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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

JUST-R metrics for considering energy justice in early-stage energy research

We report achieving sustainable decarbonization of the energy sector requires implementing and improving energy technologies while simultaneously managing sources of social inequity in the energy system. Centering energy justice, which has "the goal of achieving equity in both the social and economic participation in the energy system, while also remediating social, economic, and health burdens on those historically harmed by the energy system," in the transition to clean energy has become an increasingly urgent priority for social scientists, policymakers, and community activists alike. However, late-stage consideration of social impacts of energy technologies may result in identifying inequities only after substantial time, money, and effort have been expended on research and development (R&D). This issue is exemplified by concerns over environmental and human health impacts related to cobalt in lithium-ion batteries, which has spurred research into alternatives only after decades of R&D and the establishment of supply chains, infrastructure, and markets for cobalt-containing chemistries. Other examples include issues with land use and resource consumption related to first-generation biofuel feedstocks as well as occupational hazards and pollution associated with photovoltaics manufacturing. In all these cases, subsequent R&D to improve technologies or processes cannot undo the effects already experienced. Incorporating energy justice from the earliest stage of R&D will enable more just technology implementation, but integrating justice considerations into early-stage research is a challenge due to a lack of tools to assess and manage them. To fill this gap, we center early-stage research to develop the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - energy justice metrics specifically targeted at early-stage researchers to assess their work on an immediate timescale. By applying these metrics to a case study focused on materials for next-generation photovoltaics, we highlight potential benefits and barriers to implementing this framework in early-stage research and discuss necessary institutional and individual actions needed for researchers to effectively leverage the tool to incorporate justice-focused criteria into R&D decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Innovations in Renewable Energy Technologies, Systems, and Energy Analysis

Costs for renewable energy technologies have declined rapidly in the past decade and their use for residential, commercial, and utility scale electricity has therefore grown exponentially. As the energy mix diversifies, energy analysis becomes increasing important, requiring renewable energy to integrate with other reduced emission energy sources, such as small modular nuclear reactors and carbon capture and utilization. Energy analysis can also explore the expansion of clean energy into new sectors, such as industrial processes and electrification. The U.S. National Renewable Energy Laboratory (NREL) and the Joint Institute for Strategic Energy Analysis (JISEA), which is a partnership of NREL and universities, study the integration and application of clean energy technologies into existing and new sectors. This presentation includes an overview of NREL and JISEA and summarizes innovations in renewable energy technologies and energy systems analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗

DOE OSTI2023

Mobility Energy Productivity and Equity: E-Bike Impacts for Low-Income Essential Workers in Denver

New mobility technologies such as electrified and shared mobility, combined with polices and incentive programs, are emerging to help address sustainability and equity issues in transportation planning. However, it can be difficult to understand the impacts of novel mobility trends and emerging modes on energy-efficient access. This is owing to a lack of (1) open-source tools enabling rapid data collection, and (2) open-source metrics that consider multimodal, multiactivity access and mobility within the contexts of sustainability and equity. Here, this paper addresses the topic of improving evaluation of transportation modes and incentive programs by integrating an open-source platform for tracking human travel data—the Open Platform for Agile Trip Heuristics (OpenPATH)—with a mobility metric that quantifies the efficiency of a region’s transportation system: Mobility Energy Productivity (MEP). Integration is demonstrated in the context of pilot programs in Colorado, where low-income essential workers were provided with electric bikes (e-bikes). OpenPATH-informed MEP calculations showed that several locations in downtown Denver provided comparable time-, cost-, and energy-efficient access to opportunities using e-bikes compared with driving. Additionally, providing e-bikes to low-income essential workers was found to be meaningful, as they utilized e-bikes the most to commute, despite driving still being their most utilized mode and the mode with highest MEP scores in Denver. We show how data collected from open-source tools coupled with robust metrics such as MEP can help evaluate the impacts of emerging mobility options. This could support developing policies to incentivize novel modes to achieve greater levels of sustainable, equitable, and efficient access.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortages with uncertainty-aware and physics-informed machine learning

Focal Area(s): The focus area is predictive modeling through the use of AI techniques and AI-derived model components with a particular emphasis on extreme weather in Atmospheric Science and power outages and shortages in Energy Science. Science Challenge: Predicting weather extremes (e.g., heavy precipitation, strong wind, and large hailstones), and weather-related power system outages and shortages can mitigate economic losses, save lives, support renewables integration, and improve power system resiliency. However, currently, the poor reliability and large uncertainty associated with the weather extreme prediction in the current climate models make the problem intractable. The key challenges are: (1) physical factors like green-house gases (GHGs), aerosols, and land use and land cover (LULC) can significantly impact extreme storms, but the understanding of these impacts is limited, particularly globally; (2) the convective permitting resolutions needed to model severe convective storms and their impacts are computationally prohibitive with global climate models (GCMs); (3) interactions between weather extremes and power system outages are complex and subject to great uncertainty. Current outage prediction models are short lead (~ 3 days), which do not allow for long-time planning of energy production and distribution. Moreover, we have limited capacity to predict weather events leading to sustained shortages in a renewable-energy-dominated power system. These challenges drive motivation for mechanistic understanding and reliable and efficient predictive modeling of extremes and their impacts from the sub-seasonal to long term projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Common occupational classification system amendments for Accelerator Science and Engineering workforce

