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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Remove Welding Residual Stress for CFETR Vacuum Vessel by Trailing Ultrasonic Impact Treatment

The China fusion engineering test reactor (CFETR) vacuum vessel is welded by narrow gap TIG (NG-TIG) welding, and the welding residual stress of the CFETR vacuum vessel can be redistributed by trailing welding ultrasonic impact treatment. In order to investigate the feasibility of the residual stress removing scheme, and to obtain the optimal trailing ultrasonic impact treatment technological parameters in the process of removing welding residual stress, a welding model that similar to vacuum vessel welding seam is established by using ABAQUS software, a NG-TIG welding heat source subroutine which is written in FORTRAN language used to simulate NG-TIG welding. According to the welding simulation results, a trailing welding ultrasonic impact treatment model is established, and the effects of the impact pin number, the impact method, the impact pin diameter and the impact frequency on welding residual stress are studied. The results show that the longitudinal residual stress in welding seam and its adjacent area and the lateral residual stress in the whole region have been obviously decreased by different trailing welding ultrasonic impact processes, and have made the tensile stress in the welding seam and its adjacent area has been changed into compressive stress, which can provide theoretical guidance and reference for actual production.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Subtask 1.3 – Integrated Carbon Capture and Storage for North Dakota Ethanol Production

The Energy & Environmental Research Center (EERC), in partnership with Red Trail Energy, LLC (RTE), a North Dakota ethanol producer; the North Dakota Industrial Commission (NDIC) Renewable Energy Program (REP); and the U.S. Department of Energy (DOE), conducted a feasibility and implementation study for a commercial carbon capture and storage (CCS) effort. This subtask provided technical support and developed recommended practices of how small-scale industrial CO2 emitters (<1,000,000 tonnes of CO2 emitted annually) may economically deploy CCS. The 64-million-gallon dry mill RTE ethanol facility, emitting an average 180,000 tonnes of CO2 annually, was the subject of the case study. The positive outcome of this research, which shows technical and economic potential for ethanol–CCS in North Dakota, has resulted in RTE acquiring an approved Permit to Drill on December 2, 2019, for a stratigraphic test well in early 2020, a necessary step toward a North Dakota CO2 Storage Facility Permit (SFP) for the RTE CCS effort. Outcomes include 1) validating the Broom Creek Formation as a regional target for CCS, 2) determining the full carbon life cycle of an industrial fuel production facility with CCS, 3) developing a field implementation plan (FIP) for small-scale CCS, and 4) determining the validity and pathway for using CCS to meet low-carbon fuel (LCF) standards. The technical team included the EERC, RTE, Trimeric Corporation, Schlumberger Carbon Services, and Computer Modelling Group (CMG). Findings from activities conducted November 2016 – May 2020 are summarized as follows. Several key steps have been accomplished toward validating the Broom Creek Formation as a CO2 injection and storage target at the RTE CCS site, including verification of the presence and structure of sandstone layers that may comprise the potential CO2 storage reservoir and the several thousand feet of overlying confining zone. Work included site characterization using existing data, interpretation of seismic data within the study area, and geologic modeling simulation of CO2 injection. Interpreted results estimate 3000 feet of confining zone between the Broom Creek Formation (storage target) and the lowermost underground source of drinking water (e.g., the Fox Hills Formation). The thickness of the Broom Creek injection target varies 230–420 ft within the survey area. Results were used to inform the location of a stratigraphic test well and associated characterization test program. No impediments were identified within the targeted CO2 storage complex that would prevent the project from moving forward. A stratigraphic test well is the next step to validate these results and to acquire remaining data necessary to develop a North Dakota CO2 SFP application. The EERC generated full carbon life cycle estimates for CCS integration with the RTE ethanol facility. Results indicated that an average 40% reduction in CO2 emissions is possible through CCS implementation. Approximately half of the carbon in the overall life cycle for a dry mill ethanol plant is generated through the fermentation process and emitted to the atmosphere; this is the CO2 stream targeted for CCS. The remaining carbon is attributed to corn feedstock farming (diesel, fertilizer), energy for fuel processing (natural gas, electricity), and transportation (diesel) of the fuel product. Life cycle carbon estimates are also affected by the anticipated energy consumption of a potential capture facility, which depend on the type of CO2 product generated. For example, a 30%–40% net CO2 emission reduction is estimated if a liquefied CO2 