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DOE OSTI2025

Cambium 2024 Scenario Descriptions and Documentation

The National Renewable Energy Laboratory's (NREL's) Cambium data sets are annually released sets of simulated hourly data for a range of modeled futures of the U.S. electric sector with metrics designed to be useful for long-term decision- making. The 2024 Cambium data set is the fifth annual release. The data sets are a companion product to NREL's Standard Scenarios, which are likewise released annually and are a set of projections of how the U.S. electric sector could evolve across a suite of different potential futures, but covering more scenarios with less temporal granularity. Information about Cambium and related publications can be found at https://www.nrel.gov/analysis/cambium.html, and the Cambium data sets can be viewed and downloaded at https://scenarioviewer.nrel.gov/. In this documentation, we describe Cambium 2024's scenarios, define the metrics, and document the Cambium-specific methods for calculating those metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Sensitivity of Next-Generation CMB Surveys to Neutrinos and Other Light Relics

Neutrinos and other light relics leave characteristic imprints in the cosmic microwave background anisotropies, making their observation a sensitive probe of the particle content and thermal history of the early universe. The energy density in these relativistic species is parameterized by their effective number $N_\mathrm{eff}$. Measuring this parameter at the percent level, which is a long-standing science goal of CMB-S4 and other experiments, would test a wide range of well-motivated physics within and beyond the Standard Model of particle physics. In this paper, we present Fisher-matrix forecasts of the projected sensitivity to $N_\mathrm{eff}$ of several CMB-S4 survey configurations considered during its extensive design phase. The conceptual design reaches $σ(N_\mathrm{eff}) < 0.03$ over its seven-year observing period, while the revised configuration achieves the same precision over a longer timescale. We complement these results with a cosmic-variance-limited survey over the same multipole range to quantify the room for improvement accessible with additional instrumental, observational, and theoretical efforts. Finally, we discuss the broad implications of precise $N_\mathrm{eff}$ measurements for the radiation sector, big bang nucleosynthesis, light thermal relics, and other early-universe physics. The forecasts presented in this work are performed with the publicly released DRAFT (Dark Radiation Anisotropy Flowdown Team) tool. It provides an end-to-end pipeline from simulated foreground maps and component separation to delensing and projected sensitivities for any cosmological parameter, and it can be directly applied to other cosmic microwave background survey designs.

Trendafilova, Cynthia [Case Western Reserve U., CE↗
DOE OSTI2022

The impacts of COVID-19 on clean energy labor markets: Evidence from multifaceted analysis of public health interventions and COVID-health factors

COVID-19 pandemic has affected clean energy labor market. Using real-time job vacancy data, this study analyzes the impacts of the pandemic on the U.S. clean energy labor market in 2020, including biomass, energy efficiency (EE), electric vehicle (EV), power/microgrid, solar, and wind industries. This study identifies how COVID-health factors and public health interventions influence clean energy job availability during the early COVID pandemic. Overall, California had the most energy jobs and experienced a significant decrease in April 2020. EV and solar had the highest percentages of job vacancies during the pandemic in general. Still, lockdowns had the most severe influence on EE and wind jobs. Stay-at-home orders negatively affected clean energy job vacancies in biomass, EV, power/microgrid, and wind. Social-gathering restrictions, however, did not have much influence. Increased COVID tests at the state level had the strongest and most positive influence on clean energy job postings, indicating the importance of a state's ability to manage public health infrastructure or crisis issues. COVID hospitalizations negatively influenced the job vacancies in biomass and wind but did not affect the other four sectors; conversely, as COVID death numbers increased, the number of jobs in biomass, EV, power grid, solar, and wind decreased, but not in EE jobs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

What can we learn from decarbonization of the energy sector?

This is a chapter that will be part of an Open Source e-book published by the International Water Association Publishing House. The co-editors are Professors Z. Jason Ren and Krishna Pagilla. Decarbonizing water and wastewater treatment is an enormous challenge, but it is substantially smaller, in total carbon emissions, than decarbonizing the energy sector. When planning, executing, and assessing strategies for decarbonizing the water sector, water experts should partner with the energy sector and heed that sector’s lessons-learned in its ongoing process of decarbonization. In the energy sector, decarbonization pathways can be as simple as a supply-side technology that converts fuel to electricity more efficiently, reducing net carbon emissions for every kilowatt-hour generated. The pathways can be much more complex, however, as is the case with the demand-side reordering of behavior as seen with online shopping or working from home during a public health crisis. Both of those pathways reduce demand for private-vehicle fuel and shift some work, and associated carbon emissions, to other parts of the economy. This chapter explores decarbonization pathways that have been followed by the energy sector and assesses their applicability to the water sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Search for Higgs bosons decaying into new spin-0 or spin-1 particles in four-lepton final states with the ATLAS detector with 139 fb -1 of pp collision data at $\sqrt{s}$ = 13 TeV

