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This is a slide deck for the Procurement Analysis Tool and the deck published in August 2025. This webinar is a part of the PAT roadshow and we are presenting it in different forums and to different audience. https://research-hub.nrel.gov/en/publications/procurement-analysis-tool-pat-informational-webinar
This a poster on PAT for the CESA National Energy Summit for States: Navigating Energy Trends and Federal Programs Conference.
This is a slide deck for the public PAT Informational Webinar scheduled for July 22, 2025. This the first webinar for PAT after the new tool was launched on nrel.gov on June 16, 2025.
Webinar with NARC/Solsmart to showcase NREL tools related to energy with local governments interested in Solsmart designation. NREL staff will present on SAM, PV Watts, REopt, Procurement Analysis Tool, Slope, SolarAPP+, and SolarTRACE.
Virtual Trainings are the online version of the multi-day workshops known as In-Plants (INPLTs) offered by the DOE Better Plants program. ORNL has a 6-session weekly training on Renewable Energy Contracting Options and RECs starting August 5th 2025 (10 am ET) focused on options and resources for the manufacturing sector. This session is earmarked for Tuesday, August 26th, 10 AM ET.
High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.
To accelerate nuclear fuel qualification and deployment efforts, Oak Ridge National Laboratory has led the world in developing vehicles for accelerating burnup accumulation in nuclear fuel by testing miniature samples in the High Flux Isotope Reactor. However, a challenge remains to establish parity between post irradiation characterization methods for miniature fuel specimens in comparison to conventional fuel elements. This work aims to identify artifacts associated with the thermal analysis of miniature disk specimens in comparison to samples of standardized dimensions through a combination of experimental and computational tools. Using a newly procured Netzsch 427 laser flash diffusivity system, preliminary data on mild steel specimens were collected, and finite element techniques were used to model heat transfer using 1D conditions to assess issues associated with pulse width and data collection frequency effects. These preliminary investigations found that significant variability in measured thermal diffusivity exist within datasets. These variations are not necessarily attributed to limitations with respect to data collection rate; nor are they associated with limitations on pulse width. Instead, these artifacts may be a result of limitations in detector intensity. Future work aims to design a focusing lens to better measure the smaller surface area of miniature specimens analyzed using the laser flash analysis system.
Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.
Renewable energy auctions are now a common competitive approach to procure low-cost renewable power around the world. Ensuring a successful auction process increasingly depends on the capabilities of auction designers and participants to identify actionable and defensible insights from large data sets (on renewable energy resources and complementary data) to both attract potential investors and address stakeholder concerns. The Renewable Energy (RE) Data Explorer is a user-friendly geospatial analysis tool for analyzing renewable energy potential and informing decisions. Developed by the National Renewable Energy Laboratory (NREL) and supported by the U.S. Agency for International Development (USAID), RE Data Explorer performs visualization and analysis of renewable energy potential that can be customized for different scenarios. RE Data Explorer can support prospecting, integrated planning, policymaking, and other decision-making activities to accelerate renewable energy deployment. The broader RE Explorer website provides guidance and information to link the RE Data Explorer geospatial analysis tool to key decision areas. This document provides information on how the RE Data Explorer can be used to support renewable energy auction processes.
The main focus of this work was to evaluate additive manufacturing of metal molds, with significantly reduced cost and/or procurement lead time, for production of large composite components for mainstream (> 100k units/yr) automobiles. A two-component large tool with conformal heating channels was designed, optimized, printed, machined, and tested in production. The resulting analysis indicates that large scale AM is suitable for manufacturing complex geometry large scale metal molds.
There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.
As the uncertainty of a sustained water supply, regulatory constraints, competition among end users, and public scrutiny increases, a growing number of manufacturing sectors are adapting to current and emerging water-related risks by optimizing productivity and reducing waste. As a step toward increasing water use efficiency and making informed business decisions, corporations must make an effort to understand and track their water demands, losses, and costs associated with each subsystem within their facilities. Manufacturers are often unaware of the “true cost of water” (i.e., the total costs associated with procurement, treatment, and consumption of water, and wastewater disposal), which reduces the visibility of the actual impact of water-saving measures. To help manufacturers account for water procurement and use in manufacturing operations, quantify the true cost of water, and identify potential areas for water and associated energy cost savings, an open access tool—Plant Water Profiler (PWP)—was developed. The tool is based on water mass balance analysis and has been adopted as the core analysis tool for plant water use assessments, Water In-Plant Training (Water INPLT), as part of the US Department of Energy’s Better Plants program. During three pilot INPLTs conducted in 2019 at three manufacturing facilities, the PWP tool allowed users to understand water flows within the facilities and provided additional capabilities to analyze their water use. This paper describes the methodology behind the PWP tool and its implementation through pilot Water INPLT trainings delivered in three manufacturing facilities in the United States. The three case studies demonstrate opportunities to improve water efficiency and reduce associated costs and the challenges encountered in three different manufacturing sectors.