facility were incorporated compared to a 40%–50% net CO2 reduction if a supercritical “injection-grade” CO2 product is generated; i.e., more energy is required to further refine the CO2 stream, affecting the full carbon life cycle estimates. A CCS FIP was developed and initiated at the RTE CCS site, resulting in the development of several guidance documents: a CO2 Capture Process Design Package, a North Dakota CO2 geologic Storage Permits Template, and a Public Outreach Package for CCS in North Dakota. General FIP components include CO2 capture system, pipeline and well designs; monitoring, verification, and accounting (MVA) plans; geologic characterization and testing programs; and permitting and outreach plans. Vendor bids were also acquired for the CO2 liquefaction facility. Near-surface characterization (groundwater and soil gas sampling) and geologic characterization (seismic survey) were initiated to inform development of a UIC Class VI-compliant MVA plan compliant with a North Dakota CO2 SFP. Designs (well and geologic testing) were completed for a stratigraphic test well compliant with a North Dakota CO2 SFP. In addition, the outreach plan was executed, including community open houses, meetings with city/county/state officials, and development of public materials. Although other entities continued to mature incentive programs in 2019–2020, California and the Internal Revenue Service (IRS) currently provide the most advanced economic opportunities for CCS integrated with fuel production. The California Low-Carbon Fuel Standard (LCFS) adopted a CCS Protocol in January 2019, allowing submittal of a design-based pathway (DBP) application for an approved temporary (not certified) carbon intensity value for a fully engineered facility. An ethanol–CCS DBP application to the California LCFS Program (officially approved February 28, 2020) was developed to show that the RTE CCS effort meets LCFS requirements. The approved DBP provides confidence to advance the project and supports potential investments. Other entities continue to mature incentive programs. The IRS issued guidance in February 2020 that addresses the definition of beginning of construction and revenue procedure on partnerships for the Enhancement of Carbon Dioxide Sequestration Credit (a.k.a. Section 45Q) CCS tax credit program; the IRS anticipates issuing further guidance on issues such as secure geologic storage, utilization qualifications, and recapture of claimed credits. Maturing incentive programs coupled with workable permitting regulations provide confidence to advance CCS projects in North Dakota and support financial investment to proceed with designing, constructing, and implementing CCS projects at small-scale fuel production facilities. The largest hurdles for CCS implementation at small-scale industrial systems are often business/economic-related (i.e., not technical). Market uncertainty already exists for agriculture-based alternative fuels such as corn ethanol, for which production has increased by ~33%, and prices have correspondingly lowered since 2015. Passing the Section 45Q tax credit program improves economic feasibility for CCS but may require external investors for a small business to achieve maximum benefits. Public–private partnerships with NDIC and DOE have resulted in foundational technical and regulatory knowledge, growing stakeholder confidence, and a pathway to implementation that enables similar industrial CCS projects in the region to advance. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by NDIC and RTE. The authors would also like to thank CMG, ESRI, IHS, Neuralog, and Schlumberger for allowing the use of their software packages in support of this work.

42 ENGINEERING↗
DOE OSTI2016

The ADAQ framework: An integrated toolkit for data acquisition and analysis with real and simulated radiation detectors

Here, the ADAQ framework is a collection of software tools that is designed to streamline the acquisition and analysis of radiation detector data produced in modern digital data acquisition (DAQ) systems and in Monte Carlo detector simulations. The purpose of the framework is to maximize user scientific pro-ductivity by minimizing the effort and expertise required to fully utilize radiation detectors in a variety of scientific and engineering disciplines. By using a single set of tools to span the real and simulation domains, the framework eliminates redundancy and provides an integrated workflow for high-fidelity comparison between experimental and simulated detector performance. Built on the ROOT data analysis framework, the core of the ADAQ framework is a set of Cþþ and Python libraries that enable high-level control of digital DAQ systems and detector simulations with data stored into standardized binary ROOT files for further analysis. Two graphical user interface programs utilize the libraries to create powerful tools: ADAQAcquisition handles control and readout of real-world DAQ systems and ADAQAnalysis provides data analysis and visualization methods for experimental and simulated data. At present, the ADAQ framework supports digital DAQ hardware from CAEN S.p.A. and detector simulations performed in Geant4; however, the modular design will facilitate future extension to other manufacturers and simulation platforms.

