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DOE OSTI2026

Energy to Communities Program Short Introduction: Evaluation of Enhanced Geothermal System for Cornell District Heating [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Cornell University (Itaca, NY) community efforts in developing Enhanced Geothermal System (EGS) reservoir performance, by simulating fracture flow to estimate pressure drop, flow distribution, and heat extraction efficiency in fractured rock. Thermal drawdown modeling was used to assess long-term resource sustainability, while system optimization studies refined key parameters such as well configuration and flow rate to improve overall reservoir efficiency.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Understanding Fleet Charging Needs [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, National Laboratory of the Rockies, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Prince William County, VA with information about fleet charging and telematics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Techno-Economic Analysis of Distributed Energy Generation in Crow Creek, Alaska

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

SMARTER Rules-Based Distributed Deconfliction of ADMS Applications

A conceptual numerical methodology derived from Grid Architecture principles is introduced for deconflicting setpoints issued by multiple advanced distribution management system applications. The methodology applies technical, economic, environmental, and social rules to eliminate non-viable combinations. The concept of temporal equipment controls budgets is introduced to preserve the health of physical assets and avoid equipment damage through repeated controls cycling. The rules are combined with a multi-criteria decision-making framework to select a near-optimal set of deconflicted setpoints using a set of qualitative and quantitative decision criteria selected by the distribution system operator. Numerical results are demonstrated on the IEEE 123-bus test feeder for three competing applications. Three alternative distributed schemes are used to decompose the problem: by topological area, by phase, and fully decentralized. The fully decentralized implementation is shown to yield near-optimal deconfliction results with significantly reduced computational time.

Anderson, Alexander A.↗

DOE OSTI2025

Preliminary Assessment of Renewable Energy Options at the Sravasti Abbey [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NREL, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term clean energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional clean energy expertise to inform key upcoming decisions. This Expert Match request supported the Sravasti Abbey in Washington a preliminary assessment of distributed energy technology financial feasibility for their site. The outcome consists of low estimated savings from these technologies due to their already low energy consumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optimization problems governed by systems of PDEs with uncertainties

This paper reviews current theoretical and numerical approaches to optimization problems governed by partial differential equations (PDEs) that depend on random variables or random fields. Such problems arise in many engineering, science, economics and societal decision-making tasks. This paper focuses on problems in which the governing PDEs are parametrized by the random variables/fields, and the decisions are made at the beginning and are not revised once uncertainty is revealed. Examples of such problems are presented to motivate the topic of this paper, and to illustrate the impact of different ways to model uncertainty in the formulations of the optimization problem and their impact on the solution. A linear–quadratic elliptic optimal control problem is used to provide a detailed discussion of the set-up for the risk-neutral optimization problem formulation, study the existence and characterization of its solution, and survey numerical methods for computing it. Different ways to model uncertainty in the PDE-constrained optimization problem are surveyed in an abstract setting, including risk measures, distributionally robust optimization formulations, probabilistic functions and chance constraints, and stochastic orders. Furthermore, approximation-based optimization approaches and stochastic methods for the solution of the large-scale PDE-constrained optimization problems under uncertainty are described. Some possible future research directions are outlined.

Heinkenschloss, Matthias [Rice Univ., Houston, TX ↗

DOE OSTI2025

Alabama Carbon Storage: Bringing Data to the People

The Gulf Coastal Plain of Alabama has proven potential for geologic carbon storage and current interest in the area for large carbon capture and storage (CCS) projects is high. Extensive CCS relevant data exist in the records of the Geological Survey of Alabama and State Oil and Gas Board of Alabama, however, most of this data is not publicly available or is scattered in separate databases, file cabinets, and tables in publications. The “Alabama Carbon Storage: Data Sharing and Engagement” (ACS-DSE) project seeks to accelerate the responsible development of large CCS projects in the Gulf Coastal Plain of Alabama and offshore in state waters through a publicly accessible database of geologic carbon storage models and data across the region. The ACS-DSE draws on the over 150 years of geologic research and over 20 years of experience in CCS research to place relevant geologic, geophysical, and infrastructure data on a single web platform. Datasets available will include formation depths and elevations, geologic structures, reservoir properties, digital well logs (LAS files), existing penetrations, and geologic models. In addition to downloadable datasets, links to CCS related regulatory agencies and other sources of information will be included (for example, Class VI UIC permitting regulations and pipeline regulations). By making these datasets and models available in commonly used formats on a public website, the project will increase transparency in decision making and decrease the data acquisition time for industry.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2026