A common system to classify occupations and skills is essential to support accelerator workforce planning between the Department of Energy (DOE) National Laboratories. In 2025, ten DOE laboratories conducted a census of their accelerator science and engineering workforce and projected their accelerator workforce needs for the next ten years. To support this, revision 3 of the “Common Occupational Classification System” (COCS) was used as a common taxonomy. Modifications were made to include accelerator-specific skills and specialisms. COCS was originally developed for DOE Office of Environmental Restoration and Waste Management in 1996. The framework provides a high-level functional structure that can be expanded with further occupations and specialisms and remains well aligned to DOE laboratory roles nearly 30 years later. Accelerator occupations and specialisms were added to the framework for the 2025 effort to quantify the accelerator workforce. This document is a companion document to COCS and provides a definition for these new occupations and specialisms that do not appear in COCS. Proceeding with a stable taxonomy is seen as essential such that its use becomes easier each year; the taxonomy as used for the 2025 effort is recommended to be continued.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Reserve and energy scarcity pricing in United States power markets: A comparative review of principles and practices

Here, errors in forecasting load and renewable-based generation in restructured power systems mean that independent system operators (ISOs) must procure sufficient operating reserves to keep the real-time operation of the system reliable and secure. But when procured reserves turn out to be insufficient in real-time due to the lack of resource capacity or ramp capability, operators often set higher prices for reserves and energy to encourage more supply, and to motivate consumers to decrease usage or shift it to other times. This procedure, which is called scarcity or shortage pricing, is a core feature of U.S. electricity markets. It is receiving increased attention from market designers and stakeholders because scarcity will become more important for spot price formation in the future with the increased penetration of zero-marginal cost renewables, and the shrinking role of fuel costs in setting prices. Scarcity pricing is implemented in various ways by different ISOs. These differences have practical implications for the level of prices and incentives for investment, operations, and demand modification. In this paper, general approaches and specific calculation procedures for reserve and energy scarcity pricing practices and calculations across the seven ISO-based U.S. power markets are reviewed and compared. A consistent terminology is used to facilitate the comparison. Current scarcity pricing practices are grouped into three approaches: (1) imposing an adder after the spot market is run; (2) including stepwise demand curves within market clearing procedures for non-contingency reserve products (e.g., the novel flexiramp product), which tends to yield longer right tails for energy scarcity premium curves; and (3) having stepwise demand curves for traditional contingency reserve products only, which results in shorter right tails in energy scarcity curves. A generic numerical example is presented to highlight the large practical differences among the reserve scarcity pricing approaches and specific implementations. To further investigate factors that contribute the most to demand curves differences among ISOs, a sensitivity analysis is performed. This analysis shows that the largest source of differences among the curves is the scarcity prices assumed in the case of severe scarcity, while the number of steps used and whether flexiramp is considered also yields important differences in scarcity prices. As renewable penetration increases, it will become increasingly crucial to employ administrative demand curves so that spot prices more effectively motivate supply and demand adjustments exactly when and where they are needed. This study shows that the different assumptions yield very different scarcity premiums for reserves and energy, and are likely to provide divergent incentives for resources to respond to shortages. It is concluded that to promote market efficiency, a reserve shortage demand curve should have at least three features: inclusion of the marginal value of reserve products at each shortage level, consideration of the magnitude and probability of supply contingencies, and avoidance of abrupt price discontinuities that can cause excessively volatile market outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Model, Design, and Control for Power Conversion in Wave Energy Converter System

Wave energy, if all been harvested along the U.S. coastline, can power around 65% of the energy consumption in U.S.. Comparing to other renewable energy sources like solar or wind, ocean wave can provide up to 90% of steady uptime. With the high energy density (2-3 kW/m 2 ), it can produce more energy with the same amount of installation area comparing to the energy density of wind turbine (0.6 kW/m 2 ) and solar panel(0.2 kW/m 2 ). The predictability of wave provides advantages like planning installation, power dispatching, and maintenance activities. Although with all these advantages, wave energy converter system is still in the research stage due to its high system cost per electricity production. One of the challenges that need to be solved is the irregularity from the wave motion that leads to high instantaneous peak power into the wave energy converter, which usually reaches up to 10 - 20 times of the average power. The high peak power will not only bring high mechanical/electrical stress but also result in an overrating design of the components in the system. Another obstacle that prevents the wave energy system from moving forward is the high testing cost from the validations in wave-energy-test sites or tank-test sites. A high-fidelity multi-disciplinary system model, including hydrodynamics, mechanical dynamics, electromagnetics, and power electronics, is needed to predict the behavior of the system and reduce the cost of design validation.

