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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mass transport in nanoarray monolithic catalysts: An experimental-theory study

Reducing the mass transfer resistance globally of a catalyst is a key to enhancing the catalytic reaction kinetics and fully utilizing the catalyst activity. Despite the success in tailoring the external mass transfer in the widely studied washcoat monoliths, the internal mass transfer resistance is difficult to be reduced due to the requirement of increasing macroporosity while maintaining high specific surface area and mechanical stability. Therefore, nanostructured array-based monolithic catalysts (nanoarray catalysts) have been developed in the past decade as a promising class of structured catalysts that may complement or substitute washcoat catalysts. This work fundamentally elucidates the enhanced mass transport properties of the nanoarray monolithic catalysts by a combination of experimental measurements and theoretical modeling. Using a low-dimensional model, the relative contributions of resistances were quantified in terms of chemical kinetics, internal and external mass transfers based on a probe model of C2H4 oxidation over the TiO2 supported Pt-based monolithic catalysts. The nanoarray catalysts displayed a lower internal mass transfer resistance than the washcoat counterparts as a result of the high macroporosity and small thickness of nanoarray layers. Finally, the nanoarray configuration provides a new pathway towards designing high-performance monolithic reactors and catalysts with low internal diffusion limitations for various gas phase reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Characterization of commercial vehicles’ start-up operations from in-use data

Diesel engines produce disproportionate levels of emissions when the engine and after-treatment systems are operating at low temperatures. This situation arises most commonly when the vehicle is first started after overnight. To quantify emissions attributable to vehicle starts, a sizable collection of on-road commercial vehicle operating data is analyzed to identify start-up events and inspect associated emissions. Data was obtained from the National Renewable Energy Laboratory’s (NREL’s) Fleet DNA and from the Center for Environmental Research & Technology (CE-CERT). Included are 500 + diesel vehicles with more than 42,000 recorded days, drawn from 25 vocational categories across the United States. Analysis shows that vehicle behavior, in terms of engine cold- and warm-operation, starts per day, soak time, and warm-up duration, differs significantly between vehicle vocations. Also, weighting factors for cold- and hot-starts currently used in the U.S. Environmental Protection Agency’s Federal Test Procedure (FTP) for heavy-duty emissions certification accurately represent real-world operations. Although the FTP includes a comparable fraction of cold operation, the hot fraction is much shorter than real-world operation due to limited test duration. The investigation also revealed that real-world engines operate for a significant amount of time when the engine coolant is in the “hot-stabilized” region, but the selective catalytic reduction (SCR) temperature is below its effective operating temperature of 200 °C. Of the vehicles under investigation, almost 20% of their operational time is within this condition. Therefore, novel approaches to raise and maintain SCR temperature are highly required to further reduce engine emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2023

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Abstract Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris . This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Transforming an ATP-dependent enzyme into a dissipative, self-assembling system

Nucleoside triphosphate (NTP)-dependent protein assemblies such as microtubules and actin filaments have inspired the development of diverse chemically fueled molecular machines and active materials but their functional sophistication has yet to be matched by design. Given this challenge, we asked whether it is possible to transform a natural adenosine 5'-triphosphate (ATP)-dependent enzyme into a dissipative self-assembling system, thereby altering the structural and functional mode in which chemical energy is used. Furthermore we report that FtsH (filamentous temperature-sensitive protease H), a hexameric ATPase involved in membrane protein degradation, can be readily engineered to form one-dimensional helical nanotubes. FtsH nanotubes require constant energy input to maintain their integrity and degrade over time with the concomitant hydrolysis of ATP, analogous to natural NTP-dependent cytoskeletal assemblies. Yet, in contrast to natural dissipative systems, ATP hydrolysis is catalyzed by free FtsH protomers and FtsH nanotubes serve to conserve ATP, leading to transient assemblies whose lifetimes can be tuned from days to minutes through the inclusion of external ATPases in solution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Three dimensional low-mode areal-density non-uniformities in indirect-drive implosions at the National Ignition Facility