detector simulation↗
DOE OSTI2020

Spitfire

Spitfire is a Python and C++ code for solving canonical flame structure problems to build tabulated chemistry models for the simulation of large-scale fires. Spitfire is a modular software with performance critical code written in C++, abstract numerical methods provided in Python, and chemistry tabulation techniques made convenient for high-level use by non-expert users. It facilitates both high-level usage and novel research in tabulated chemistry approaches. SAND2019-11791 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Armstrong, Elizabeth↗
DOE OSTI2024

Prime VI

SAND2025-03757O Prime VI is a distribution-of-disease outbreak model calibration code based on variational inference. It accompanies a publication for submission to Statistics in Medicine journal, and the code will be maintained for open-source use on Sandia's GitLab. The software provides methods for calibrating an epidemiological model to measured case-count data for a multitude of correlated spatial regions. The code solves a Bayesian inverse problem for model calibration where the posterior over-model parameters are approximated through a custom implementation of variational inference. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗
DOE OSTI2025

Vedizar Fingerprinter

SAND2025-03289O Vedizar Fingerprinter simplifies the process of identifying devices on a network by analyzing traffic data. It uses a unique library to recognize different devices, making it easier for users to understand what is happening on their networks. This software is ideal for IT and operational technology environments, helping organizations monitor their networks effectively. By saving results in a database, it allows for easy access and review of device information. Users can enhance their network security and optimize performance without needing specialized hardware or technical expertise. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Jacobellis, John [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2024

Control System for RF OPM

SAND2025-01910O This software built in LabVIEW FPGA provides experimental control of the RF OPM prototype via a reconfigurable NI I/O card. It samples the analog input, processes it on the FPGA in real time, and generates control and feedback outputs to control and optimize the operation of the device. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Bainbridge, Jonathan↗
DOE OSTI2016

GenAp: a distributed SQL interface for genomic data

Abstract Background The impressively low cost and improved quality of genome sequencing provides to researchers of genetic diseases, such as cancer, a powerful tool to better understand the underlying genetic mechanisms of those diseases and treat them with effective targeted therapies. Thus, a number of projects today sequence the DNA of large patient populations each of which produces at least hundreds of terra-bytes of data. Now the challenge is to provide the produced data on demand to interested parties. Results In this paper, we show that the response to this challenge is a modified version of Spark SQL, a distributed SQL execution engine, that handles efficiently joins that use genomic intervals as keys. With this modification, Spark SQL serves such joins more than 50× faster than its existing brute force approach and 8× faster than similar distributed implementations. Thus, Spark SQL can replace existing practices to retrieve genomic data and, as we show, allow users to reduce the number of lines of software code that needs to be developed to query such data by an order of magnitude.

