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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls

Here, this paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.

Frequency control↗
DOE OSTI2024

Coherency-Aware Learning Control of Inverter-Dominated Grids: A Distributed Risk-Constrained Approach

Here, this letter investigates the importance of integrating the coherency knowledge for designing controllers to dampen sustained oscillations in wide-area power networks with significant penetration of inverter-interfaced resources. Coherency is a fundamental property of power systems, where time-scale separation in frequency dynamics leads to clustered behavior among generators of different groups. Large-scale penetration of inverter-driven low inertia resources replacing conventional synchronous generators (SGs) can lead to perturbation in the coherent partitioning; hence, integrating such information is of utmost importance for oscillation control designs. We present the coherency-aware design of a distributed output feedback-based reinforcement learning method that additionally incorporates risk constraints to capture the uncertainties related to net-load fluctuations. The use of domain-aware coherency information has produced improved training and oscillation performance than the coherency-agnostic control design, hence proving to be effective in controller design. Finally, we validated the proposed method with numerical experiments on the benchmark IEEE 68-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.

chance constraints↗
DOE OSTI2023

Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control

Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to decentralized paradigm, LFC needs to consider the decentralized scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing the convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.

Kwon, Kyung-bin↗
DOE OSTI2026

Guideline for Characterizing and Evaluating a Candidate Project Site for Solar Thermal Applications

This document presents a structured procedure for characterizing and evaluating candidate project sites for concentrating solar power (CSP) and solar heat for industrial processes (SHIP) applications. The objective is to provide project developers, researchers, and other stakeholders with a consistent, technology-agnostic framework for early-stage site assessment, enabling informed decision-making prior to significant investment in project development. Site selection is a critical factor in project success or failure for both CSP and SHIP projects. Key factors such as solar resource availability, land characteristics, environmental and regulatory constraints, infrastructure availability, and community context are determined by the choice of project site and can materially impact project performance, cost, schedule, and overall viability. This procedure is designed to systematically evaluate these factors, identify potential fatal flaws, and prioritize the most favorable candidate sites for further development. The process begins with rapid screening-level evaluation, using publicly available data to assess solar resource, land availability and suitability, zoning and land-use compatibility, and exclusion zones such as protected lands or sensitive habitats. Sites that meet the minimum screening criteria advance to a more detailed characterization. Subsequent sections of this report provide guidance for a next-level assessment of the most important technical and environmental parameters, including: 1) Solar resource quality, variability, and uncertainty using multiyear datasets and, where appropriate, on-site measurement campaigns; 2) Meteorological conditions such as wind, temperature, extreme weather events, and soiling impacts; 3) Land characteristics including slope, shading, and geotechnical conditions; and 4) Environmental and regulatory considerations, including permitting processes, endangered species, cultural resources, and visual impacts. The procedure also addresses infrastructure and integration considerations, including: 1) Grid interconnection requirements for CSP power generation projects; 2) Electrical and operational integration for SHIP facilities; 3) Water availability, quality, and permitting constraints, which are particularly critical for CSP in arid regions; and 4) Site access, construction logistics, and availability of workforce and supporting services. Recognizing the importance of social and economic context, the procedure includes evaluation of community engagement factors, such as stakeholder sentiment, proximity to sensitive visual receptors, workforce development opportunities, and local economic incentives. The outputs of these assessments are synthesized in a cost and risk evaluation, translating site characteristics into expected impacts on capital cost, operating cost, schedule, and technical risk. This is complemented by screening-level performance modeling, including 8760 simulations and long-term projections, to quantify expected energy or thermal output, assess variability thereof, and support comparison between candidate sites. Finally, the procedure provides high-level guidance on a structured go/no-go decision framework, categorizing sites based on identified risks and constraints, and outlining a clear path forward to feasibility studies and front-end engineering design for viable projects. By standardizing the site characterization process across both CSP and SHIP applications, this guideline aims to: 1) Improve consistency and transparency in early-stage project evaluation; 2) Reduce development risk and avoid investment in nonviable project sites; 3) Support collaboration between developers, researchers, and public agencies; and 4) Accelerate successful deployment of concentrating solar technologies for both power generation and industrial process heat.