Holistic Transportation and Energy Modeling

This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Integrating Cambium Marginal Costs into Electric Sector Decisions: Opportunities to Integrate Cambium Marginal Cost Data into Berkeley Lab Analysis and Technical Assistance

NREL’s Cambium tool generates forward-looking simulations of marginal wholesale electricity costs associated with NREL’s Standard Scenarios. The scenarios include growing shares of variable renewable energy (VRE, i.e. wind and solar), among other power sector assumptions, between 2018 and 2050. The tool’s primary output—hourly costs at more than 130 balancing areas—could serve as public and transparent data source that supports electric-sector decision-making processes across the U.S. Berkeley Lab conducts a large range of analyses that use historical and forward-looking wholesale electricity prices to inform electric-sector decisions. In this report, Berkeley Lab uses its expertise to evaluate the Cambium cost data. We compare Cambium data with historical wholesale prices for the year 2018 and other modeled prices for the year 2030. We then present eight case studies in which Berkeley Lab researchers use Cambium data to replicate previous analyses based on other price datasets. We describe where primary findings and underlying key price dynamics align or differ, and highlight possible novel insights from the Cambium data. Finally, we qualitatively evaluate the suitability of Cambium costs in ten additional Berkeley Lab studies, though a direct comparison with alternative price data was not feasible at this time. The goal is to inform how electric-sector decision-makers and DOE program offices may be able to use this cohesive dataset, and to highlight what improvements to Cambium may make it even more useful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Expert and operator perspectives on barriers to energy efficiency in data centers

Abstract It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Coffee supply chain planning under climate change

The growing demand but uncertain supply makes the sustainability of the coffee industry a shared concern for all participants along the coffee supply chain. This study proposed a decision-making model that comprises the cultivation management, including shade management and annual agriculture management, and the supply chain logistics. A two-stage stochastic program is presented and used within a rolling horizon scheme that periodically updates input data information to deal with uncertainty associated with future climate scenarios. The program minimizes the total expected cost of the entire supply chain of arabica coffee. The study applied the model to the real case study of arabica coffee bean supply to the U.S. market, trying to answer whether arabica coffee supply can meet the U.S. demand from 2022 to 2050 and how to best mitigate any shortage through corporate-farmer partnerships. The results show that the coffee supply will have a 3% shortage in the future; medium-level shade management and more irrigation and fertilization are possible mitigation strategies. These results demonstrate the need for all participants to adopt suitable technologies for the sustainability of global coffee supply chains together.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Effect of barren plateaus on gradient-free optimization

Barren plateau landscapes correspond to gradients that vanish exponentially in the number of qubits. Such landscapes have been demonstrated for variational quantum algorithms and quantum neural networks with either deep circuits or global cost functions. For obvious reasons, it is expected that gradient-based optimizers will be significantly affected by barren plateaus. However, whether or not gradient-free optimizers are impacted is a topic of debate, with some arguing that gradient-free approaches are unaffected by barren plateaus. Here we show that, indeed, gradient-free optimizers do not solve the barren plateau problem. Our main result proves that cost function differences, which are the basis for making decisions in a gradient-free optimization, are exponentially suppressed in a barren plateau. Hence, without exponential precision, gradient-free optimizers will not make progress in the optimization. We numerically confirm this by training in a barren plateau with several gradient-free optimizers (Nelder-Mead, Powell, and COBYLA algorithms), and show that the numbers of shots required in the optimization grows exponentially with the number of qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Flow Forecasting on New England’s Great River Hydro (CRADA 549) (Final Report)

