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DOE OSTI2023

More Than Recycling: How Should We Define Circularity Goals for PV in a Global Energy Transition? Preprint

Energy transition to carbon-free electricity is a crucial pillar of the Circular Economy. Renewable energy reduces environmental impacts and decarbonizes the production of other goods. But, manufacturing renewable energy sources, such as photovoltaic (PV) modules, require energy inputs that are currently carbon intensive. So, how do we decarbonize and circularize these critical technologies to achieve a sustainable energy transition? This work proposes that effective capacity-the installed capacity accounting for degradation rates and failures-is a critical metric to evaluate renewable energy technologies on the path toward circular economy and energy transitions. Our analyses also emphasize the importance of examining a suite of metrics incorporating mass and energy flows to identify potential tradeoffs and inform design or lifecycle management decisions holistically.

bifacial↗

DOE OSTI2023

Estimating Travel Energy Consumption Uncertainty Based on Inferred Travel Mode and Sensed Travel Length

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 8%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2023

Environmental and Social Justice Considerations in a Circular Economy

A consideration of environmental justice (EJ) and social justice (SJ) is critical to minimize the impacts of technology deployment on local communities. SJ and EJ impacts occur in specific geographic locations but can cover a wide range of effects (e.g., air pollution, access to clean water, jobs, wages, and education), making it challenging to determine which metrics are appropriate to evaluate and which data are required for qualitative or quantitative analysis. This issue is only exacerbated when the technology in question is at an early technology readiness level (TRL) without community specific data. Here, we present a framework for evaluating the human health, local environment, and job implications of processes that are at early to middle TRLs. Using a case study on enzymatic polyethylene terephthalate (PET) recycling (middle TRL), we demonstrate how to qualitatively and quantitatively assess these EJ and SJ metrics for a circular economy context and how to communicate the results in a manner beneficial to both researchers and local communities.

circular economy↗

DOE OSTI2024

Measuring Economy-Wide Circularity of the United States: An Input-Output Model in Mass Units

The ideal of creating closed material cycles by transforming the way we make, use and repurpose goods has become known as the circular economy. Besides its visionary appeal, material efficiency strategies central to circular economy can allow us to meet decarbonization goals that are otherwise out of reach. Production of basic materials such as cement, iron and steel and petrochemicals is one of the largest drivers of greenhouse gas emissions. Measuring circularity, and understanding its relationship with other sustainability metrics, however, is still difficult due to lack of data in mass units covering the entire economic system. Input-output (I-O) tables were originally created as a means of tracking the monetary flows that represent exchanges of goods and services within an economy, but have found additional uses in life cycle assessment and material flow accounts. In the traditional approach of environmentally extended I-O tables, emissions and energy data augment monetary flows. This approach can lead to price effects that distort physical quantities. Also, circular economy strategies and their goals relate to mass, not monetary flows. Therefore, it is best to simulate them using physical quantities. With these issues in mind, we have developed I-O tables in mass units to measure the flow of goods in the U.S. economy. This allows us to better measure how circular the economy really is, and how policy changes to affect this circularity may also affect decarbonization goals. Improving knowledge of these linkages could allow manufacturers to make changes in their production to achieve their sustainability and decarbonization targets. Our tool also provides a standard approach and public repository for data in physical units, making such data more available, useful, and meaningful. Following an established set of material flow metrics used by the European Union, we have used our tool to calculate material footprints over time differentiated by oil and gas versus other extractive industries. This approach also shows the relative trade balances of the U.S. in materials. We have developed a case study for the iron and steel sector, showing how different decarbonization scenarios affect not only economy-wide greenhouse gas emissions, but also total material use.

