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DOE OSTI2026

Strategic Energy Plan, Town of Cranberry Isles

This report details the strategic energy plan for the Town of Cranberry Isles, Maine. This project was spearheaded by the Cranberry Isles Community Solar Association (CICSA), and supported by Island Institute and Sandia National Laboratories, in partnership with the National Laboratory of the Rockies and Versant Power. The plan details the energy vision, goals, and focus areas for the community, provides technical analysis of the current energy system, and outlines potential projects to support community energy. Throughout the energy planning process, the project team engaged with members of the community to gather input and ideas to shape the plan. The project team and Versant also completed an extensive data collection to better inform the project. Sandia National Laboratories completed baseline technical assessments and feasibility studies to better understand potential feasible options for future projects. These potential projects, high-level costs, and next steps are a key output of this report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Herbaceous Feedstock 2022 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for nth-plant and 1st-plant herbaceous biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for herbaceous biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Although conventional feedstock supply systems form the backbone of the emerging biofuels industry, they have limitations that restrict widespread implementation on a national scale. To meet the demands of the future industry, the feedstock supply system must shift from the conventional system to what has been termed “advanced” supply systems. In advanced designs, a distributed network of aggregation and processing centers, termed “depots,” are employed near the points of biomass production (i.e., the field or forest) to reduce feedstock variability and produce feedstocks of a uniform format, moving toward biomass commoditization. The 2022 Herbaceous SOT is part of a vision of achieving an implemented advanced feedstock supply system, which produces a stable, tradable commodity at the decentralized distributed depot. It utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (leaves, husks, stems and cobs) to reduce impurities and produce fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in corn stover and produce enriched tissue fractions that can be blended to a conversion specification or converted individually in optimized biochemical conversion campaigns. Additionally, a majority of the leaves (which do not meet the quality specification) are separated out early and can be supplied to alternate markets. The 2022 Herbaceous SOT incorporates an advanced biomass fractionation and processing system to produce pellets enriched tissues from three-pass corn stover. The resulting enriched pellets are delivered to the biorefinery individually where they can be blended to a specification or converted in campaigns where the conditions are optimized for each tissue. Unused fractions can be sent to a a midstream market or to a different conversion process that is better suited to their properties to offset the cost of the delivered feedstock. The main benefits from the proposed system can be summarized as: (1) $6.86/dry ton (2016$) lower cost for the air classification due to elimination of the requirement to discard the high ash lights fraction; (2) $1.56/dry ton lower delivered cost by selling the unsuitable leaf fraction into the feed market as a midstream co-product (assuming a selling price that is 11% higher than their cost of production); (3) 0.98% increase in carbohydrate content (from 60.16% to 61.14%); and (4) 0.97% decrease in ash content (from 6.00% to 5.03%) compared to the 2021 Herbaceous SOT. Overall, the 2022 nth-plant Herbaceous SOT predicts a modeled delivered feedstock cost of $78.64/dry ton (2016$) if it is assumed that the enriched leaf fraction is sold at its production cost; this is a slight increase of $0.43/dry ton increase from the 2021 Herbaceous SOT nth-Supply case cost. The increased cost derived from a $0.38/dry ton increase in transportation and handling cost to procure more biomass (to replace the enriched leaf fraction that was not delivered to the biorefinery. The total preprocessing cost was $0.27/dry ton higher than the 2021 result because of updates to energy consumption, purchasing price and dry matter loss data for the rotary shear ($3.00/dry ton increase) and the pelleting mill ($4.52/dry ton increase). The data utilized were generated in pilot-scale tests in the Biomass Feedstock National User Facility (BFNUF) at INL and at Forest Concepts, including tests for rotary shear and pelleting of the air classified fractions. A greenhouse gas emissions analysis was performed by Argonne National Laboratory using the most up to date version of the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation model (GREET®). The analysis showed an increase of 17.34 kg CO2e/dry ton from the 2021 SOT (67.71 kg CO2e/ton in the 2021 Herbaceous SOT to 85.05 kg CO2e/ton in the 2022 Herbaceous SOT). The net increase is primarily attributed to increased energy consumption in pelleting mill.

