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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 OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Enabling Grid-Forming Control with Fault Ride-Through in Unbalanced Distribution Networks

Distribution networks are often unbalanced, causing oscillatory responses in inverter control designed for balanced conditions. Here, to address this problem, this paper proposes a novel time-domain transformation appropriate for inverter control and enables the decomposition of three-phase unbalanced signals into constant positive and negative components. Relations useful for calculating unbalanced active and reactive power are derived from first principle, providing insight into vector products of unbalanced three-phase signals. Furthermore, a grid-forming control effective under unbalanced conditions is developed, which delivers superior performance while meeting UNIFI1 specifications for grid-forming control under unbalanced conditions. specifications applicable to category 4 inverter-based resource, like setting and regulating frequency/voltage, providing voltage support, sharing active power, injecting negative sequence current, and riding through faults. A current limiter is proposed for safe fault ride-through and integrates with the grid-forming control featuring frequency/voltage droop controllers and current and voltage control loops. The transformation of interconnected inverters is formulated and stability of the proposed control analyzed to support robust parameter selections. The effectiveness of the proposed transformation and grid-forming control is demonstrated through analytical results and real-time simulation of a IEEE 123 distribution network on the Real-Time Digital Simulator. Comparison with existing methods shows that the proposed strategy satisfies the UNIFI specifications with a much better performance.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Study of the Protection Improvements for a Weak Grid Area With High Inverter-Based Resources (IBRs)

This project designs enhanced protection scheme for the real-world weak grid area with a high penetration of IBRs. As the existing protection schemes are originally designed for traditional synchronous machines, we first evaluate if the protection scheme will continue to operate reliably in systems with high levels of IBRs. Hardware relays are tested using a controller-hardware-in-the-loop setup. PSCAD electromagnetic transient simulation with IBR original equipment manufacturer black-box models is used to perform fault studies and generate COMTRADE data, which are replayed by a real-time digital simulator (RTDS) to feed input to the hardware relays. Three scenarios are analyzed: normal operation, an N-1 contingency, and an IBR-only scenario. The evaluation results reveal the following: 1) the protection scheme remains reliable under normal conditions and N-1 contingencies and 2) in IBR-only scenarios, differential protection (87L) continues to operate reliably, whereas local protection elements, such as distance and directional elements, fail because of the lack of regulated negative sequence current contributed by IBRs. Enhanced protection is designed to address the challenge of lack of negative sequence current from IBRs, including increased restraining factors a2 and k2 to block 32Q or using V instead QV ORDER for ground faults, enhanced mho distance element with voltage and phase angle supervision for L-L faults. The efficacy of enhanced protection logic is validated and proven to work reliably. Additionally, IEEE Std. 2800-2022 negative sequence current compliant GFL and GFM IBRs from another vendor are tested and proven to work reliably without need for enhanced logic. Therefore, this work provides valuable decision-making for utilities facing protection system challenges due to IBRs, either designing enhanced protection scheme or requesting their IBRs being IEEE Std. 2800-2022 compliant to produce regulated negative sequence current for protection relay to make correct decision.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Evaluating Protection System Performance for a Real-World Weak Grid Area With High Inverter-Based Resources: Preprint

This paper evaluates an existing protection scheme implemented in a real-world weak grid area with a high penetration of inverter-based resources (IBRs). The study aims to assess the reliability and adequacy of protection schemes originally designed for traditional synchronous machine systems and determine whether they can continue to operate reliably in systems with high levels of IBRs. Hardware relays are tested using a controller-hardware-in-the-loop setup. PSCAD electromagnetic transient simulation with an IBR original equipment manufacturer black-box model is used to perform fault studies and generate COMTRADE data, which are replayed by a real-time digital simulator (RTDS) to feed input to the hardware relays. Three scenarios are analyzed: normal operation, an N-1 contingency, and an IBR-only scenario. The evaluation results reveal the following: 1) the protection scheme remains reliable under normal conditions and N-1 contingencies and 2) in IBR-only scenarios, differential protection (87L) continues to operate reliably, whereas local protection elements, such as distance and directional elements, fail because of the lack of regulated negative sequence current contributed by IBRs. These findings provide utilities with valuable insights for improving their protection systems in high-IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Digital Grid Twin–Direct Communication Scheme Test Bed for Assessing Relay-to-Relay Radio Antenna and Optical Fiber Performance and Misoperations