Searches are conducted for new spin-0 or spin-1 bosons using events where a Higgs boson with mass 125 GeV decays into four leptons (ℓ = e, μ). This decay is presumed to occur via an intermediate state which contains two on-shell, promptly decaying bosons: H → XX/ZX → 4ℓ, where the new boson X has a mass between 1 and 60 GeV. The search uses pp collision data collected with the ATLAS detector at the LHC with an integrated luminosity of 139 fb -1 at a centre-of-mass energy $\sqrt{s}$ = 13 TeV. The data are found to be consistent with Standard Model expectations. Limits are set on fiducial cross sections and on the branching ratio of the Higgs boson to decay into XX/ZX, improving those from previous publications by a factor between two and four. Limits are also set on mixing parameters relevant in extensions of the Standard Model containing a dark sector where X is interpreted to be a dark boson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Storage Futures Study - Distributed Solar and Storage Outlook: Methodology and Scenarios

This presentation discusses the fourth report in NREL’s Storage Futures Study (SFS) publications. The SFS is a multiyear research project that explores the role and impact of energy storage in the evolution and operation of the U.S. power sector. The SFS is designed to examine the potential impact of energy storage technology advancement on the deployment of utility-scale storage and the adoption of distributed storage, and the implications for future power system infrastructure investment and operations. This report describes the expanded capabilities of the Distributed Generation Market Demand (dGen) model to analyze the economics of distributed (behind-the-meter) PV paired with battery storage systems and presents projections of adoption for the contiguous United States out to 2050 under a range of scenarios. These scenarios use technology cost and performance assumptions consistent with the National Renewable Energy Laboratory’s 2020 Standard Scenarios paired with updated battery cost projections and existing policies. Additional scenarios evaluate sensitivities to the value of backup power and DER compensation mechanisms, collectively characterizing the future potential for behind-the-meter storage and identifying key drivers of adoption. Adoption projections of DER and battery storage at high spatial and temporal resolution, as presented in this report, can enable informed planning of technical infrastructure that can help planners capture the benefits and mitigate challenges to support the ongoing trend toward distributed electricity generation.

backup power↗
DOE OSTI2024

helios: An R package to process heating and cooling degrees for GCAM

helios is an open-source R package that estimates population-weighted heating and cooling degree-hours (HDH and CDH) and degree-days (HDD and CDD) at various temporal (e.g., energy dispatch segments, monthly, yearly) and spatial scales (e.g., U.S. states, global political regions, countries). The degree hour and degree day outputs from helios are used to inform electricity demand load in the Global Change Analysis Model (GCAM) as well as in GCAM-USA (which is the version of GCAM with U.S. state-level details). helios uses a workflow with four steps: processing raw data; calculating heating and cooling degrees; visualizing performance diagnostics; and outputing results in various formats. There are two sources of widely-used climate data compatible with helios: (1) hourly climate data with 12-km resolution that are dynamically downscaled with the Weather Research and Forecasting (WRF) model and projected using a thermal global warming (TGW) approach; and (2) daily climate data with 0.5-degree resolution from the Coupled Model Intercomparison Project (CMIP) that is bias-adjusted and statistical downscaled by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In summary, helios is a model that standardizes methodology of heating and cooling degrees-hours and degree-days using publicly available data and advance the understanding of the impact of spatial and temporal temperature variability on building energy services.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cambium 2023 Scenario Descriptions and Documentation

The National Renewable Energy Laboratory's (NREL's) Cambium data sets are annually released sets of simulated hourly emission, cost, and operational data for a range of modeled futures of the U.S. electric sector with metrics designed to be useful for long-term decision- making. The 2023 Cambium data set is the fourth annual release. The data sets are a companion product to NREL's Standard Scenarios, which are likewise released annually and are a set of projections of how the U.S. electric sector could evolve across a suite of different potential futures, but covering more scenarios with less temporal granularity (Gagnon et al. 2024). Information about Cambium and related publications can be found at https://www.nrel.gov/analysis/cambium.html, and the Cambium data sets can be viewed and downloaded at https://scenarioviewer.nrel.gov/. In this documentation, we describe Cambium 2023's scenarios, define the metrics, and document the Cambium-specific methods for calculating those metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Catalytic Fast Pyrolysis for Chemicals: Cooperative Research and Development, CRADA Number CRD-16-00637 (Final Report)