14 SOLAR ENERGY↗
DOE OSTI2021

A unifying modeling abstraction for infinite-dimensional optimization

Infinite-dimensional optimization (InfiniteOpt) problems involve modeling components (variables, objectives, and constraints) that are functions defined over infinite-dimensional domains. Examples include continuous-time dynamic optimization (time is an infinite domain and components are functions of time), PDE optimization problems (space and time are infinite domains and components are functions of space-time), as well as stochastic and semi-infinite optimization (random space is an infinite domain and components are a function of such random space). InfiniteOpt problems also arise from combinations of these problem classes (e.g., stochastic PDE optimization). Given the infinite-dimensional nature of objectives and constraints, one often needs to define appropriate quantities (measures) to properly pose the problem. Moreover, InfiniteOpt problems often need to be transformed into a finite dimensional representation so that they can be solved numerically. Here in this work, we present a unifying abstraction that facilitates the modeling, analysis, and solution of InfiniteOpt problems. The proposed abstraction enables a general treatment of infinite-dimensional domains and provides a measure-centric paradigm to handle associated variables, objectives, and constraints. This abstraction allows us to transfer techniques across disciplines and with this identify new, interesting, and useful modeling paradigms (e.g., event constraints and risk measures defined over time domains). Our abstraction serves as the backbone of an intuitive Julia-based modeling package that we call InfiniteOpt.jl. We demonstrate the developments using diverse case studies arising in engineering.

42 ENGINEERING↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2022

Data-Driven Chance-Constrained Design of Voltage Droop Control for Distribution Networks: Preprint

This paper addresses the design of local control methods for voltage control in distribution networks with high level of distributed energy resources (DERs). The designed control methods adapt the active and reactive power output of distributed energy resources proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control. Thus, the design focuses on determining the droop characteristics which satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging. Hence, this paper proposes chance constraints to limit the risk from intermittent DERs, by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder.

chance-constrained optimization↗
DOE OSTI2023

Challenges in Demonstrating Safety Criteria Satisfaction for External Events under a Risk-Informed Performance-Based Framework

The NRC is currently developing 10 CFR Part 53 that would establish a technology-inclusive regulatory framework for use by applicants for new commercial advanced nuclear reactors. The new draft rule language brings new explicit regulatory requirements of the quantitative health objectives (QHOs) as a constraint on plant risk and safety criteria for the plant design, including external hazards. Historically, light water reactors (LWRs) have demonstrated satisfaction of the QHOs through the use of surrogates in the form of CDF/LERF. However, these risk surrogates are not broadly applicable for all advanced reactor types, and for advanced reactors where external events are likely to be the dominant contributor to plant risk, a direct calculation of the QHOs in terms of offsite consequence is likely necessary. While the detailed analysis of low frequency events provides insights that can help justify alternative treatments to past conservatisms, the findings are dependent on the quality and confidence associated with the analyses. The assessment of external hazards presents a unique challenge, as their potential frequency of occurrence, especially of largemagnitude events, is inherently uncertain given the long return periods in question. This paper evaluates the QHOs and their potential role as safety criteria in evaluating external event risk as it applies to advanced reactors to aid in the development of consistent and appropriate analysis methodologies.

Chen, Ben↗
DOE OSTI2026

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Minimizing Ground Risk in Cellular-Connected Drone Corridors With mmWave Links