Kozanitis, Christos↗
DOE OSTI2017

Water Quality Projects Summary for the Mid-Columbia and Cumberland River Systems

Scheduling and operational control of hydropower systems is accompanied with a keen awareness of the management of water use, environmental effects, and policy, especially within the context of strict water rights policy and generation maximization. This is a multi-objective problem for many hydropower systems, including the Cumberland and Mid-Columbia river systems. Though each of these two systems have distinct operational philosophies, hydrologic characteristics, and system dynamics, they both share a responsibility to effectively manage hydropower and the environment, which requires state-of-the art improvements in the approaches and applications for water quality modeling. The Department of Energy and Oak Ridge National Laboratory have developed tools for total dissolved gas (TDG) prediction on the Mid-Columbia River and a decision-support system used for hydropower generation and environmental optimization on the Cumberland River. In conjunction with IIHR - Hydroscience & Engineering, The University of Iowa and University of Colorado s Center for Advanced Decision Support for Water and Environmental Systems (CADSWES), ORNL has managed the development of a TDG predictive methodology at seven dams along the Mid-Columbia River and has enabled the ability to utilize this methodology for optimization of operations at these projects with the commercially available software package Riverware. ORNL has also managed the collaboration with Vanderbilt University and Lipscomb University to develop a state-of-the art method for reducing high-fidelity water quality modeling results into surrogate models which can be used effectively within the context of optimization efforts to maximize generation for a reservoir system based on environmental and policy constraints. The novel contribution of these efforts is the ability to predict water quality conditions with simplified methodologies at the same level of accuracy as more complex and resource intensive computing methods. These efforts were designed to incorporate well into existing hydropower and reservoir system scheduling models, with runtimes that are comparable to existing software tools. In addition, the transferability of these tools to assess other systems is enhanced due the use of simplistic and easily attainable values for inputs, straight-forward calibration of predictive equation coefficients, and standardized comparison of traditionally familiar outputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Piezoelectric Load Cell Literature Review

The purpose of this document is to analyze the benefits and drawbacks of using piezoelectric load cells, as well as to provide an explanation of how they work. Providing a better understanding of piezoelectric load cells may open up more possible applications and will allow for more effective use of piezoelectric load cells in Q-14. The following was found via literature research: Piezoelectric load cells smaller size, although a minor difference, makes them easier to handle and transport; Piezoelectric load cells are more expensive, require periodic recalibration at potentially higher frequencies than conventional load cells, and require signal conditioning equipment (Signal Amplifier and compatible DAQ Software); conventional load cells can measure torsion and tension/compression at the same time, this cannot be done with a single piezoelectric load cell, at least one additional specialized piezoelectric load cell is required for this application

42 ENGINEERING↗
DOE OSTI2019

Achieving Verifiable and High Integrity Instrumentation and Control Systems through Complexity Awareness and Constrained Design. Final Report

While most process control industries have been able to embrace digital Instrumentation and Control (I&C) technology to improve performance, reliability, maintainability, and efficiency of production, the nuclear industry has been relatively slow to adopt digital I&C for safety critical plant functions. There are a variety of reasons for this, but in general the nuclear industry takes a conservative stance that new technologies must mature before adoption. A deeper underlying reason for not fully realizing digital I&C and automation in nuclear power is associated with the complexity that can manifest in software-based systems that is different in nature from the complexity of their analog predecessors. Accordingly, this research pursued the development of innovative architectures and methods to “constrain complexity” to enhance verifiability of digital I&C devices. These constraints can positively impact the reduction of Software Common Cause Failures (SCCF) and other systematic errors. The architectural concept developed in this research effort is called SymPLe. SymPLe is specifically aimed at safety critical functions within Nuclear Power Plants and other critical infrastructure where constraining device complexity to support verifiability and safety case arguments is important. The SymPLe architecture is designed to promote “accessible verification” between OEM/utility engineers and the regulatory agencies who review and license nuclear power plant I&C systems. The major contributions of this work to the nuclear industry are: Demonstration of SymPLe as a viable approach to “complexity aware” I&C systems and Embedded Digital Devices.Comprehensive use of model-based design and engineering to produce verifiable I&C systems for the nuclear industry with clear findings on the usability of model-based engineering. Employ comprehensive use of model-based engineering, testing, and verification in an IEC 61508 workflow in support of design assurance. Gathered evidence of “difficult to find” design flaws found using model-based design and testing methods. Detailed the nature of these SCCFs including where they were introduced, the triggers, and how they were found. Gathered evidence on the synergistic use of practical formal methods and testing and how these methods can be used to significantly increase assurance of reduction and avoidance of design flaws.