The purpose of this partnership between the ExxonMobil Chemical Company and NREL is to advance the state of the art for converting biomass to liquid hydrocarbons using catalytic fast pyrolysis (CFP), with particular emphasis on directing and improving yields of such liquid hydrocarbons for uses in commodity chemical markets. CFP of biomass is a promising route for the production of renewable fuels and chemicals, and the key driver for economic viability is carbon yield to valuable products. The majority of prior CFP work has been targeted at fuel production, but this CRADA seeks to pursue routes to chemical co-products, thereby enhancing the overall economic viability of CFP. This project is beneficial to the public because it advances a technology that could one day provide renewable fuels and chemicals to our economy, thus improving the sustainability of our transportation and chemical manufacturing sectors.

09 BIOMASS FUELS↗
DOE OSTI2024

2023 Standard Scenarios Report: A U.S. Electricity Sector Outlook

This report, originally published in December 2023, has been revised in January 2024. There were three changes in the revision. First, Anthony Lopez was added as a contributing author, as he had been erroneously omitted in the original publication. Second, in appendix section A.1 the cost of upgrading a hydrogen combustion turbine from a natural gas turbine was erroneously reported as 20% when it should have been 33%. Lastly, appendix Figure A-8 was erroneously a duplication of the same figure from the Standard Scenarios 2022, it has now been updated to reflect this year’s data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗
DOE OSTI2023

Grid Interconnection and Renewables Deployment Related Air Quality and Human Health Benefits in Southeast Asia

The Association of Southeast Asian Nations (ASEAN) has increasingly focused on multilateral electricity trade, improved grid resiliency and modernization. These opportunities in the power sector have been studied through the ASEAN Interconnection Masterplan Studies, the most recent of which is the ASEAN Interconnection Masterplan Study III (AIMS III). AIMS III assessed four different scenarios of power system in the ASEAN countries that include a base scenario and three additional scenarios (called optimum RE, ASEAN RE, and High RE) considering different levels of deployment of renewable energy (RE) and cross-border generation and trade of electricity for three years (2025, 2030, 2040). In this work, we quantify the potential air quality and public health co-benefits of AIMS III scenarios. For a rapidly expanding, energy hungry region like SE Asia generation is power generation is expected to increase significantly. The AIMS III scenarios' capacity expansion modeling suggest that: 1) Generation nearly doubles from 2025 to 2040 in all four AIMS III scenarios; 2) Most of the increased generation is met by coal in all four AIMS III scenarios; 3) While renewables generation increases in all four AIMS III scenarios, the fraction of total generation (its share) generally decreases because of the much greater increase in non-renewable sources, mostly coal. Only in the High RE Target scenario do renewables represent a higher share in 2040 than in 2025, though even here it is at the expense of natural gas rather than coal; 4) As a result, emissions of gaseous and aerosol pollutants increase significantly in all scenarios. Because emissions increase in 2040 compared to 2025 for all four AIMS III scenarios, so do PM2.5 concentrations. Even for the High RE Target scenario (the scenario with highest share of renewable energy), compared to the Base scenario in 2025, in 2040 PM2.5 concentrations are higher. Compared to the Base scenario in the same years, the Optimum RE and ASEAN RE Target scenarios do not differ very much from the Base in terms of PM2.5 concentration. The High RE Target scenario, on the other hand, yields noticeably lower PM2.5 concentration. For example, in 2040 the population-weighted decrease in annual average PM2.5 concentration in the High RE Target scenario relative to the Base scenario is 0.5 ug m-3. Compared to the Base scenario in 2040, each of the alternative AIMS III scenarios is estimated to result in net reductions in power-sector air quality-related excess mortality in the ASEAN region. Yet there are a few countries for which the Optimum RE and ASEAN RE Target result in increases in PM2.5-related excess mortality (Thailand and Vietnam for the ASEAN RE Target scenario and Thailand for the Optimum RE scenario). However, for the High RE Target scenario, all countries benefit and find reductions in excess mortality resulting from the power sector scenarios modeled in AIMS III with regionwide mortality decreasing by 16,000 compared to the Base scenario in 2040. In summary, our analysis finds that changing power generation emissions is a crucial lever for improving public health in ASEAN member countries and provides a pathway for policymakers to make decision backed by a realistic power sector expansion and air quality analyses.