circular economy↗

DOE OSTI2020

Characterizing The Mechanical Properties of Polyurethane Foams

The Department of Energy has many radioactive facilities that are on the path of deactivation and decommissioning (D and D). These facilities can sit cold and dark for many years before final disposition, and must be maintained to ensure no radiological release occurs in the interim. Improvements and additions to D and D tool sets can greatly: Save time and money, Reduce worker risk. Conventional fixatives widely used often take the form of paints or films that are not readily applicable to 3-dimensional void spaces. Foams are one promising platform that may offer solutions to a number of contaminated problem sets such as: Gloveboxes, Pipes, Tanks. SRNL is working to characterize various commercial foams that would encapsulate the interior volume of a given space and are capable of immobilizing any remaining contamination. One key performance metric of these foams is how well they will perform in an accident scenario. To this end, SRNL is researching the mechanical properties of these foams to ensure that the material can withstand the environment of application while maintaining structural integrity. ASTM standard E3191-18 served as a guiding document for this project, outlining the requirements that foaming fixatives used for the mitigation of radioactive contamination need to meet before being implemented. Objective: Quantification of the mechanical properties of 6 commercial polyurethane foams was performed using multiple ASTM standards to record measurements for: Compression testing: Flexible Foams, Force required to produce 50% compression, Rigid Foams, Compressive and apparent modulus, Point of 10% core deformation, The 'Zero Deformation' point, Compressive strength, Yield point. Tensile testing: Tensile strength, Tensile stress, Percent elongation. Experiment 1: An electromechanical compressive tester (MTS Criterion Series 43) was used to evaluate 6 foams (4 flexible, 2 rigid). Per ASTM D1621, each rigid foam was compressed at 10% of the measured thickness per minute until the sample was 13% of it's original thickness. Per ASTM D35/4, each flexible foam was pre-flexed twice to 80% original thickness at a rate of 250 mm/min, then compressed to 80% original thickness at a rate of 50 mm/min. Experiment 2: An electromechanical tensile tester (MTS Criterion Series 43) was used to evaluate 6 foams (4 flexible, 2 rigid). Per ASTM D1623, rigid foams were pulled apart at a rate of 1.27 mm/min until the sample broke. Per ASTM D35/4, flexible foams were pulled apart at a rate of 500 mm/min until the sample broke. The strongest material in both compression and tensile testing scenarios was found to be the rigid intumescent polyurethane Hilti foam. The experiments revealed that the Hilti foam in a tensile scenario had a peak stress value that was larger than the closest competitor by a factor of 2.3 and a compressive yield point that was larger than the closest competitor by a factor of 1.4, indicating that the Hilti foam is the best choice for implementation in mechanically harsh environments. The performance metrics measured can serve as a basis for future mechanical tests that would help set relevant ASTM standards (E3191) for intumescent polyurethane foams in fixating applications. Tests like surface adhesion, impact, and flame tests would serve as a better indicator as to how this material would perform in environmentally harsh scenarios often found in decommissioned nuclear facilities. Further tests of the foams' intumescent properties would also be important should these foams be implemented in environmentally harsh scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Panel Session 145: Challenges and Opportunities in Establishing a System for Transportation of SNF/UNF

Issues associated with establishing a SNF Transportation System in the U.S. are complex. In part, this is because in the US when discussing SNF, there are two distinct categories. The two categories represent significant differences in ownership, transportation-regulator, and even the physical characteristics of the SNF. Specifically, the physical differences in the SNF include size of the rods, associated quantities to be shipped, cladding material, and radionuclide content/enrichment. The first is commercial SNF (i.e., that associated with nuclear power plants), owned by utilities, until a permanent repository is built with DOE obligated by law to then assume the responsibility for shipping and ownership of the SNF. Since no-permanent repository currently exists, most commercial SNF is stored near the reactor in which it was installed, hence requiring no shipments. Although current shipments of commercial SNF are very limited, the few that do occur are generally performed to support a utilities SNF consolidation effort utility and for post irradiation examination/testing. However, with approximately 83,000 metric tons currently being stored at more than 70 sites around the U.S., when a permanent repository becomes available, the required number of shipments will be extensive. The second category is DOE and research reactor fuel. These fuel rods are generally much smaller in length than commercial fuel rods, with the quantity of rods transported in a shipment significantly less than the number of rods in a typical commercial fuel assembly. Currently, most of the SNF shipments within the U.S come from this category. DOE is currently managing about 2500 metric tons of heavy metal of SNF. This panel focused on updates and status of transportation of SNF/UNF. Panelists provided updates and information on continuing impacts, risk assessment, equipment (rail cars, handling, securement), possible rail routing, and proposed inspection equipment/routing prior to shipments. Panelists with presentations: Preparing for Nuclear Waste Transportation (Daniel Ogg); Challenges and Opportunities in Establishing a System for Transportation of SNF/UNF (Kathy Langan); UNF Transportation: Challenges and Opportunities (Michael Valenzano)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Plastic Parallel Pathways Platform- 4P Model