09 BIOMASS FUELS↗
DOE OSTI2025

Hydrogen Bond Benchmark: Focal‐Point Analysis and Assessment of DFT Functionals

We performed a hierarchical, convergent ab initio benchmark study and systematically analyzed the performance of density functional approximations for describing hydrogen bonds in small neutral, cationic, and anionic complexes, as well as in larger systems involving amide, urea, deltamide, and squaramide moieties. Focal point analyses (FPA), extrapolating to the ab initio limit, were carried out using correlated wave function methods up to CCSDT(Q) for the small complexes and CCSD(T) for the larger systems, together with correlation-consistent Gaussian basis sets up to the complete basis set limit. Optimized geometries and vibrational frequencies were obtained at the CCSD(T) level. The resulting FPA hydrogen-bond energies converge within a few tenths of a kcal mol −1 . These reference data were used to evaluate 60 density functionals (including 12 dispersion-corrected), spanning the local-density approximation (LDA), generalized gradient approximations (GGAs), meta-GGAs, hybrids, meta-hybrids, double-hybrids, and range-separated hybrids. Overall, the meta-hybrid M06-2X provides the best performance for both hydrogen bond energies and geometries, while the dispersion-corrected GGAs BLYP-D3(BJ) and BLYP-D4 also yield accurate hydrogen-bond data and can serve as cost-effective options for studying large and complex systems.

coupled cluster theory↗
DOE OSTI2025

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗
DOE OSTI2025

Tunable Semiconducting Behavior and Linear-Nonlinear Optical Properties of Ag–Sn Dual-Doped Nanocrystalline CdO Thin Films for Optoelectronics

Semiconductor-based thin films have a great impact on determining the anticipated optoelectronic device construction and the advancement of cutting-edge applications. Herein, Ag and Sn dual-doped nanocrystalline transparent conducting CdO thin films were prepared on glass substrates using a cost-effective spray coating method, and their structural, morphological, optical, and semiconducting behaviors were investigated. The successful incorporation of Ag and Sn resulted in the polycrystalline nature of the deposited films without the additional peaks, as verified by X-ray diffraction (XRD) analysis. The XRD report also revealed the enhanced crystallinity (65%) at the higher doping level (3 wt % Ag and Sn-doped CdO film). All the deposited CdO films exhibited homogeneous, spherical, or round-shaped grains, with agglomeration revealed by scanning electron microscopy analysis. UV–visible spectroscopy was utilized to determine the linear and nonlinear optical properties of the deposited CdO thin films, and a reduction in the band gap from 3.891 to 3.772 eV was observed. A significant enhancement in the first- and third-order nonlinear susceptibility and nonlinear refractive index of the doped CdO films was also observed with increasing doping concentration. Hall effect data were collected at room temperature to investigate the electrical properties of all of the CdO films. The charge carrier concentration of CdO thin films was increased from 142.08 × 10 18 to 169.10 × 10 18 cm –3 , and the highest conductivity was found to be 192 s/cm on doping 3 wt % Ag–Sn. All the CdO thin films exhibited n-type conductivity, while an incredible n-type to p-type charge carrier transition was noticed at a higher doping level (3 wt % Ag–Sn). The findings of the current work are expected to advance the synthesis of semiconductor thin films through cost-effective spray coating methods for optoelectronic device applications.

deposition↗
DOE OSTI2024

Integrated multimodel analysis reveals achievable pathways toward reliable, 100% renewable electricity for Los Angeles

Climate change has prompted many communities to set targets for carbon-free power supplies, but they often lack data-driven strategies to achieve them. We present a comprehensive analysis of an entirely renewable electric power system that can maintain operating reliability and resource adequacy using detailed models of the city of Los Angeles power grid. In consultation with the operating utility, the Los Angeles Department of Water and Power (LADWP), and the local community, we develop four supply scenarios across three demand projections to analyze which types of infrastructure and operational changes would achieve reliable electricity at least cost. We find that a reliable, 100%-renewable power system yielding more than $1 billion annually in health and climate co-benefits is achievable. Solar can supply most future energy needs, while combustion turbines that use renewable, storable carbon-neutral fuels are key to maintaining reliability. This study provides a replicable methodology that other jurisdictions globally can follow.

