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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Multistage Stochastic Transmission Expansion Algorithm for Wide-Area Planning under Uncertainty

The overall objective for this project was to develop and demonstrate a set of methods for solving the transmission investment problem for a large network considering many possible scenarios of future conditions and multiple decision points when investments can be made. Project sub-objectives achieved this goal through a succession of extending the methods to apply to problems with increasing complexity or additional features, including the number of decision points, whether generation and transmission are co-optimized, and whether AC or DC power flow is used. A transmission model was developed for the Western Electric Coordinating Council (WECC) region, the high-voltage transmission system that serves the western third of the continental U.S. Using a dataset provided by WECC and by researchers from John Hopkins University, we have validated and demonstrated the model and used it to compare the new method for solving multi-stage stochastic transmission planning to several state-of-the-art techniques. The project has resulted in several key outcomes and achievements: The covariance-based method for choosing a small set of hours to represent short-term variability has superior performance in terms of accuracy to existing methods, including K-means clustering and Importance Sampling; The combined partitioning method for long-term uncertainty with the nested clustering approach for choosing representative hours for each long-term group has superior accuracy for equivalent computational effort compared with existing methods; Using the partitioning/clustering method combined with Sample Average Approximation provides both statistical bounds on the quality of the solution and at the same time, a complete investment plan for all contingencies in the full uncertainty set; no existing methods can provide both at the same time; The method is demonstrated to work well for choosing both transmission and generation investments; A variant on the method allows for both scenario selection and simultaneous correction for the error from the DC power flow approximation to provide a tractable method for AC power flow-based transmission planning under uncertainty; The method applied to the WECC case study demonstrates the additional value to the system operator and the consumer of identifying flexible investment options in the near-term decisions. In particular, the case study exhibits significant option value in postponing some transmission additions that appear useful but in some long-term system states create new congestion problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Distribution Grid Impact Study in Highland Park, Michigan: Understanding Rooftop Solar, Behind-the-Meter Energy Storage, Electric Vehicle Charging, and Building Electrification [Slides]

Through the Communities LEAP (Local Energy Action Program) Pilot, the National Renewable Energy Laboratory (NREL) engaged the Highland Park Stakeholder Coalition to scope four technical assistance work areas to address their energy needs and goals. This slide deck addresses the highlighted tasks under work area "B" related to policy analysis, due diligence, and case studies for removing barriers and providing best practices for local clean energy development. Highland Park community members face frequent, long-duration power interruptions due largely to the aging distribution system serving the area and the legacy design standards used in its construction. While degrading physical infrastructure such as poles, crossarms, and transformers can result in this substandard reliability, another notable characteristic of this legacy system is the lower, 4.8 kV, voltage class. This is a legacy construction standard which many utilities, DTE included, are phasing out in favor of higher, 15 or 25 kV, voltage classes instead. The existing 4.8 kV distribution system serving Highland Park may limit significant adoptions of clean-energy technologies like high percentages of building electrification or electric vehicle adoption. The following analysis seeks to quantify these limitations under a variety of clean-energy technology adoption scenarios. It compares the overall system risks of the present system to those of a hypothetical, upgraded 13.2 kV system, using NREL-developed risk metrics and offers upgrade cost considerations. The legacy 4.8 kV voltage class serving Highland park is not, as we have modeled it, a major limitation to the widespread adoption of cost-optimal rooftop solar and/or behind-the-meter energy storage. Within our modeling framework, these technologies namely impact secondary, low-voltage assets, which may be remedied without the need for a system-wide upgrade to a 13.2 kV voltage class. Our model of the current 4.8kV system indicates it is not capable of supporting community-wide electrification efforts. Widespread building electrification, and the resulting large increase in wintertime load, dramatically increases the prevalence of voltage violations and thermal overloading on the current 4.8 kV distribution system. These impacts illustrate the need for system-wide upgrades to a 13.2 kV voltage class to accommodate these technologies. Low to Moderate DER adoption does not adversely impact the grid but does improve undervoltage and asset overloading issues. However, these benefits are insufficient to defer grid upgrades. Higher DER penetration is shown to increase overvoltage and asset overloading in future electrification scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2020