This study introduces a novel “Digital Grid Twin–Direct Communication Scheme” test bed. This advanced platform evaluates point-to-point communication between transmitter and receiver relays with optical fiber and radio omnidirectional antenna systems, implemented at the Advanced Protection lab in the Grid Research Innovation and Development Center at Oak Ridge National Laboratory. The increased diversity of energy sources has led to more protective relay misoperations. In North America, microgrid protection schemes now use point-to-point communication along distribution lines between relays to implement advanced logic in nonradial grids that include both high- and low-inertia generators. This trend challenges utilities to minimize misoperations while ensuring rapid fault clearance and accurate selectivity coordination between primary and backup relays. This study assesses relay-to-relay communication schemes by introducing an advanced testing platform based on a digital grid twin protection test bed using a synchronized time source system. The platform evaluates the communication system using radio antennas or optical fiber links by integrating protective relays that operate breakers within the digital twin and record relay events and communication signals. In the experiments, transmitter and receiver relays were configured with inverse time overcurrent and breaker trip detection logic to assess the total time of the communication protection schemes based on the sum of the relay protection element operating time, radio latency, propagation delay, baud rate delay, and relay processing time. These delays were derived from recorded relay events and communication signals from the interface of a real-time simulator set as a digital grid twin. The test bed successfully simulated various electrical faults along a distribution line while ensuring effective and reliable point-to-point communication between transmitter and receiver relays. The radio antenna communication system exhibited latency because of the radio. This latency depends on the baud rate setting and type of radio application; in general, the higher the baud rate, the lower the radio latency. The measured radio latency (for Mirrored Bits with an encryption card at 9,600 bps) was about 9–10 ms. Additionally, calculated propagation delay per mile for radio antennas and optical fiber was 5.36 µs/mi and 8.04 µs/mi, respectively. Optical fiber communication did not demonstrate radio latency. Instead, the protection element operating time depends mainly on the protection logic function set in the relay, and the relay processing time depends on the processing rate of the relay in samples per power system cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2026

Fermilab PIP-II machine protection system digitized data noise elimination scheme and its FPGA implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges—especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies—advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance and were successfully implemented on a low-cost FPGA.

Wu, Jinyuan [Fermilab]↗
DOE OSTI2026

Fermilab PIP II machine protection system digitized data noise elimination scheme and its FPGA implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, J. [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗
DOE OSTI2026

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2026

Balance of Plant Modeling and Real-Time Hardware-in-the-Loop Integration with the Microreactor Automated Control System

The advent of novel microreactor technology has driven a focused effort to explore safety and efficiency improvements that can be achieved through the use of automated system control. Development of control strategies, especially for initial demonstration, requires an adequate surrogate environment to safely research failure modes and control integration with realistic hardware delay. However, efficiency gains from control strategies are improved when the scope of controller action is expanded to include system-level dynamics such as downstream heat extraction and mass flow. For this reason, a balance-of-plant (BOP) model of a representative microreactor system has been developed using the TRANsient Simulation Framework of Reconfigurable Models library in Modelica. This model captures a reactor and primary NaK coolant loop that represent corresponding system components of the Microreactor Applications Research Validation and EvaLuation (MARVEL) design as well as a secondary coolant loop and heat extraction representative of the Microreactor Agile Non-Nuclear Experimental Test Bed (MAGNET). This model configuration allows for hardware-in-the-loop (HIL) integration with microreactor automated control system (MACS) hardware in real time through a Python-based gRPC client. Real-time simulation of model performance with emulated hardware and communication delay suggests that under independent proportional-integral-derivative control of BOP model drum dynamics and downstream heat extraction, stable power load following is achievable. A slight delay in load following, filtering of high-frequency dynamics, and localized temperature fluctation suggest room for improvement through the development of higher-level control strategies. The simulated coupling of the MAGNET facility lays the groundwork for future digital twin analysis with a coupled MACS-MAGNET HIL demonstration.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