Testing↗
DOE OSTI2019

Applied Controls for sCO 2 Brayton Cycles

Sandia has a history of testing supercritical CO 2 Brayton Cycles to explore operation fundamentals and provide validation data for computer models. These systems have always had data acquisition and controls features. The Development Platform (DP) has been a flagship system for loop testing and operation but has been limited to manual control of many systems. Manual operation has increased operating complexity and reduced stability and repeatability. This work documents automated control development by linearizing otherwise non-linear valves, addition of closed-loop proportional-integral control software that has been tuned to the Sandia DP. It also describes testing of control methods that have improved test quality and reliability as shown in actual test data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Intelligent System Partitioning for Agent-Based Security Constrained Optimal Power Flow

This project developed scalable, computationally efficient algorithms to solve realistic large-scale power system optimization problems as part of a larger series of competitions run by ARPA-E. These problems are important because the secure and reliable operation of the power grid, especially under increased uncertainty and variability, is growing increasingly challenging. The economic feasibility of the proposed methods developed by our team is quite low, considering it’s a purely software-based solution to operate power grids more efficiently. The technical effectiveness, as evidenced by our performance in the competition, balances heuristics and approximations to provide a tradeoff between speed and accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The Weak Form Is Stronger Than You Think

The weak form is a ubiquitous, well-studied, and widely-utilized mathematical tool in modern computational and applied mathematics. In this work we provide a survey of both the history and recent developments for several fields in which the weak form can play a critical role. In particular, we highlight several recent advances in weak form versions of equation learning, parameter estimation, and coarse graining, which offer surprising noise robustness, accuracy, and computational efficiency. We note that this manuscript is a companion piece to our October 2024 SIAM News article of the same name. Here we provide more detailed explanations of mathematical developments as well as a more complete list of references. Lastly, we note that the software with which to reproduce the results in this manuscript is also available on our group's GitHub website https://github.com/MathBioCU .

26A33, 35D30, 62FXX, 62JXX, 65L09, 65M32, 68Q32,↗
DOE OSTI2022

Coupling subsurface and above-surface models for optimizing the design of borefields and district heating and cooling systems in the presence of varying water-table depth. In: Proceedings, 46th Workshop on Geothermal Reservoir Engineering

Dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange. Current modeling approaches in building and district energy simulation tools typically consider heat conduction through the ground between boreholes, without flow of groundwater. On the other hand, detailed simulation tools for subsurface heat and mass transfer exist, but these fall short in simulating above-surface energy systems. To support the design and operation of such systems, we have developed a coupled model including a software package for building and district energy simulation, and software for detailed heat and mass transfer in the ground. For the first, we use the open-source Modelica Buildings Library, which includes dynamic simulation models for building and district energy and control systems. For the heat and mass transfer in the soil, we use the TOUGH simulator. TOUGH can model heat and multi-phase, multi-component mass transport for a variety of fluid systems, as well as chemical reactions, in fractured porous media. In previous work, we described the coupling of these software packages, including how time-dependent boundary conditions for the borehole walls are synchronized for use in Modelica and TOUGH. We verified that the coupled Modelica/TOUGH code produced consistent results with the original Modelica code for an idealized problem in which heat transfer was purely by conduction in a uniform geologic medium. Here, we examine less idealized problems for which TOUGH’s advanced capabilities for modeling fluid flow are required. The first problem has a shallow vadose zone, and the second problem has a thicker vadose zone with a water-table depth that varies in time, which requires a fine vertical grid discretization for the TOUGH model.

Doughtry, Christine↗
DOE OSTI2022

A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)

The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An introduction to partial differential equations constrained optimization

Partial differential equation (PDE) constrained optimization is designed to solve control, design, and inverse problems with underlying physics. A distinguishing challenge of this technique is the handling of large numbers of optimization variables in combination with the complexities of discretized PDEs. Over the last several decades, advances in algorithms, numerical simulation, software design, and computer architectures have allowed for the maturation of PDE constrained optimization (PDECO) technologies with subsequent solutions to complicated control, design, and inverse problems. This special journal edition, entitled “PDE-Constrained Optimization”, features eight papers that demonstrate new formulations, solution strategies, and innovative algorithms for a range of applications. In particular, these contributions demonstrate the impactfulness on our engineering and science communities. This paper offers short remarks to provide some perspective and background for PDECO, in addition to summaries of the eight papers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities

The behavior of modern power systems becomes more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority (BA) to predict the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities that successfully predicates the growing system peak load and increased stress levels under extreme heat waves in the United States of America.

Berscheid, Alan P.↗