To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and high areal density at stagnation. The size of the driver and scale of the capsule required can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Significant 3D low mode asymmetries, however, are commonly observed in indirect-drive implosions and reduce the coupling of shell kinetic energy to the hotspot. To better quantify the magnitudes and impacts of shell density asymmetries, we have developed new analysis techniques and analytic models [Hurricane et al., Phys. Plasmas 27(6), 062704 (2020)]. To build confidence in the underlying data, we have also developed an analytic neutron transport model to cross-compare two independent measurements of asymmetry, which shows excellent agreement across shots for mode-1 (ℓ = 1). This work also demonstrates that asymmetry can introduce potential sampling bias into down-scattered ratio measurements causing the solid-angle-average and uncertainty-weighted-average down-scattered ratios to differ significantly. Diagnosing asymmetries beyond mode-1 (ℓ > 1) presents significant challenges. Using new diagnostic instruments and analysis techniques, however, evidence of significant Legendre mode P2 (ℓ = 2, m = 0) and additional 3D asymmetries (ℓ > 1, m ≠ 0) are beginning to emerge from the high precision activation diagnostic data (real-time nuclear activation detectors) and down-scattered neutron imaging data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Computationally efficient zero-noise extrapolation for quantum-gate-error mitigation

Zero noise extrapolation (ZNE) is a widely used technique for gate error mitigation on near term quantum computers because it can be implemented in software and does not require knowledge of the quantum computer noise parameters. Traditional ZNE requires a significant resource overhead in terms of quantum operations. A recent proposal using a targeted (or random) instead of fixed identity insertion method (riim versus fiim) requires significantly fewer quantum gates for the same formal precision. We start by showing that riim can allow for ZNE to be deployed on deeper circuits than fiim but requires many more measurements to maintain the same statistical uncertainty. We develop two extensions to fiim and riim. The List Identity Insertion Method (liim) allows to mitigate the error from certain cnot gates, typically those with the largest error. Set Identity Insertion Method (siim) naturally interpolates between the measurement-efficient fiim and the gate-efficient riim allowing to trade off fewer cnot gates for more measurements. Finally, we investigate a way to boost the number of measurements, namely to run ZNE in parallel, utilizing as many quantum devices as are available. We explore the performance of riim in a parallel setting where there is a non-trivial spread in noise across sets of qubits within or across quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Gauging staggered fermion shift symmetries

Staggered fermion shift symmetries correspond to translations of the fermion field within the unit cell of a hypercubic lattice. They satisfy an algebra and in four Euclidean dimensions can be related to a discrete subgroup of an S U ( 4 ) flavor symmetry which plays a crucial role in showing that staggered fermions lead to a theory of four degenerate Dirac fermions in the continuum limit. They are associated with the appearance of certain Z 2 valued global parameters. We propose a strategy to try to partially gauge these translation symmetries by allowing these parameters to vary locally in the lattice. To maintain invariance of the action requires the addition of Z 2 valued higher form lattice gauge fields. An analogous procedure can be carried out for reduced staggered fermions where the shifts correspond to a discrete subgroup of an S O ( 4 ) flavor symmetry. Published by the American Physical Society 2024

Catterall, Simon (ORCID:0000000327352682)↗
DOE OSTI2023

Evaluation of Distributed Power Apportioning with Net Load Management Engine in Microgrids Using Power Hardware-in-the-Loop Simulation

This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.

hardware-in-the-loop simulation↗
DOE OSTI2020

Cost Estimation of a Permanent Magnet Synchronous Machine for Use in Adjustable Speed-Pumped Storage Hydropower

Electric machine design has become an easier process with the availability of computers in recent decades. However, the interest in electric machine design among students has declined. University-level design courses must compete with courses perceived as more desirable by the potential job market. Hence, the power system engineers with this expertise are very few and are nearing retirement. Contrarily, renewable energy has been picking up in the past few decades. The indeterminacy and variation of renewable energy sources require a balancing mechanism to maintain the constant voltage and frequency of the grid. Adjustable-Speed Pumped-Storage Hydropower plants are considered the contender for future energy storage, providing large capacity and fast-acting response. Large-scale hydropower applications depend on the natural characteristics of each project site, making less expensive, off-the-shelf generators unfeasible. This work gives a shortcut to estimate the cost of constructing a permanent magnet generator for low-volume manufacturing.