Unmanned Aircraft Systems (UASs) have been receiving significant interest and support from academia, industry, and regulatory bodies over the past decade due to their various use cases. To safely integrate UAS operations into the national airspace, particularly overpopulated regions, the risk posed to ground users, buildings, and vehicles due to unmanned aerial vehicle (UAV) flight should be minimized. This risk can be represented by a numerical metric, which we refer to in this article as the “ground risk.” Many UAS applications also depend on the presence of a reliable wireless communication link between the UAV and a control station for the transmission of UAV position, surveillance video, UAV payload commands, and other mission-related data. Such wireless communication requirements also need to be considered in the design of UAS operations. In this article, we consider both these aspects and study the design of nonintersecting trajectories for UAS operations to minimize ground risk, subject to constraints on the wireless signal strength and geometry of the trajectory, specified in terms of: 1) an enclosing cylinder within which the trajectory must lie and 2) an integrated angular change along the UAV's trajectory. The performance of a computationally expensive optimal algorithm is compared with that of a computationally faster heuristic approach within the dense urban environment of Manhattan, NY, USA. Performance evaluation using ray-tracing simulations shows that the heuristic approach performs close to the optimal algorithm at a reduced computation cost. In conclusion, this research can be utilized to make UAS operations safe and reliable and accelerate their adoption.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Design Status and Experimental Strategy for Initial Molten Salt Irradiation Experiments in HFIR

The development and deployment of molten salt reactor (MSR) technologies require experimental capabilities that can evaluate molten fuel salt behavior and structural material performance under representative irradiation conditions. Although modeling and separate effects testing provide important insight, there remains a critical lack of in-pile data that capture the coupled effects of neutron irradiation, temperature, salt chemistry, and time. Informed by lessons learned from historical MSR programs and recent international irradiation efforts, this report presents a structured approach to addressing existing gaps in molten salt irradiation testing using the High Flux Isotope Reactor (HFIR). A phased irradiation strategy is presented that incrementally increases experimental complexity while managing cost, risk, and facility constraints. The framework progresses from passive, nonfueled static capsule experiments to fueled, instrumented, and ultimately circulating molten salt systems, providing a pathway for materials screening, mechanistic understanding, and qualification-relevant testing. The report defines a near-term Phase 1 passive capsule concept, associated irradiation conditions, and a conceptual post-irradiation examination strategy. Ongoing modeling, out-of-pile testing, and safety analysis activities are described to support continued capability maturation and to enable future phases of molten salt irradiation testing in support of MSR technology deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities: Preprint

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources downstream of a regulated distribution system operator's (DSO) net energy metering revenue meter. Due to the elevated risk of grid constraint violations and to ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, the proposed community market mechanism maximizes the community's social welfare in a decentralized fashion while every community member abides by its own operating envelopes. We show that the proposed market mechanism conforms with the cost-causation principle and guarantees community members a surplus level no less than their maximum surplus when they autonomously face the DSO. Lastly, a numerical study is implemented to showcase and compare the community's welfare under the proposed operating-envelopes-aware mechanisms to others, including the welfare of customers under the DSO's regime.

distributed energy resources aggregation↗
DOE OSTI2024

Enhancing Nuclear and Radiological Security in Polarized Times: Safeguarding Against Extremist Insider Threats

Domestic violent extremism has been on the rise in recent years, fueled by growing political polarization, the COVID-19 pandemic, and the spread of misinformation online. This concerning trend raises an important question: has this increase in extremist violence manifested in more incidents involving radioactive or nuclear (RN) materials and a corresponding security concern? To explore this question, we analyzed existing data on events involving chemical, biological, radiological, or nuclear materials over time. Specifically, we examined the rate of RN-related incidents, the targets of these events, and the ideologies motivating the perpetrators by using the Violent Non-State Actor Chemical, Biological, Radiological, and Nuclear Event Database from the National Consortium for the Study of Terrorism and Responses to Terrorism. Our analysis aimed to determine whether the broader rise of violent extremism has translated to an elevated risk of attacks involving RN materials. Results of the analysis revealed that there was no clear increase in events involving RN material in the dataset. However, given the rarity of these events, this conclusion does not indicate that such a trend might not appear in coming years if violent extremism continues to rise. To address the potential risk presented by violent extremism, in addition to quantifying the frequency of RN events, we also reviewed traits that could make organizations more susceptible to insider threats from radicalized employees. Focusing on the literature around counterproductive workplace behavior, we identified organizational characteristics that might increase the risk of malicious acts committed by radicalized insiders. These risk factors include abusive leadership, situational constraints, and organizational injustice. Finally, using these organizational risk characteristics, we discuss interventions that organizations with RN material can take to reduce their risk of attacks stemming from violent extremism while still protecting individual rights to privacy and civil liberties. Overall, this article provides an overview of the intersections between rising domestic extremism, insider threats, and risks posed by RN materials. We present current data on trends and patterns in this space as well as practical guidance for organizational resilience.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Quantitative Risk Assessment for Fuel Cell Electric Bus Hydrogen Storage and Refueling Facility