This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.
Rigorous stakeholder-vetted techno-economic analysis was performed to assess the cost of hydrogen (H 2 ) produced using state-of-the-art Anion Exchange Membrane (AEM) electrolysis. Projected high-volume, untaxed and unsubsidized levelized cost of hydrogen (LCOH)1 range from 2020 $\$$1.78 to $\$$3.68/kg H 2 depending on technology year, process design, and electrolyzer project scale, assuming an electricity price of $\$$0.03/kWh and a capacity factor of 97%. The total installed capital cost for an AEM electrolysis plant was estimated from bottom-up stack and process plant cost models. The stack cost model accounts for manufacturing equipment, equipment maintenance, material, tooling, cycle time, yield, labor, utilities and general overhead. The process plant cost model accounts for purchased equipment, installation costs, site preparation, and general overhead costs. For this study, the AEM electrolysis plant is assumed to be a stick-built, greenfield project developed by an engineering, procurement, and construction (EPC) firm with electrolysis stacks purchased directly from an electrolysis stack manufacturer. The price of the electrolysis stacks is based on a bottom-up cost assessment with business markup for the electrolysis company fabricator. Methods from the Hydrogen Analysis (H2A) production model, a peer-reviewed national laboratory-developed discounted cash flow (DCF) model, were used to calculate the production LCOH in 2020 $\$$/kg H 2 . The baseline electricity price case ($\$$0.03/kWh) corresponds to average wholesale electricity prices currently possible in U.S. markets with plentiful wind. Similar low-cost electricity pricing is possible from solar Power Purchase Agreements (PPA) although these prices are typically limited by renewable energy capacity factors.
An automated solvent extraction system for the eventual handling of radioactive materials has been designed and procured for rapid, efficient and safe liquid-liquid extraction processes. This system will be configured to carry out liquid-liquid separations from beginning to end, automating tedious and time-consuming tasks such as organic/aqueous phase prep, pH checks, phase separation, and metal ion analysis. The system features advanced liquid handling capabilities, a robotic arm for precise sample transfer, and sophisticated analytical tools including UV-VIS spectrophotometry for real-time monitoring. Additionally, it incorporates automated capping of vials, vortex mixing, and centrifugation to ensure thorough mixing and phase separation. This report provides an overview of the system and general capabilities as well as initial efforts to develop an automated workflow for liquid-liquid separations.
There are many national policies aimed at driving an increase in energy efficiency (EE) implementation. But how effective are they? Over the past 20 years, the U.S. government has established five legal authorities mandating federal agencies to prioritize energy-efficient products when purchasing. As the largest single buyer of energy-consuming products, energy-efficient purchasing across the federal sector would result in huge energy savings and emissions reductions. Despite this, our research has found that only ~55% of federal purchases currently meet existing requirements. If national mandates are not enough to ensure that federal agencies are buying efficient products, what is? To answer this question, we surveyed 161 procurement and sustainability staff from 26 different federal agencies. Findings revealed that several institutional factors (i.e., the roles, rules, and tools within an organization) play a key role in determining how likely federal buyers are to prioritize EE. Ensuring that federal agencies comply with existing EE requirements is foundational to achieving a clean energy future. This will require more than national mandates, but also the design of policies that successfully identify and address the institutional factors that must be changed in order to increase EE implementation. This paper presents an overview of the data collection and analysis methods for our study, as well as key survey findings and the insights they offer for overcoming institutional barriers to compliance with existing EE requirements. We conclude by discussing how these strategies can be broadly applied to improve the design of EE policies in the U.S. and beyond.
The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.
The project demonstrated the ability to improve boiler performance and reliability through the integrated use of condition-based monitoring (CBM) and predictions of the impacts of coal quality on boiler operations at a full-scale coal-fired power plant. The advanced tool developed actively monitors and manages coal quality and overall boiler conditions that maximizes availability and maintains generating capacity while reducing cost. The tool is used to forecast and alert plant operators and engineers about poor boiler conditions which may occur as a result of incoming coal and/or current power plant operating conditions. The Combustion System Performance Indices (CSPI) and CoalTracker (CT) programs predicts fireside performance of the plant including slagging, fouling, erosion, slag flow, strength development (sintering/densification), and slag layer thickness based on the composition of delivered coal. CT program is tailored for each plant and is used to track the coal from the point of delivery to the burner. The most successful application of the CSPI-CT has been at plants where the coal composition is determined through the use of a full stream elemental analyzers based on prompt gamma neutron analysis (PGNAA). The CSPI-CT program information is used by coal procurement, coal mining and plant operations personnel to select and blend coal properties for the best plant performance.