13 HYDRO ENERGY↗
DOE OSTI2020

Improvements to PVWatts for Fixed and One-Axis Tracking Systems

This work presents improvements to the widely used NREL PVWatts photovoltaic system energy model to improve modeling accuracy for typical fixed and one axis system designs. The aim is to calculate losses in the PV system assuming typical modern system design practices, while maintaining simplicity by keeping the required set of input parameters small. These improvements allow users to more credibly and quickly evaluate competing system designs in early stage feasibility. Common submodels for module cover, spectral, snow, tracker, transformer, plant controller, and self-shading losses, in addition to a bifacial gain option, are incorporated into the PVWatts model, and are shown to improve PVWatts' system performance prediction capabilities without major impact to ease of use. We anticipate including these improvements in a future release of NREL's open source PVWatts code, and some of the features may become available in the System Advisor Model (SAM) desktop software as well as the popular PVWatts web application.

14 SOLAR ENERGY↗
DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗
DOE OSTI2020

Zeolite Membrane Reactor for Pre-Combustion Carbon Dioxide Capture (Final Scientific/Technical Report)

Water-gas shift (WGS) reaction followed by carbon dioxide (CO 2 ) separation is a critical step in the integrated gasification combined cycle (IGCC) process for fossil-fuel-fired electrical power generation with CO 2 capture. To intensify the IGCC process hydrogen-permselective zeolite membrane reactor offers promise to replace the conventional energy-intensive fixed-bed reactors and solvent-based CO 2 capture units. The objectives of this project were to develop a bench-scale zeolite membrane reactor (total membrane area: 932 cm 2 for a 21-tube membrane bundle) for the water-gas-shift reaction of raw syngas from an oxygen-blown coal-gasifier for H 2 production with simultaneous CO 2 separation at the capability of about 2 kilograms H 2 per day (equivalent to 2 kW IGCC power plant) and to demonstrate significant progress toward achieving overall performance goal of 90% CO 2 capture rate with 95% CO 2 purity at the cost of electricity 30% less than the baseline carbon capture approaches. This report summarizes results obtained in this project on scaling up the zeolite membrane reactor by a factor of 200 in membrane area, tests of the bench-scale zeolite membrane reactor for the water-gas-shift reaction at high temperature and high-pressure, and techno-economic analysis of the integration of the zeolite membrane reactor in IGCC power plant for the electrical generation with CO 2 capture. With effective pore modification by catalytic cracking deposition of MDES (methyldiethoxysilane), fabrication of MFI-type zeolite membranes was successfully scaled-up from a lab-scale disk type to bench-scale multiple-tube bundles on low-cost alumina supports. A Co-Mo based sour shift catalyst was evaluated and used in the zeolite membrane reactors for water-gas-shift reaction. The reaction kinetic and gas-permeation equations were developed and employed in mathematic models to guide/predict experiments/performance of zeolite membrane reactors for water-gas shift reaction. Multiple-tube zeolite membrane bundles and reactors were designed, fabricated and tested for gas separation and water-gas-shift reactions with real raw syngas from a coal-fired gasifier operated at high temperature and pressure. The zeolite membrane reactors demonstrated good long-term thermal and chemical stability and constant H 2 permeance (>300 GPU) together with a Knudsen selectivity (~4.7) for H 2 over CO 2 in the field test (cumulative time >28 hours) with high-sulfur coal-derived syngas. The zeolite-membrane-reactor integrated IGCC process was designed using the performance experimentally measured by the University of Cincinnati team with a single-tube zeolite membrane reactor that offers a CO conversion >98% with more than 90% of CO 2 and H 2 captured. With the above integrative approaches, a techno-economic analysis for a cost-benefit comparison was performed to uncover features that determine the power output, capital expenditure, operating expenditure, cost of electricity and cost of CO 2 capture in a 550-MW zeolite-membrane-reactor integrated IGCC process. The integration of the zeolite membrane reactor in IGCC could provide a significant reduction of 80% and 27% in the power consumption for Selexol™ Acid Gas Removal and CO 2 compression, respectively, which lowers the total auxiliary power consumption by 12.5%. However, the low pressure required at permeate stream for maintaining the driving force of hydrogen permeation through the zeolite membrane costs a huge power in permeate compressor, compensating the power consumption reduction achieved with the membrane reactor. Thus, for coal-fired IGCC for electricity generation with 90% CO 2 captured, the integration of the membrane reactor could provide a CO conversion ~99% and a significant drop in cost-of-electricity using zeolite membrane with H 2 permeance >600 GPU and the H 2 /CO 2 selectivity over 70.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Comprehensive Characterization of Mixed Metal Oxide Catalysts for Enhanced Catalyst Lifetime During Bio-Based C2-C6 Oxygenates to Olefins Processes (CRADA CRD-18-00728 Final Report)

