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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Combining RAVEN, RELAP5-3D, and PHISICS for Fuel Cycle and Core Design Analysis for New Cladding Criteria

The Nuclear Regulatory Commission (NRC) has considered revision of 10 CFR 50.46C rule to account for the effects of burn-up rate in future analysis of reactor accident scenarios so that safety margins may evolve with the operation and reloading of the reactor as dynamic limits. In order to find these limiting conditions, both cladding oxidation and maximum temperature must be cast as functions of fuel exposure. It is computationally intensive to run a plant model through a long duration operational transient to the time of fuel reload, and this must be repeated for each reload until the time of the accident scenario. Moreover, fuel reload patterns may differ in many ways so that calculations with many different core configurations must be considered. To perform such new analyses in a reasonable amount of computational time with good accuracy, Idaho National Laboratory (INL) has developed new multi-physics tools by combining existing codes and adding new capabilities. The Parallel Highly Innovative Simulation INL Code System (PHISICS) toolkit for neutronic and reactor physics is coupled with the Reactor Excursion and Leak Analysis Program (RELAP5-3D) for the Loss of Coolant Accident (LOCA) analysis and RAVEN for the Probabilistic Risk Assessment (PRA) and margin characterization analysis. Expansion of code capability was also necessary. For RELAP5-3D to process a single sequence of cores in a continuous run required modification of RELAP5-3D to restart a sequence of input decks, each with a slightly modified neutronics model or thermal-hydraulic flow region and culminating in an accident scenario. A new multi-deck input processing capability was developed and verified for this analysis. In conclusion, the combined RAVEN/PHISICS/RELAP5-3D tool is used to analyze a typical Pressurized Water Reactor (PWR).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Semiglobal Safety-Filtered Extremum Seeking With Unknown CBFs

We introduce a safe extremum-seeking (Safe ES) algorithm which achieves the minimization of an unknown objective function while ensuring that an unknown, yet measured, control barrier function (CBF) remains above an arbitrarily small negative value for all time. In other words, “practical safety” is maintained during the entire period of convergence to the constrained extremum. Our design is based on quadratic program (QP) CBF style filters for safety, which is applied in an average and estimated sense. Using nonsmooth analysis tools, we guarantee semiglobal practical asymptotic (SPA) stability of the global constrained optimum, practical convergence to the safe set if starting in a condition violating the CBF, and practical safety for all time—semiglobally—if starting in safe set. The safety result of the paper is analogous with modern notions of SPA stability, guaranteeing that, for any small violation of safety, there exist design coefficients which guarantee that such a small violation is not exceeded. The paper outlines a set of sufficient conditions on the barrier and objective functions, and by way of a Lyapunov argument, we demonstrate that nonconvex constrained optimization problems can be solved. We present these results in the setting of a static map and a dynamical system. A simulation example illustrates the results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Energy Exchange Dynamics across L-H Transitions in NSTX

We studied the energy exchange dynamics across the low-to-high-confinement (L-H) in NSTX discharges using the gas-puff imaging (GPI) diagnotic. The investigation focused on the energy exchange between flows and turbulence, to help clarify the mechanism of the L-H transition. We apply this study to three type of heating schemes, including a total of 17 shots from the NSTX 2010 campaign run. Results show that the edge fluctuation characteristics (fluctuation levels, radial and poloidal correlation lengths) measured using GPI do not vary just prior to the H-mode transition, but change after the transition. Using a velocimetry approach (orthogonal-programming decomposition), velocity fields of a 24 $\times$ 30 cm GPI view during the L-H transition were obtained with good spatial ($\sim$1 cm) and temporal ($\sim$2.5 $\mu$s) resolutions. Analysis using these velocity fields shows that the production term is systematically negative just prior to the L-H transition indicating transfer from mean flows to turbulence, which is inconsistent with the predator-prey paradigm. Moreover, using the inferred absolute value of the production term, an estimate of the L-H transition duration is found to be 25 ms, which is much larger than the measured duration. These discrepancies are further reinforced by consideration of the ratio between the kinetic energy in the mean flow to the thermal free energy, which is estimated to be much less than 1, suggesting again that turbulence depletion mechanism may not be playing an important role in the transition to the H-mode. Although the Reynolds work is too small to directly deplete the turbulent free energy reservoir, order-of-magnitude analysis shows that the Reynolds stress may still make a non-negligible contribution to the observed poloidal flows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Evaluation of CFD Methods for Simulation of Two-Phase Boiling Flow Phenomena in a Helical Coil Steam Generator