14 SOLAR ENERGY↗
DOE OSTI2024

IDAES-PSE Software Tools for Optimizing Energy Systems and Market Interactions

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-eco-nomic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully generates multi-period price-taker models, facilitates model discrimination, and aids in analyzing various technologies for deployment in unique energy markets.

Laky, Daniel↗
DOE OSTI2025

Analysis of Hydrogen Supply Chain Readiness in Selected Indo-Pacific Countries

This report explores the low-carbon hydrogen production and procurement targets of Indo-Pacific Economic Framework for Prosperity (IPEF) countries, highlighting key cost and infrastructure considerations. By 2030, production costs are targeted between US$\$$1.4-4.8/kg-H2, with procurement prices at US$\$$2.3/kg-H2, narrowing further by 2050. Midstream costs, including storage and transportation, are critical to meeting these targets and enabling hydrogen trade. The report discusses challenges such as infrastructure readiness, regulatory frameworks, and the need for improved data reporting. It emphasizes the importance of tax incentives, research investments, and port readiness to support the growth of low-carbon hydrogen markets in the region.

08 HYDROGEN↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Reliability Assessment of Cooling Fans for PV Inverters: Testing, Modeling, and Case Studies

The reliability of photovoltaic (PV) inverters is critical for long-term solar system performance, with cooling fan failures frequently leading to costly downtime. While much research exists on general cooling fan reliability, little attention has been given to fans operating within PV inverters and their unique environmental challenges. Here, this article proposes a comprehensive methodology to address this gap. First, a failure mode and effects analysis is performed on fans to identify the key failure mechanisms in PV applications, their corresponding stressors, and the models necessary for lifetime prediction. Second, an accelerated life test is designed and conducted to collect valuable experimental data for PV inverter fans in a reasonable amount of time. Third, a mathematical conversion of dynamic mission profiles into effective constant stress levels is derived. Fourth, case studies are given, showcasing lifetime estimates that account for geographic variations in mission profile data. The results demonstrate that this integrated approach leads to an accurate reliability assessment for PV inverter cooling fans.

accelerated life testing (ALT)↗
DOE OSTI2024

Understanding GPU Memory Corruption at Extreme Scale: The Summit Case Study

GPU memory corruption and in particular double-bit errors (DBEs) remain one of the least understood aspects of HPC system reliability. Albeit rare, their occurrences always lead to job termination and can potentially cost thousands of node-hours, either from wasted computations or as the overhead from regular checkpointing needed to minimize the losses. As supercomputers and their components simultaneously grow in scale, density, failure rates, and environmental footprint, the efficiency of HPC operations becomes both an imperative and a challenge. We examine DBEs using system telemetry data and logs collected from the Summit supercomputer, equipped with 27,648 Tesla V100 GPUs with 2nd-generation high-bandwidth memory (HBM2). Using exploratory data analysis and statistical learning, we extract several insights about memory reliability in such GPUs. We find that GPUs with prior DBE occurrences are prone to experience them again due to otherwise harmless factors, correlate this phenomenon with GPU placement, and suggest manufacturing variability as a factor. On the general population of GPUs, we link DBEs to short- and long-term high power consumption modes while finding no significant correlation with higher temperatures. We also show that the workload type can be a factor in memory’s propensity to corruption.

Oles, Vlad↗
DOE OSTI2025

Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

09 - BIOMASS FUELS↗
DOE OSTI2026

Used Nuclear Fuel Management Using the Next Generation System Analysis Model

The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.