Great River Hydro (GRH) is New England's largest conventional hydropower generator and operates sustainable hydropower systems in Vermont, New Hampshire, and Massachusetts. Several of GRH’s hydropower projects on the Connecticut River are largely constrained to operate as run-of-river; however, though a new FERC license they have opportunity in the form of flex hours where they are allowed to deviate operations to capture favorable market conditions or emergency operations. To maximize value from these new operations, GRH is considering new sources of forecasted inflow. Pacific Northwest National Laboratory (PNNL) is evaluating the quality of two commercial inflow forecasts for GRH under DOE Water Power Technology Office’s Notice of Opportunity for Technical Assistance: Improving Hydropower’s Value through Informed Decision Making. This project will provide an unbiased evaluation of the accuracy of inflow forecasts available through commercial vendors. GRH will use this information to support their decision on which inflow forecast to use under their new FERC license and to assess the overall value of the available inflow forecasting tools and practices. In this talk we will discuss details of the near real-time forecast evaluation process, the metrics involved in the evaluation, and some preliminary results. In addition, we’ll touch on the second phase of the project which involves evaluating scheduling strategies that identify windows of opportunity to change operations in ways that increase profit through participation in day ahead and real time markets.

13 HYDRO ENERGY↗

DOE OSTI2020

Sources of Conservatism in Making Corrective Action Decisions at the NNSS - 20477

During the course of closing nuclear testing release sites at the Nevada National Security Site (NNSS) under the Federal Facility Agreement and Consent Order (FFACO) using the Risk-Based Corrective Action (RBCA) evaluation process, conservative assumptions and estimates were used in many phases of the work. These assumptions and estimates were agreed to during Data Quality Objective (DQO) meetings and published in the FFACO documents. However, the potential cumulative effect of these multiple sources of conservatism was not evaluated. The RBCA process ensures that corrective action decisions minimize the risk of making Type I errors (not applying corrective action to areas that exceed action levels) at the expense of making Type II errors (applying corrective action to areas that do not exceed action levels). Rather than fully characterizing the spatial distribution of contaminants throughout each site, the RBCA process uses a bounding methodology that conservatively estimates spatial boundaries beyond which contamination is not present that exceed corrective action levels. This process accepts that these boundaries may encompass areas that do not exceed corrective action levels and, therefore, may be somewhat larger than for fully characterized sites. This is justified at sites where the reduction in investigation costs are more than any potential increase in corrective action costs. To minimize the risk of making Type I errors, conservatism was used throughout the process. This study documents the many sources of conservatism used in the RBCA process. Conservatism was used in the RBCA process in determining potential receptors, determining action levels, collecting samples, converting sample results into receptor dose, and accounting for uncertainty. This provides decision-makers with a high level of confidence that unacceptable risk to receptors will not result from exposure to residual contamination outside the corrective action boundaries. However, it does not provide an estimate of the actual extent of areas exceeding action levels. This study provides an evaluation of the effects of compounding conservatism on RBCA process decisions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Determining Levels of Detail for Simulators of Parallel and Distributed Computing Systems via Automated Calibration

There are two sources of inaccuracy when simulating parallel and distributed computing systems: (i) a simulator implemented at an insufficient level of detail; and (ii) incorrectly calibrated simulation parameter values. Increasing the simulator’s level of detail can improve accuracy, but at the cost of higher space, time, and/or software complexity. Furthermore, evaluating the intrinsic accuracy of a simulator requires that its parameters be well-calibrated. Making decisions regarding the level of detail is thus challenging. We propose a methodology for instantiating the simulation calibration process and a framework for automating this process, which makes it possible to pick appropriate levels of detail for any simulator. We demonstrate the usefulness of our approach via two case studies for two different domains.

McDonald, Jessie [University of Hawaii at Manoa, H↗