Modeling Flexible Generator Operating Regions via Chance- constrained Stochastic Unit Commitment

Here, we introduce a novel chance-constrained stochastic unit commitment model to address uncertainty in renewables' production uncertainty in power systems operation. For most thermal generators,underlying technical constraints that are universally treated as "hard" by deterministic unit commitment models are in fact based on engineering judgments, such that system operators can periodically request operation outside these limits in non-nominal situations, e.g., to ensure reliability. We incorporate this practical consideration into a chance-constrained stochastic unit commitment model, specifically by in-frequently allowing minor deviations from the minimum and maximum thermal generator power output levels. We demonstrate that an extensive form of our model is computationally tractable for medium-sized power systems given modest numbers of scenarios for renewables' production. We show that the model is able to potentially save significant annual production costs by allowing infrequent and controlled violation of the traditionally hard bounds imposed on thermal generator production limits. Finally, we conduct a sensitivity analysis of optimal solutions to our model under two restricted regimes and observe similar qualitative results.

42 ENGINEERING↗
DOE OSTI2022

Techno-Economic Analysis of Recuperated Joule-Brayton Pumped Thermal Energy Storage

This article describes a techno-economic model for pumped thermal energy storage systems based on recuperated Joule-Brayton cycles and two-tank liquid storage. Models have been developed for each component, with particular emphasis on the heat exchangers. Economic metrics such as the power and energy capital costs (i.e., per-kW and per-kWh capacity) and levelized cost of storage are evaluated by gathering numerous cost correlations from the literature, thereby enabling estimates of uncertainty. It is found that the use of heat exchangers with effectivenesses up to 0.95 is economically worthwhile, but higher values lead to rapidly escalating component size and system cost. Several hot storage fluids are considered; those operating at the highest temperatures (chloride salts) improve the round-trip efficiency but the benefit is marginal and may not warrant the additional material costs and risk when compared to lower-temperature nitrate salts. Cost-efficiency trade-offs are explored using a multi-objective optimization algorithm, yielding optimal designs with round-trip efficiencies in the range 59-72% and corresponding levelized storage costs of 0.12 0.03 and 0.38 0.10 $/kWhe. Lifetime costs are competitive with lithium-ion batteries for discharging durations greater than 6 h under current scenarios.

Carnot battery↗
DOE OSTI2024

A country-scale green energy-water-hydrogen nexus: Jordan as a case study

Many developing countries suffer from a shortage of clean energy-water production, causing significant dependency on imported fossil fuel to meet the local energy and freshwater demands. Furthermore, when planning a 100 % renewable energy system (RES) to match the energy demand on an hourly basis, the RES is usually oversized. Hence, unavoidable large amounts of excess energy would be generated during hours of high production, low demand, and fully charged energy storage systems (ESSs). Hence, using Jordan as a case study, this work proposes a novel integrated system of wind, solar photovoltaic (PV), and lithium-ion ESS to match 100 % of the country's energy demand while using the excess generated power to drive reverse osmosis water desalination plants to match the demand of freshwater as well. Furthermore, the remaining excess energy is used for producing green hydrogen. A techno-economic optimizer was developed to show that this concept is indeed feasible. Here, the model was used to scan Jordan for the fitting sites of the highest demand-supply matching and the lowest levelized cost of electricity (LCOE), simultaneously. Three different integration scenarios are presented, namely PV-ESS, wind-ESS, and hybrid PV-wind-ESS RESs. The hybrid system showed the best performance, especially compared to the wind-ESS RES, in terms of total installed capacity (33.37 GW), LCOE (0.0492 USD/kWh), specific water cost (0.3629 USD/m 3 ), and energy and water demand-supply fractions (99.47 and 96.16 %, respectively). Storing desalinated water in natural aquifers was found to be a favorable option to enhance the performance of the three systems, where stored water by the end of the year was sufficient to cover the freshwater requirements for several following years. Finally, the remaining excess energy is used to produce green hydrogen with an annual production of up to 1.37 million tons and a specific cost down to 1.08 USD/kg.