Craig, Brian [Argonne National Laboratory (ANL)]↗
DOE OSTI2025

Proactive Regulatory Approaches to Electrification and Load Growth: Workshop Report

On July 10 and 11, 2024, Pacific Northwest National Laboratory and RMI led a workshop in Aurora, Colorado, to explore novel and proactive approaches to electrification and load growth while minimizing risks and costs to customers. Over the next decade, a unique opportunity exists to invest strategically in the electricity system to enable electrification across the transportation, industrial, and building sectors and respond to data and technology-based load growth. However, current utility and regulatory planning practices are insufficient to identify and enable the right investments, and work must be done to reduce the risk and decisional uncertainty faced by utility regulatory commissions and utilities. Ensuring timely electrification investments may require new approaches to address risk, uncertainty, prudence, and cost recovery. Understanding the decision-making process and information needs of utilities and regulators is critical. New policies (or application of policies), financial tools, systems analysis, regulatory mechanisms, and enhanced process transparency may be required. The workshop's goal was to identify proactive regulatory approaches for electrification and load growth that minimize costs and risks to customers. Our intention was that the conversations and the resulting solutions and takeaways would be specific and tactical rather than general and theoretical and that together we would create actionable next steps for key actors in the system, including utilities, regulators, thought leaders, researchers, and the U.S. Department of Energy (DOE). This report is intended to provide workshop attendees with a record and summary of the discussion and proposals raised at the workshop and to provide interested entities who did not attend, such as other regulators, policymakers, utilities, and U.S. DOE offices, with an understanding of what was discussed and with ideas to explore in their organizations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and riverine sediment transport. This study presents the first integrated modeling framework that couples the Energy Exascale Earth System Model (E3SM) land component, which simulates runoff and hillslope erosion, with TELEMAC-GAIA, a two-dimensional hydrodynamic and sediment transport model. This framework enables the fully distributed, process-based simulation of high-resolution (as fine as 30 m) sediment dynamics from hillslopes to floodplains. Applied to a highly urbanized watershed in Houston during Hurricane Harvey, this framework reproduced observed water levels at 16 USGS gauges (median R 2 = 0.83 and KGE = 0.78), key sediment dynamics such as sediment transport and deposition processes, and reproduced spatial deposition patterns consistent with LiDAR-derived data. Based on the simulation, we estimate 8.0 million m 3 of event-scale sediment deposition, including 5.7 million m 3 trapped in the flood-control reservoirs and 2.3 million m 3 deposited along major channels and floodplains. Using a representative unit removal cost, this corresponds to an estimated dredging cost of $581 million for total deposition. These results provide a first-order, physically based quantification of Harvey-scale sediment impacts. This study provides a valuable tool for the holistic analysis of sediment dynamics triggered by extreme urban flooding, supporting flood-resilience planning. More broadly, it highlights the importance of integrating physically based hydrological processes for urban flooding and sediment research.

Hurricane Harvey↗
DOE OSTI2025

Interregional Electricity Transmission in the United States: Realized Savings and Opportunities for Increased Value, 2014 to 2023

Interregional electricity trade in the United States reduces total energy costs, but falls short of maximizing the economic potential of existing transmission infrastructure. This analysis of thirty-two U.S. electricity system interfaces crossing grid or market seams finds an average achieved cost savings of at least $1,226 million per year. This positive impact is offset by an average annual cost of uneconomic interchange of $551 million. Additional value of at least $238 million per year could be enabled by increasing utilization. Notably, uneconomic flows and low utilization rates can persist even when the price spread between regions is large. These conclusions are derived from historical (2014–2023) data on the magnitude and direction of interregional price spreads, the magnitude and direction of net energy transfers, and how these variables coincide, but do not account for all system constraints. These findings motivate the development and deployment of solutions that improve interregional transmission operations, in parallel with robust transmission infrastructure planning. JEL Classification: Q40 Energy: General

Q40 Energy: General↗
DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or timeintensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)-a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building's monthly energy usage in response to weather conditions. The tool benchmarks a building's electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE's Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization's challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

BETTER↗