16 TIDAL AND WAVE POWER↗

NASA NTRS1990

Procedural error monitoring and smart checklists

Human beings make and usually detect errors routinely. The same mental processes that allow humans to cope with novel problems can also lead to error. Bill Rouse has argued that errors are not inherently bad but their consequences may be. He proposes the development of error-tolerant systems that detect errors and take steps to prevent the consequences of the error from occurring. Research should be done on self and automatic detection of random and unanticipated errors. For self detection, displays should be developed that make the consequences of errors immediately apparent. For example, electronic map displays graphically show the consequences of horizontal flight plan entry errors. Vertical profile displays should be developed to make apparent vertical flight planning errors. Other concepts such as energy circles could also help the crew detect gross flight planning errors. For automatic detection, systems should be developed that can track pilot activity, infer pilot intent and inform the crew of potential errors before their consequences are realized. Systems that perform a reasonableness check on flight plan modifications by checking route length and magnitude of course changes are simple examples. Another example would be a system that checked the aircraft's planned altitude against a data base of world terrain elevations. Information is given in viewgraph form.

Palmer, Everett↗

DOE OSTI2021

Enhancing Responsiveness and Resilience with Distributed Applications in the Grid

A number of trends are increasing the variability in electric power systems resulting in a need for new approaches in control. The increased variability is originating from distributed energy resources and an increasing demand for personalized energy choice from customers grid-edge. Electricity distribution systems continue to incorporate increasing numbers of intelligent end devices, automated switchgear, and sensing and measurement devices which are providing more visibility and control at the grid-edge. Traditional distribution system planning and operations techniques must evolve to meet the new complexity while continuing to deliver safe, reliable, and cost-effective energy. New technologies are enabling new capabilities at the grid-edge to respond to these needs. Distributed applications can deploy intelligence across the distribution grid with peer-to-peer communications and a shared data context for their local environment. These applications can cooperate with each other to respond quickly to changing grid conditions. This enables a layered coordination framework spanning the centralized Advanced Distribution Management System functions with wide area visibility to these new distributed applications running at intelligent devices and meters throughout the distribution system. This paper describes an open-architecture, open-source system approach for commercial scale integration of devices and intelligence at the grid-edge.

Ogle, James P.↗

DOE OSTI2026

Cybersecurity Standards for Distributed Energy Resources: Gaps and Harmonization Strategy

This report examines cybersecurity standards for Distributed Energy Resources (DERs) in light of their rapid growth and increasing integration into energy systems. It identifies critical gaps in existing frameworks, including inadequate coverage of DER-specific challenges, complexities in implementing comprehensive standards, integration issues with legacy systems, adoption hurdles for newer standards, and a lack of harmonization across regulatory landscapes. The analysis highlights vulnerabilities such as data integrity risks, unauthorized device control, and denial-of-service attacks across various DER technologies like solar PV, wind turbines, energy storage systems, and hydrogen fuel cells. The report proposes a harmonization strategy to address these deficiencies by developing unified cybersecurity requirements, certification programs, and training resources while fostering collaboration among stakeholders such as government agencies, industry groups, DER operators, manufacturers, and research institutions. A phased roadmap is outlined to refine and implement these measures through pilot testing and widespread adoption. Ultimately, the report underscores the urgent need for coordinated efforts to enhance DER cybersecurity and ensure the reliable operation of future energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Task 12 PV Sustainability - Mineral Resource Use Footprints of Residential PV Systems

Resource use intensity is often mentioned as one of the main characteristics of PV systems and PV electricity. Recently, the International Energy Agency published a report on the role of critical minerals in clean energy transitions. The Product Environmental Footprint pilot study on PV electricity quantified (among other environmental impacts) its abiotic depletion potential. So far, a comprehensive assessment of resource use impacts highlighting the different facets of its impacts is however lacking. For the first time, the resource use impacts of PV electricity are quantified simultaneously with four impact category indicators recommended or suggested by the Life Cycle Initiative hosted at UN Environment. The indicators cover distinctly different aspects of resource use, namely resource depletion with the Abiotic Depletion Potential, ultimate reserves (ADP UR ), economic resource scarcity with the Abiotic Depletion Potential, economic reserves (ADP ER ), resource quality with the Surplus Ore Potential, Ultimate Recoverable Resources (SOP URR ) and re-source criticality with the ESSENZ method. The resource use impacts caused from the generation of 1 kWh electricity with a residential scale photovoltaic (PV) system installed in Central Europe using mono- and multi-crystalline silicon panels and CdTe panels, respectively are quantified. The product system includes manufacture, use and end of life treatment (take back and recycling) of the PV panels, cabling, inverter and supporting structure, the supply chains of the raw materials and energy used in PV panel and inverter manufacture as well as transport logistics.

14 SOLAR ENERGY↗

DOE OSTI2025

Advance Distribution Management System (ADMS)

This presentation explores the transition from traditional distribution management systems to Advanced Distribution Management Systems (ADMS) as a foundation for smart grid development. It highlights the key benefits of ADMS, including enhanced reliability, improved operational efficiency, and increased situational awareness. The presentation also addresses common implementation challenges such as system integration, data management, and organizational readiness. It concludes with a forward-looking perspective on the evolving role of ADMS and essential takeaways for utilities and stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