14 SOLAR ENERGY↗
DOE OSTI2025

A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning

Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a major challenge. We present MODES (Multi-mOdal Disentangled Embedding Space), a representation fusion framework that explicitly separates shared and modality-specific factors of variation, offering a structured latent space for multimodal information that improves both prediction and interpretability. By leveraging pre-trained unimodal foundation models, MODES mitigates the dependency on extensive paired datasets, crucial in data-scarce clinical settings. We introduce a masking strategy that optimizes representation dimensionality by eliminating low-information dimensions, to achieve compact, information-rich representations. Our framework demonstrates superior performance in predicting diagnoses and phenotypes compared to unimodal and conventional fusion models. MODES also enables robust diagnostic inference in missing data scenarios, offering an opportunity toward interpretable and efficient multimodal diagnostics in personalized healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Concrete Thermal Energy Storage Enabling Flexible Operation without Coal Plant Cycling

The work described in this report is responsive to the Office of Fossil Energy program “Energy Storage for Fossil Power Generation.” The pilot plant built as a result of this project demonstrated the feasibility and performance of a concrete thermal energy storage (CTES) system integrated with a supercritical coal power plant. The 10 MWh electrical (>25 MWh thermal) CTES unit, developed by Storworks Power, was designed to enable flexible operation of coal plants without cycling damage. The project's key technical achievements showcase a significant advancement in energy storage technology. A modular CTES system using 42 “Bolderblocs” units was successfully designed and constructed at Alabama Power’s Plant Gaston Unit 5, with each block containing embedded stainless-steel coils in specialized, cost-effective high-temperature concrete. The system interfaced seamlessly with the plant's 3500 psig (241 barg), 1000°F (538°C) supercritical steam, demonstrating operational flexibility. Over 86 full cycles, the CTES exhibited rapid charging and discharging capabilities, effectively mimicking steam turbine feed conditions and handling varying load profiles and storage durations. Performance validation confirmed the system's ability to consistently meet design target steam conditions of 75 bar-a and ~400°C for nominal baseline discharge. The concrete material withstood repeated thermal cycling without degradation, validating earlier lab-scale tests. Integration of balance of plant components, including a condensate management system with storage tank and air-cooled condenser, minimized plant interfaces and water consumption. A robust control scheme ensured safe, automated operation across various scenarios. Key learnings from the project were invaluable: 1. Initial concrete drying and commissioning procedures were refined for future deployments, enhancing efficiency in subsequent installations. 2. System flexibility exceeded expectations, with rapid response to changing conditions. 3. Design improvements were identified including optimized insulation and piping that will enhance overall system efficiency in future deployments 4. Full cycle thermal roundtrip efficiencies exceeded 88%. While the roundtrip electrical efficiency was somewhat limited by known challenges using input steam, such constraints may be mitigated by swapping steam for hot air as thermal input. 5. A summary of key performance parameters for the pilot test and predicted performance of a full scale commercial system with specified improvements determined from the pilot are shown in Section 8. The project faced challenges, including COVID-19 delays and host plant availability constraints. However, these were overcome through adaptive planning and execution. The successful management of these obstacles demonstrated the resilience and adaptability of the project team and the robustness of the CTES technology. This successful pilot demonstrates the potential for CTES to enhance coal plant flexibility, supporting grid stability as renewable penetration increases. The validated design and operational data provide a solid foundation for scaling up to utility-scale implementations, potentially transforming how thermal plants operate in evolving energy landscapes. The system's ability to rapidly respond to changing grid conditions while maintaining high efficiency makes it a promising solution for balancing intermittent renewable energy sources. Furthermore, the project highlighted the potential for even greater efficiencies in future iterations. The use of air as an input medium could potentially eliminate the limitations observed with steam input, opening new possibilities for energy storage applications beyond coal plant integration. In conclusion, this pilot project not only achieved its primary goals but also uncovered additional benefits and potential applications of the CTES technology. It represents a significant step forward in addressing the challenges of grid stability and flexibility in an increasingly renewable-driven energy landscape.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

