations. Some tools are award winners, others are open-source data explorers, and all are rigorously designed to empower decision-makers with accurate and accessible information. This software selection shows how NREL resources can help stakeholders achieve a clean, just, and resilient energy transformation.

data↗
DOE OSTI2022

Application of Advanced Metering Infrastructure Data to Advanced Utility System Operations: Cooperative Research and Development, CRADA Number CRD-17-00712 (Final Report)

The primary objective of the proposed project is to evaluate the capabilities of Advanced Metering Infrastructure (AMI) based controls for grid operations. This includes monitoring and control at the secondary transformer level using existing AMI infrastructure. In addition, some of the challenges of using a model-less control will also be identified. The lessons learned will be disseminated to other utilities around the country through peer-reviewed publications with the consent of SDG&E. The scope of the work includes using data from SDG&E's existing AMI infrastructure and working with third party vendors selected by SDG&E. The grid edge monitoring systems and controls provided by the selected vendors will perform grid operations using AMI data. The National Renewable Energy Laboratory (NREL) will perform a software and controller-based evaluation of the grid edge controller, intelligent devices and sensors, at the Energy System Integration Facility (ESIF) laboratory.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Exploring the interplay between distributed wind generators and solar photovoltaic systems

This study investigates the spatial and temporal dynamics of wind and solar energy generation across the continental United States, focusing on energy availability, reliability, variability, and cooperation. Using data from the National Renewable Energy Laboratory, we analyze the performance of wind turbines and photovoltaic systems, revealing distinct patterns in energy production and reliability. The classification of wind and solar zones based on energy availability and reliability provides valuable insights for renewable energy planning and grid integration strategies. Overall, this methodology contributes to understanding wind and solar interactions, which will lead to informing effective renewable energy deployment and grid integration efforts.

14 SOLAR ENERGY↗
DOE OSTI2023

High-Resolution Synthetic Solar Irradiance Sequence Generation: An LSTM-Based Generative Adversarial Network

The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources' primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy Laboratory. The results of the case studies demonstrate the proposed approach's capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.

dynamic scheduling↗