It is necessary to understand the safety implications and risk mitigation options for fuel cell electric bus fleet deployment, especially for related facilities responsible for operations such as production, storage, compression, and dispensing of hydrogen for use by the buses. In this report, we present a quantitative risk assessment for a potential fuel cell electric bus fleet that was motivated by efforts to improve resilience at the Portland International Airport but can be applicable to a range of hydrogen case studies and use cases. We estimated risk for a facility that produces, stores, compresses, and dispenses hydrogen for the fleet of buses, with a focus on individual risk to people in terms of annual frequency of fatality. We considered the frequency of hydrogen leaks that could result in harmful physical outcomes like jet fires or explosions, and the consequences of those outcomes for people. We created customized fault trees to calculate the frequencies of different sizes of leaks and event sequence diagrams to calculate ignition probabilities for the various leak sizes. We also leveraged the HyRAM+ toolkit to use these inputs to calculate overall risk for the facility, which we separated into one section responsible for producing, storing, and compressing hydrogen, and one section responsible for dispensing the hydrogen to the buses. We found that the dispensing area seemed to have a higher risk than the production/storage/compression area of the facility, largely because of the inclusion of a component with a high leak frequency (the heat exchanger used to cool the hydrogen before entering the vehicle, to prevent overheating and expansion of hydrogen in the onboard tank). For the example production and refueling facility we evaluated and the data we used for the analysis, the leak frequency had a larger impact on the risk differences between the two sections on the facility, compared to the physical outcome consequence, which was slightly different due to the varying fuel conditions, but not substantially different. Actions can be taken to prevent these hazards (e.g., lowering leak frequencies in system components) or to mitigate the consequences if they do occur (e.g., installing barriers to protect people if ignition events occur). The choice of which actions to take depends not only on safety considerations but also on space, time, staffing, feasibility, and financial constraints. Therefore, the quantitative risk assessment approach can help understand relative risk contributions from different components, leak sizes, consequences, and human actions, to prioritize risk reduction strategies and balance these parameters. The outcomes of this report may be useful for a variety of stakeholders working in the hydrogen, transportation, vehicle, and aviation sector, including those responsible for aspects like facility design, operations, and regulations. There is not a single value of risk that determines whether a hypothetical system is “safe” or not. The insights about risk mitigations may be leveraged, and the quantitative risk assessment approach can be applied to other case studies to understand risk priorities and contributions specific to different FCEB and hydrogen facility uses.

08 HYDROGEN↗
DOE OSTI2021

A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method

Abstract Bayesian optimization (BO) is a low-cost global optimization tool for expensive black-box objective functions, where we learn from prior evaluated designs, update a posterior surrogate Gaussian process model, and select new designs for future evaluation using an acquisition function. This research focuses upon developing a BO model with multiple black-box objective functions. In the standard multi-objective (MO) optimization problem, the weighted Tchebycheff method is efficiently used to find both convex and non-convex Pareto frontiers. This approach requires knowledge of utopia values before we start optimization. However, in the BO framework, since the functions are expensive to evaluate, it is very expensive to obtain the utopia values as a prior knowledge. Therefore, in this paper, we develop a MO-BO framework where we calibrate with multiple linear regression (MLR) models to estimate the utopia value for each objective as a function of design input variables; the models are updated iteratively with sampled training data from the proposed MO-BO. These iteratively estimated mean utopia values are used to formulate the weighted Tchebycheff MO acquisition function. The proposed approach is implemented in two numerical test examples and one engineering design problem of optimizing thin tube geometries under constant loading of temperature and pressure, with minimizing the risk of creep-fatigue failure and design cost, along with risk-based and manufacturing constraints. Finally, the model accuracy with frequentist, Bayesian and without MLR-based calibration are compared to true Pareto solutions.

Engineering↗