Participant has developed mixed metal oxide (MMO) catalysts to selectively convert biomass-derived C2-C6 oxygenates to olefins and then hydrocarbon fuels to meet DOE 2022 fuel targets. Selective conversion ethanol to isobutylene with MMO catalysts (ZnOZrOx) requires low ethanol feeds to maintain performance. Increasing the feed results in rapid catalyst deactivation. Participant has increased catalyst stability by adding additional metal oxides. However, variations in performance based on the level and type of additive and catalytic conditions have been observed. This project will use advanced catalyst characterization methods through the ChemCatBio Advanced Catalyst Synthesis and Characterization (ACSC) project to gain insight into key catalyst features and deactivation modes with the goal of tailoring catalyst composition to improve performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

VERA-Grizzly Ex-Core Calculations: Watts Bar Unit 1 Cycles 1-2

The critical structures that comprise light-water reactor (LWR) nuclear power plants are subjected to operating environments that can challenge their integrity. Structures in close proximity to the reactor core, such as the reactor pressure vessel (RPV) and the biological shield wall, are subjected to high levels of radiation emanating from the core, as well as elevated temperatures. As the US fleet of operating LWRs ages, the effects of these operating environments on the integrity of these structures must be considered to ensure their continued safe operation. Extending the lifetime of commercial reactors and maintaining the aging reactor fleet require accurate prediction of the exposure of ex-core components to neutron and photon radiation. In particular, concrete degradation studies must be performed to evaluate the safety and long-term operation of reactors with lifetime extensions. The concrete reactor bioshield is important for providing radiological protection during operation and must last for the entire lifetime of the reactor. Recent interest in lifetime extensions furthers the need to accurately simulate concrete material degradation in the reactor bioshield. As a result of this need, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has funded this study to couple its tools, Virtual Environment for Reactor Applications (VERA) and Grizzly. VERA allows users to set up models to calculate time-dependent and fully coupled solutions (with thermal feedback) for ex-core quantities of interest such as vessel and coupon fluence and detector responses for multiple statepoints and cycles. Grizzly is a finite-element application based on the Multiphysics Object Oriented Simulation Environment (MOOSE) framework that is used to enable aging materials calculations. This report highlights the work performed to calculate the fluence in the vessel and concrete for Watts Bar Nuclear Plant Unit 1 (WBN1) Cycles 1 and 2. The fluences obtained from VERA were successfully transferred to Grizzly using a Python script. Four simulations were run with Grizzly: (1) the Mazars model with the initial Young’s modulus being the instantaneous modulus, (2) the Mazars model with the initial Young’s modulus being the delayed modulus, (3) the Mazars model with the initial Young’s modulus being the delayed modulus with the addition of the effects of micro-damage caused by irradiation, and (4) the Mazars model with the initial Young’s modulus being the instantaneous modulus, and with the addition of micro-damage and creep. Details regarding the methods used to obtain the fluence and the statistical errors associated with the VERA Monte Carlo Shift calculations are discussed in greater detail in this report. The results obtained from the four Grizzly models are also presented in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

DEReliction: A Cybersecurity Vulnerability Assessment Methodology for Distributed Energy Resources

With the increasing integration of Distributed Energy Resources (DER) into the electric grid, maintaining grid reliability and resilience requires that these devices remain secure. This paper discusses a cybersecurity vulnerability assessment methodology that incorporates best practices from Sandia National Laboratories, SANS Institute, OWASP Foundation, and other web and Internet of Things (IoT) penetration testing (“pen testing”) programs, courses, and frameworks for assessing the security posture of devices. The methodology involves five sequential steps: (1) Collect Public Information, (2) Extract Hardware Details, (3) Inventory Software Components, (4) Identify Vulnerabilities, and (5) Test Vulnerabilities. Each step uncovers potential weaknesses in both hardware and software components of DER devices, considering adversary tactics, techniques, and procedures (TTPs), and potential attack vectors along the way. The results from the execution of this method on multiple residential- and small commercial-scale photovoltaic (PV) inverters reveled hardware and software vulnerabilities, which highlight the benefit of taking a methodical approach to discover vulnerabilities. While the specific vulnerability details are not shared here, a generalized overview of findings underscore the importance of robust security assessments for DER devices. Adoption of an assessment framework of this kind will identify and mitigate cybersecurity threats and bolster the resilience of DER-integrated electric grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