The U.S. generates 42 million metric tons of plastic waste each year - the most of any country - of which less than 9% is recycled. This represents an estimated loss of $2.3 billion and 3.4 EJ embodied energy per year. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. Various recycling and waste management options are available for plastics, such as conversion into energy (incineration, pyrolysis, gasification), conversion to lower- or higher-grade plastics (mechanical recycling, chemical or enzymatic depolymerization processes, dissolution) or up-cycling to feedstock chemicals (photo/electro/catalytic methods, pyrolysis, gasification). While many previous publications have investigated a subset of these options, there is currently no modelling platform that can quantitatively assess and compare the economic and environmental impacts of all these different plastic management pathways and their respective products simultaneously. To directly compare the benefits and disadvantages of plastic-to-x technologies, we propose a novel analysis framework: the Plastic Parallel Pathways Platform (4P). 4P will model a systems analysis framework for capturing plastic material flows processed via different waste management scenarios. The total economic cost and greenhouse gas emissions (as well as additional economic, energetic, and environmental metrics indicators) of a given scenario will be calculated enabling comparison to other scenarios, thereby informing decisions on plastic recycling pathways.

circular economy↗

DOE OSTI2024

Model Gap Quantification and Evaluation

In the real world, model gaps always exist because models cannot perfectly match the objective physical plants. Model gaps reflect the integrity of models, which is vital for model validation and calibration. This letter proposes a novel approach for model gap quantification and evaluation. First, a comprehensive metric is developed to quantify dynamic model gaps. Next, the model gaps are evaluated from a panoramic view of the probability distribution of the comprehensive metric for multiple scenarios. Finally, we demonstrate the proposed quantification and evaluation approach through synchronous machine models.

Wang, Shaobu↗

DOE OSTI2024

Sustainability Criteria for Hydrogen Deployments

This presentation is an update of progress to date on the work done on developing sustainability metrics for hydrogen projects. Here we outline the motivation and objective for the work as well as the process we use to derive a sustainability framework and metrics to be considered when evaluating the sustainability of a hydrogen infrastructure project.

DEIA↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗

DOE OSTI2024

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by Circle Economy Foundation found that the circularity score for the global economy is declining. This indicates that material extraction from virgin sources is climbing over earlier years. As per data released by US Geological Survey, material use in the US economy has increased exponentially with the expanding US economy over last century. EPA tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste is destined for landfills. EPA estimates that US industry is responsible for 2.7 billion ton of solid non-hazardous waste annually in our mostly linear economy model. The circular economy framework aims at decoupling economic value generation from virgin materials extraction from nature. The linear model of material extraction and disposal at end of life is highly unsustainable. Manufacturing companies have realized this and in their commitments to sustainability are adopting ambitious waste reduction targets. Through Better Plants program US Department of Energy has established Waste Reduction Network where it is offering technical assistance to partners to achieve these ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is the unstandardized metrics to quantify waste performance that may not be well suited to demonstrate progress. In this paper, we research methods traditionally used to measure waste performance and highlight advantages, gaps and limitations of each method. Suitability of the methods applicable to different manufacturing circumstances are also examined. Finally, we present a case study of a large manufacturer that faced inconsistencies in their tracked measurement metric. A solution was proposed to alter the methodology to enable more accurate waste performance tracking against a baseline.