State-of-the-Art Techniques for Large-Scale Stochastic Unit Commitment

Recent advances in deterministic unit commitment, both formulaic and algorithmic, along with modern algorithmic approaches for stochastic programming, have enabled the solution of stochastic unit commitment problems with hundreds of scenarios on large-scale transmission networks. In this presentation, we will give an overview of these methods, including lazy transmission constraint generation, lower-bounding techniques, and heuristics, all of which can be executed in concert with customized decomposition approaches for optimization under uncertainty. We demonstrate the effectiveness of these techniques on the TAMU Texas7K synthetic transmission network, leveraging realistic high-resolution forecasts based on NREL renewable resource availability data. The software leveraged for these demonstrations is available via the open-source software packages EGRET (for electrical grid optimization) and mpi-sppy (for optimization under uncertainty).

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Feasible region-based heuristics for optimal transmission switching

In this paper, we develop a optimal transmission switching (OTS) heuristic based on DC optimal power flow (OPF) and assess the efficacy of the approach when implemented within AC OPF. Traditional formulations of the OTS problem can result in hundreds or thousands of binary variables for large networks, making the OTS problem challenging to solve on fast timescales even for relatively small networks. Here, we identify which constraints and therefore which variables are constraining the DC OPF feasible region, and rank them based on their impact on the cost function. We develop a heuristic algorithm which iteratively removes these constraints and solves a series of standard DC OPF problems. The heuristic is tested on a variety of PGlib networks and the results show that the algorithm can provide substantial cost decreases without having to solve any mixed integer programs. Additionally, we provide insights about the OTS problem, including identifying scenarios outside congestion where OTS can prove useful. Lastly, the performance of the DC-based heuristic is shown when the line switching decisions are implemented within AC OPF.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Investigating capacity credit sensitivity to reliability metrics and computational methodologies

Assigning capacity value to renewable energy sources (RES) is a challenge faced in planning their integration with the grid. The difficulties stem from the natural characteristics of variability and intermittency of wind and solar sources. The capacity credit (CC) analysis evaluates the system’s actual power output compared with a constant capacity generator, i.e., conventional generator and determines an effective capacity to use for planning and operation. Herein this paper presents different factors that could affect the CC of a system. Two methods are proposed to determine the CC, namely equivalent firm capacity (EFC) and effective load carrying capability (ELCC). Since these methods are based on satisfying reliability criteria, daily loss of load expectation (LOLE), hourly loss of load (LOLH), and expected energy not served (EENS) have been employed as indices. To obtain the CC value, both methods apply two techniques: traditional and optimization. Genetic algorithm (GA) is the optimization approach used in this paper. Then, this work compares the two techniques and shows the superior performance of the optimization approach. Two hybrid systems, stand-alone (SA) and grid-connected (GC) modes, are proposed and used as case studies. The hybrid systems consist of photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS). In this work, three different scenarios are used to compare capacity credit: system as a whole, only wind, and no batteries. Finally, sensitivity analysis is carried out to examine the impact of varying the wind speed, solar irradiation, and load. It is demonstrated that the choice of reliability index plays an important role in determining the capacity credit and it is shown that EENS is a more comprehensive and consistent index of reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Li-ion battery design through microstructural optimization using generative AI

Lithium-ion batteries are used across various applications, necessitating tailored cell designs to enhance performance. Optimizing electrode manufacturing parameters is a key route to achieving this, as these parameters directly influence the microstructure and performance of the cells. However, linking process parameters to performance is complex, and experimental or modeling campaigns are often slow and expensive. This study introduces a fast computational optimization framework for electrode manufacturing parameters. A generative model, trained on a small dataset of microstructural images associated with different manufacturing parameters, efficiently generates representative microstructures for new parameters. This model is integrated into a Bayesian optimization loop that includes microstructure generation, characterization, and simulation, aiming to find optimal manufacturing parameters for a particular application. Significant improvement in the energy density of a 4680 cell is achieved through bespoke cell design, highlighting the importance of cell-scale normalization. The framework’s modularity allows its application to various advanced materials manufacturing scenarios.