The U.S. Department of Energy, Office of Nuclear Energy charges participants in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program with the development of advanced modeling and simulation capabilities that can be used to address design, performance and safety challenges in the development and deployment of advanced reactor technology. The NEAMS has established a high impact problem (HIP) team to demonstrate the applicability of these tools to identification and mitigation of sources of steam generator flow induced vibration (SGFIV). The SGFIV HIP team is working to evaluate vibration sources in an advanced helical coil steam generator using computational fluid dynamics (CFD) simulations of the turbulent primary coolant flow over the outside of the tubes and CFD simulations of the turbulent multiphase boiling secondary coolant flow inside the tubes integrated with high resolution finite element method assessments of the tubes and their associated structural supports. This report summarizes the demonstration of a methodology for the multiphase boiling flow analysis inside the helical coil steam generator tube. A helical coil steam generator configuration has been defined based on the experiments completed by Polytecnico di Milano in the SIET helical coil steam generator tube facility. Simulations of the defined problem have been completed using the Eulerian-Eulerian multi-fluid modeling capabilities of the commercial CFD code STAR-CCM+. Simulations suggest that the two phases will quickly stratify in the slightly inclined pipe of the helical coil steam generator. These results have been successfully benchmarked against both empirical correlations for pressure drop and simulations using an alternate CFD methodology, the dispersed phase mixture modeling capabilities of the open source CFD code Nek5000.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Uncertainty Quantification of Calculated Temperatures for the AGR 5/6/7 Experiment

This report documents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) 5/6/7 fuel irradiation experiment conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technologies? research and development program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR 5/6/7 capsules, the results of the numerical simulations are used in combination with statistical analysis methods to improve qualification of measured data. The calculated fuel temperatures for AGR tests are also used for validation of the fission product transport and fuel performance simulation models. These crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. This report covers temperature uncertainty results for each of the five AGR 5/6/7 capsules. To quantify the uncertainty of calculated temperatures determined using the ABAQUS finite element heat transfer code, this study identifies and analyzes model parameters of potential importance to the calculated temperatures of fuel compacts and thermocouples. The selection of input parameters for uncertainty quantification is based on the ranking of their influences upon temperature predictions. Thus, selected input parameters include those with high sensitivity and those with the largest uncertainty. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of calculated temperatures. Measurement uncertainty, analysis of modeling assumptions, and expert judgment are used as the basis to quantify the uncertainty range for selected input parameters. The input uncertainties are dynamic, accounting for the effect of unplanned events and changes in thermal properties of capsule components over extended exposure to high temperatures and fast neutron irradiation. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using experimental design, analysis of pairwise interactions of model parameters was performed to establish sufficiency of the time dependent first order (linear) expansion terms in constructing the temperature response surface. To achieve completeness, uncertainty propagation made use of pairwise noise correlations of model parameters. Furthermore, using an interpolation scheme over the input parameter domain, the analysis obtains time dependent sensitivity over the test campaign duration. This allows computation of uncertainty for the calculated fuel temperatures and the calculated graphite temperatures at thermocouple locations during the entire irradiation period.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage

This study investigates the technical and economic feasibility of using high levels of solar energy penetration up to 400 MW into a smart grid system of 60,000 smart houses. A novel non-cooperative Stackelberg game is introduced that incorporates the profitability of the supply-side and helps in solving problems related to overgeneration and photovoltaic curtailment. The non-cooperative game is intended to find the optimal dynamic prices that would leverage distributed storage through the demand-side to stabilize the power grid operation. Ten cases are studied with five photovoltaic plant sizes and two battery designs. Here, a novel quantitative analysis of high levels of solar penetration as a percentage of the total electricity demand is introduced to evaluate the technical feasibility of the studied cases. To evaluate the economic viability of the proposed smart grid system, four metrics were used: the levelized cost of energy, the levelized cost of storage, the payback period, and the net present value. Two out of ten studied cases were concluded to be the most promising cases, one with a solar photovoltaic plant size of 200 MW and the other with 300 MW. The case with 300 MW solar plant is preferred as it paves the way for more solar energy deployment with a solar penetration percentage up to 67.78%. This case had a payback period of 10.72 years and a net present value of $\$51.44$ M for the solar plant and a payback period of 12.06 years and a net present value of $\$40.75$ M for the demand-side.