air quality↗
DOE OSTI2021

Dishwashers in the Residential Sector: A Survey of Product Characteristics, Usage, and Consumer Preferences

Data on consumer purchasing decisions, usage, and behaviors relating to residential appliances help to inform technical and economic analyses related to the energy and water used by those appliances, including dishwashers. Existing publicly available data for dishwashers include two regularly conducted national surveys that describe dishwasher ownership and usage. The United States (US) Department of Energy’s (DOE) Energy Information Administration’s Residential Energy Consumption Survey (RECS) records the presence of a dishwasher in the home, the numbers of times per week the dishwasher is operated, and the dishwasher age, along with household demographic characteristics. The US Census Bureau’s American Housing Survey (AHS) also records the presence of a dishwasher in the home along with household demographic characteristics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Quantifying Annual Industrial Locomotive Energy Consumption in the United States

While US Class 1 railroad locomotive rosters and annual fuel consumption are well-documented, considerably less is known regarding the overall energy consumption of operations involving industrial locomotives. To determine the energy savings potential of this rail operating sector, the objective of this research is to develop an inventory of US industrial locomotives and a baseline estimate of their annual energy consumption. Creating an industrial locomotives roster from public data is challenging given their diverse ownership by shippers or leasing companies, and operating locales largely out of public view. By cross-referencing public data on locomotive reporting marks, serial numbers, online images and aerial images, the project team confirmed the age, model and horsepower of over one thousand industrial locomotives. Estimating energy consumption is complicated by the variability in industrial locomotive types and power ratings, and extreme differences in duty cycles and utilization. Given these limitations, using quantified case study examples and adjustments to standard EPA line-haul and switching duty cycles, bounds on the magnitude of annual US industrial locomotive energy consumption were estimated.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Facility-Level Industry Representation for Decarbonization Modeling [Slides]

The largest facilities of energy-intensive materials processing industries are disproportionate contributors to U.S. greenhouse gas (GHG) emissions. In general, industrial energy system modeling for the United States currently represents industrial demands at a much lower resolution than other end-use sector modeling. Therefore, characterizing even a subset of energy-intensive materials processing facilities will capture a significant portion of industrial GHG emissions. In order to further the development of publicly-available data to support modeling of industrial decarbonization, we summarize a set of approaches and results for characterizing the location, energy intensity and mix, process emissions intensity, and general production technology of existing clinker, ammonia, and iron and steel facilities in the United States. We also characterize facilities that represent options for reducing GHG emissions from each industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Mini Guide on Transportation Electrification: State-Level Roles and Collaboration among Public Utility Commissions, State Energy Offices, and Departments of Transportation

About the NCEP Mini Guide Series: The National Council on Electricity Policy (NCEP) is a platform for all state-level electricity decision makers to share and learn from diverse perspectives on the evolving electricity sector. The NCEP mini guide series promotes this dialogue by highlighting examples of successful engagement across its members. Each mini guide features collaborative approaches, lessons learned, and interviews with leading state and local decision makers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Application of Artificial Intelligence/Machine Learning to Operations Research

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Biodiversity and Global Health: Intersection of Health, Security, and the Environment

One of the biggest consequences of large-scale environmental change is the loss of biodiversity. Recent studies predict a loss of 1 million species in the near future. Biodiversity loss and land use change through anthropogenic disturbance are known to affect disease exposure, disease severity, and disease impacts. It is becoming increasingly clear that biodiversity loss will lead to an increase in infectious diseases in certain regions. Changes in biodiversity may contain important signatures for prediction of infectious diseases and outbreaks. Here, we argue that areas of high or rapid biodiversity loss will be critical to monitor under the umbrella of global health security. Global Health Security consists of the actions and activities required to reduce the impact of public health events in populations living in different geographic regions and across international borders. Like infectious diseases, climate change and other causes of environmental changes do not respect international borders and are becoming more widespread in all parts of the world. Here, the relationships among biodiversity, climate and environmental changes, and pathogen prevalence are dynamic, context dependent, and complex. Accounting for this complexity requires the inclusion of the environment sector, presently missing, when evaluating the potential of global health security actions for mitigating disease risks.

59 BASIC BIOLOGICAL SCIENCES↗