Chaudhari, Subodh↗

DOE OSTI2024

LTE Electrolyzer Data Collection

The goal for NREL is to collect, develop and publish performance metrics relative to low temperature electrolyzer installations. This will be done through the development of: Secure storage solution to house the collection of data from multiple projects Standardization of data to be collected and analyzed. This will be done using data templates developed with the help of partners involved with electrolyzer installations. Analysis that produces metrics of interest for all stakeholders Aggregation of results from multiple projects to view industry progress as a whole Publication of aggregated results in the form of composite data products (CDPs) Collaboration with Idaho National Lab and their work with high temperature electrolyzer installations will enable efficient use of storage and analysis tools.

data↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments [https://arxiv.org/abs/2403.11872]. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗

DOE OSTI2025

Better Climate Challenge Working Groups Non-Energy Benefits of Energy Projects-Improving Financial Payback

Energy efficiency is a key strategy recently identified by the United States Department of Energy as a pillar of industrial decarbonization. For manufacturing companies, improving energy efficiency will reduce money spent on energy utilities such as gas, electricity, and oil. Energy improvement projects also provide valuable benefits outside of simple operating cost reductions, such as reducing the carbon footprint, improving safety metrics and even enhancing quality and productivity. Unfortunately, energy efficiency projects have typically faced an adoption gap, even when they meet criteria such as payback period for capital projects. The inclusion and quantification of non-energy benefits (NEBs), also known as co-benefits, in the decision-making process for energy efficiency projects can improve the overall financial payback periods for those projects as well as potentially improve the company's key performance metrics aligned with business strategies. There are no readily available tools that facilitate this, however, and the most used tools for energy audits address NEBs in a perfunctory way if at all. We integrated research for finding and quantifying non-energy benefits of energy efficiency projects into a commonly recognized continuous improvement practice, the Define, Measure, Analyze, Improve and Control (DMAIC) Process. This process, along with software and supplemental materials, guides energy assessments to find and to quantify NEBs associated with energy conservation opportunities. Our aim is to deliver an easy to use and effective process and software tool and to maximize return on investment for energy efficiency projects as well as contribute to companies' strategic performance goals.

DMAIC↗

DOE OSTI2025

EVs@Scale Next-Gen Profiles - Fleet Utilization 2024

As part of the U.S. Department of Energy’s EVs@Scale initiative, the Next-Gen Profiles (NGP) project provides a comprehensive, data-driven analysis of electric vehicle (EV) and electric vehicle supply equipment (EVSE) operations across real-world fleet deployments. This paper presents findings from the NGP’s Fleet Utilization study, which investigates operational behavior and asset usage across seventeen EV fleets and two EVSE fleets, encompassing a wide range of vehicle types and use cases. Data collected from diverse sources—varying in format and temporal resolution—are first reformatted into a unified structure. From this harmonized dataset, a suite of rigorously defined performance metrics is calculated at an hourly cadence, enabling consistent cross-comparison of charging, routing, and other key operational behaviors. Amid rapidly increasing EV adoption and growing demands for energy-efficient fleet operations, the analysis reveals clear utilization trends—including diurnal and weekly activity cycles, differences in short versus long charging session dependencies, and route-specific energy usage patterns. These findings highlight the need for tailored infrastructure strategies and the deployment of advanced energy management systems, such as Distributed Energy Resource Management Systems (DERMS) and Site Energy Management Systems (SEMS), which can optimize charging schedules and mitigate peak loads. By leveraging anonymized, harmonized datasets and standardized metrics, this study offers critical insights into fleet behavior and performance, providing a foundation to improve operational efficiency, reduce costs, and enable the scalable deployment of electrified transportation.

Wells, Landon↗