14 SOLAR ENERGY↗
DOE OSTI2020

Integrated Traction Drive Thermal Management: Keystone Project 3

The presentation was developed for the U.S. Department of Energy (DOE) Vehicle Technologies Program 2020 Annual Merit Review and Peer Evaluation Meeting. It reports on progress and accomplishments of DOE funded Integrated Traction Drive Thermal Management project. Highlighted are NREL's research accomplishments during FY 2020. Challenges associated with thermal management of integrated traction drives for electric vehicles arise due to the limited packaging space, coolant distribution, electrical connections, and ambient temperatures. For this project at NREL, research focuses on enabling thermal management technologies to support a modular, compact, self-contained integrated electric drive unit with motor and power electronics. Integration approaches, based on packaging location and coolant options for power electronics integrated with candidate motor technologies to reduce cables and connectors, are reviewed and compared using finite element analysis (FEA) tools. Automatic transmission fluid (ATF) jet impingement cooling characterization is carried out experimentally (measuring heat transfer coefficients) and using computational fluid dynamics (CFD) modeling.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Measurement of $\nu_\mu$ CC Interactions With Two-Proton Final State in MINERvA

This dissertation presents a measurement of charged–current (CC) muon–neutrino interactions with exactly two protons and no pions in the final state (CC~$2p\,0\pi$), using data collected by the MINERvA detector in the NuMI medium–energy beam at Fermilab. Such two–proton topologies are a sensitive probe of nuclear dynamics in the few–GeV regime, including multi–nucleon correlations (npnh, notably $2p2h$) and intranuclear final–state interactions (FSI) such as pion absorption and nucleon rescattering. A precise experimental characterization of these processes is essential both for neutrino–interaction theory and for reducing systematic uncertainties in oscillation experiments that rely on accurate modeling of neutrino–nucleus interactions. Events are selected by requiring a $\nu_\mu$ CC interaction with a reconstructed $\mu^-$ and two proton tracks originating from a common vertex in MINERvA’s finely segmented scintillator tracker, with no reconstructed mesons. Muon charge and momentum are constrained by matching to the MINOS Near Detector, while proton identification exploits energy–loss profiles and stopping–proton features. Backgrounds from pion–producing channels that enter the signal region through FSI or reconstruction effects are constrained with data–driven sidebands (Michel–electron and isolated–cluster “blob” samples) and tuned via a simultaneous fit across signal and sideband regions. To correct detector resolution and acceptance effects, the analysis employs iterative Bayesian unfolding with extensive validation: statistical pseudo–experiments, and robustness checks against generator systematic “universes” and additional strong shape warps. Single–differential cross sections are reported for three observables tailored to the two–proton final state: the opening–angle cosine $\cos\!\left(\theta_{pp}\right)$, the leading–proton momentum, and the subleading–proton momentum. Systematic uncertainties include contributions from neutrino flux, interaction modeling (e.g., npnh and resonance parameters, pion FSI), and detector response (calibration, reconstruction efficiencies). The resulting distributions provide targeted constraints on the interplay of multi–nucleon dynamics and FSI that shape CC~$2p\,0\pi$ final states on hydrocarbon. Comparisons to modern GENIE–based simulations highlight kinematic regions where model components require refinement. These measurements thus inform generator tuning and improve the reliability of neutrino–energy reconstruction strategies for current and future long–baseline oscillation programs.

Syrotenko, Vladyslav S. [Tufts U.]↗
DOE OSTI2023

Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation

This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

Establishing up-to-date large scale building maps is essential to understand the urban dynamics, such as estimating population, urban planning, and many other applications. Although many computer vision tasks have been successfully carried out with deep convolutional neural networks, there is a growing need to understand their large scale impact on building mapping with remote sensing imagery. In this paper, taking advantage of the scalability of convolutional neural networks (CNNs) and using only few areas with the abundance of building footprints, for the first time we conduct a comparative analysis of four state-of-the-art CNNs for extracting building footprints across the entire continental United States. The four CNN architectures namely: Branch-out CNN, fully convolutional network (FCN), conditional random field as recurrent neural network (CRFasRNN), and SegNet, support semantic pixelwise labeling and focus on capturing textural information at multiscale. We use 1-meter resolution aerial images from National Agriculture Imagery Program as the test-bed, and compare the extraction results across the four methods. In addition, we propose to combine signed-distance labels with SegNet, the preferred CNN architecture identified by our extensive evaluations, to advance building extraction results to instance level. We further demonstrate the usefulness of fusing additional near IR information into the building extraction framework. Large scale experimental evaluations are conducted and reported using metrics that include: Precision, recall rate, intersection over union, and the number of buildings extracted. With the improved CNN model and no requirement of further postprocessing, we have generated building maps for the United States with an average processing time less than one minute for an area of size &#x223C;<!-- ~ --> 56 km 2 . Finally, the quality of extracted buildings and processing time demonstrated that the proposed CNN based framework fits the need of building extraction at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

ProteoSign: an end-user online differential proteomics statistical analysis platform

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a userfriendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ ProteoSign.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Data-dependence Profiling to Enable Safe Thread Level Speculation