batteries↗
DOE OSTI2024

Assessment of the polygeneration approach in wastewater treatment plants for enhanced energy efficiency and green hydrogen/ammonia production

Wastewater treatment plants (WWTPs) offer opportunities to optimize resource utilization and enhance energy efficiency. Here, this study provides a comprehensive analysis of using the polygeneration approach in WWTPs to reduce grid energy dependence, optimize energy distribution, and utilize surplus energy for hydrogen (H 2 ) and ammonia (NH 3 ) production. Several models were employed, including photovoltaic (PV) cells, parabolic trough collectors (PTCs), steam methane reforming, and polymer electrolyte membranes, to assess the feasibility of this approach. Three scenarios were evaluated and compared: Scenario 1 (Baseline) represents the current situation, Scenario 2 maximizes the Net Present Value (NPV), and Scenario 3 minimizes NH 3 production costs. Real data from As-Samra WWTP in Jordan was used to accurately assess the feasibility of each scenario. The results show that Scenario 2 offers the highest profitability and efficiency, with a NPV of 87.48 million USD and an annual NH 3 production of 15,417 tons, reducing both grid dependency and biogas fuel consumption. Both Scenarios 2 and 3 demonstrate the ability to meet thermal demands efficiently while generating significant revenue from NH 3 production. Scenario 3, in particular, achieves competitive H 2 and NH 3 production costs. Environmentally, Scenario 2 significantly reduces annual greenhouse gas emissions by 12.66 kilotons of CO 2eq , with near-zero carbon intensity for thermal energy due to solar reliance. In conclusion, the polygeneration approach offers a promising pathway for WWTPs to achieve greater sustainability, economic gains, and reduced environmental impact, providing valuable insights for decision-makers.

42 ENGINEERING↗
DOE OSTI2024

Comprehensive AI-based System for Control, Sensor Estimation, and Fault Detection of Cascaded Multilevel Inverters

In this paper, an Artificial Intelligence-based (AI) system is proposed for an 11-level cascaded H-bridge multilevel inverter (MLI) with the aims of harmonic suppression and reliability enhancement. The system consists of three seamlessly integrated Neural Networks (NNs). First, a multilayer perceptron is used to generalize the optimal switching angles for selective harmonic elimination under non-equal DC voltages. Next, an autoencoder NN estimates the voltage sensor readings to address potential drifting. Finally, a perceptron NN detects inverter faults based solely on the output voltage of the MLI. Simulation scenarios were evaluated, and the results show that the proposed system provides a comprehensive solution for the robust operation of the MLI. The proposed solution is capable of minimizing the targeted harmonics orders with minimal impact on the fundamental voltage, even when the voltage sensor drifts. Furthermore, the inverter under fault conditions was successfully identified.

Rezende da Costa Reis Kimpara, Renata↗
DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

DOE-NE LWRS Integrated Program Plan - Physical Security Pathway

Domestic nuclear power is facing increased financial pressures from a variety of areas and there is pressure on these utilities to reduce their cost of operation. Currently, about 20%-30% of all on-site personnel are related to physical security. The LWRS Program recognized that R&D related to physical security could play a role in providing nuclear utilities technical and staffing efficiency options to meet their physical security commitments, but utilities often lack the technical basis or the ability to create the technical basis to realize or implement these efficiencies; towards this end, the LWRS Program created the Physical Security Pathway in September 2019. The pathway performs R&D to develop methods, tools, and technologies to optimize and modernize a nuclear power facility’s security posture. The pathway will: (1) conduct research on risk-informed techniques for physical security that account for a dynamic adversary; (2) apply advanced modeling and simulation tools to better inform physical-security scenarios and reduce uncertainties in force-on-force modeling; (3) assess benefits from proposed enhancements and novel mitigation strategies and explore changes to best practices, guides, or regulation to enable modernization; and (4) enhance and provide the technical basis for stakeholders to employ new security methods, tools, and technologies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