Data-dependence profling is a technique that enables a com- piler to judiciously decide when the execution of a loop | which the compiler could not prove to be dependence free | should be speculated through the use of Thread Level Spec- ulation (TLS). The data collected by a data-dependence pro- fler can be used to predict if may dependencies reported by a compiler static analysis are likely to materialize at runtime. A cost analysis can then be used to decide that some loops with a lower probability of dependence should be specula- tively parallelized. This paper addresses the question as to whether a loops' dependence behaviour changes when the in- put to the program changes | a study of 57 different bench- marks indicates that it usually does not change. Then the paper describes SpecEval, an automatic speculative paral- lelization framework that uses single-input data-dependence profles to find speculation candidates in the SPEC2006 and PolyBench/C benchmarks. This paper also presents a per- formance evaluation of TLS implementation in IBM's Blue- Gene/Q supercomputer and shows that the performance of TLS is affected by several factors, including the number of speculated loops, the execution-time coverage of speculated loops, the miss-speculation overhead, the L1 cache miss rate and the effect on dynamic instruction path length.

Bhattacharyya, Arnamoy↗
DOE OSTI2019

Wabash CarbonSAFE Subtask 7.1 – Pre-drilling Site Assessment Topical Report: DOE-FE0031626-1

The Wabash CarbonSAFE project will drill a stratigraphic test well at the Wabash Valley Resources facility near Terre Haute, Indiana. Lithologic, geomechanical, and geophysical data collected from samples testing and an extensive geophysical logging suite will be used to determine the feasibility for the geologic sequestration of 50 million metric tons or more of injected carbon dioxide. The primary target reservoir and caprock to be investigated for the Wabash CarbonSAFE project are the Mt. Simon Sandstone and Eau Claire Shale, respectively. The project team has assessed the planned Wabash #1 drilling site using subsurface geologic data and analyses from both Illinois and Indiana. These data were used to generate geologic structure and isopach thickness maps for target reservoir and caprock layers which were incorporated into a detailed discussion of the nature of the Illinois Basin’s Cambro-Ordovician carbon dioxide storage complex at the drilling site. Formation tops were prognosed for 19 lithologic units to aid drilling and coring procedures. Additional GIS analysis indicates that no known structural features will negatively impact the target storage reservoir or caprock, nor will protected and sensitive areas, groundwater resources, or existing resource development be negatively affected by Wabash #1 drilling activities. The Wabash #1 Geologic/Structure Test well drilling permit was awarded by Indiana Department of Natural Resources (IN-DNR) – Division of Oil and Gas on November 12th, 2019, and the well is expected to be drilled to a total depth of approximately 8,350 feet. Potential impacts on drilling progress include abandoned underground coal mines which mined the Springfield and Survant coals until 1964 and 1954, respectively, and a lost circulation zone in the Potosi Dolomite, which has been demonstrated to have intermittent cavernous porosity from karstification. This document also details the coring and sampling program, proposed logging suite, and well testing program, all of which will be dynamically reviewed as drilling progresses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

DENNIS: a design and analysis tool for dynamic material x-ray diffraction experiments

We present DENNIS (Diffraction Experiment desigN and aNalysiS): a graphical software tool useful for the design and analysis of dynamic x-ray diffraction experiments, such as those performed on the Z Pulsed Power Facility, Thor Pulsed Power Generator, and Dynamic Compression Sector (DCS) of the Advanced Photon Source. DENNIS provides rapid powder and single-crystal diffraction pattern predictions and powder diffraction pattern image integration in three-dimensional geometries. Additional features include crystallographic information file reading, image processing, and synthetic diffraction pattern image generation. We overview the software's capabilities, detail the prediction and integration methodologies, and provide example implementations on Z and DCS experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Identification and quantification of multinuclear Cu active sites derived from monomeric Cu moieties for dry NO oxidation over Cu-SSZ-13

The identification and quantification of multinuclear Cu-oxo cations in Cu-zeolites is a challenge amidst the dynamic nature of Cu ions in Cu-zeolites. Herein, we synthesized Cu-SSZ-13 zeolites containing varying densities of monomeric Cu cations (Z 2 Cu and ZCuOH, where Z represents an anionic site on the zeolite framework). The number of Cu-oxo cations, quantified using carbon monoxide temperature programmed reduction (CO-TPR), quadratically increased with increasing ZCuOH density, evidencing that proximal ZCuOH sites are precursors to Cu-oxo dimers. We also find that dehydrated proximal ZCuOH with overlapping diffusion radii of at most 0.5 nm can form Cu-oxo dimers. CO-TPR also revealed the presence of three distinct pools (i.e types) of Cu-oxo dimers; these pools catalyze dry NO oxidation at different turnover rates, with the fastest pool 2 to 30 times faster than the slowest pool. Finally, from reaction kinetics and rate law analysis, we also proposed a cohesive dry NO oxidation mechanism and demonstrated that nitrates detected by in situ FTIR are a spectator species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