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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

Submodule-Level Decentralized Control for Cascaded Quadruple Active Bridge-Based Medium-Voltage Converters

High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Submodule-Level Decentralized Control for Cascaded Quadruple Active Bridge-Based Medium-Voltage Converters: Preprint

High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2025

Design, Control, and Protection of a 13.2 kV, 1 MVA Solid State Transformer for Electric Vehicle Extreme Fast Charging Station

In this article, a medium-voltage (MV) ac-dc solid state transformer (SST) for electric vehicle (EV) extreme fast charging (XFC) station is proposed. The SST adopts a cascaded H-bridge (CHB)-based structure where the active front end (AFE) power stages are connected in input-series followed by dual active bridge (DAB) converters connected in an output-parallel configuration providing galvanic isolation through a high-frequency transformer (HFT). The SST is rated for 1 MVA and connects directly to a three-phase 13.2 kV MV ac grid through ac switchgear and outputs 750-V dc. At the dc bus, several dc/dc converters are connected, each of which can charge an EV based on its battery capacity. A novel decentralized control architecture of the SST is adopted in this work which simplifies the MV dc link voltage and module-level power balancing. In addition, the local and central protection designs of the SST are presented which identify and respond to the internal fault of the system. Finally, the experimental validations of the SST hardware prototype are presented up to the rated voltage. Furthermore, this article details the design and implementation of the MV SST addressing the challenges of an isolated MV class power converter for connecting directly to the MV ac grid with unique controller architecture, distributed protection framework, and SST constructional features.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Infrastructure first principles for the Anthropocene

Abstract There appears to be a growing decoupling between the conditions that infrastructures were designed for and today’s rapidly changing environments. Infrastructures today are largely predicated on the technologies, goals, and governance structures from a century ago. While infrastructures continue to deliver untold value, there is growing evidence that these critical, basic, and lifeline systems appear ill-equipped to confront the volatility, uncertainty, accelerating conditions, and complexity that define them and their changing environments. Innovative and disruptive first principles are needed to guide infrastructures in the Anthropocene. Drawing from emerging infrastructure research and disciplines that appear better able to confront disruption and change, a novel set of first principles are identified: (1) Plan for complex conditions and surprise; (2) Recouple with agility and flexibility; (3) Govern for exploration and instability; (4) Build consensus as control decentralizes; (5) Restructure to engage with porous boundaries; and, (6) Cyberthreat planning is now mission critical. These principles should guide infrastructure planning recognizing the changing nature and increasingly obsolete boundaries that have defined engineered systems in the modern era.

Chester, Mikhail (ORCID:0000000293542102)↗
DOE OSTI2025

High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP): Site Energy Management System Platform Development

A site energy management system is a critical component of future high-power EV charging hubs. It monitors and supervises all inter-hub operations, coordinates grid interactions, and implements energy management strategies and load-sharing controls at various levels and time scales. This report provides a comprehensive high-level overview and guidelines on the essential elements of such energy management systems. These elements primarily focus on the core functions, architecture, and monitoring aspects of site energy management. The core functions define the grid services and operational objectives that a charging hub is expected to provide and achieve. The architectural section investigates the coordination between hub assets, different control schemes, and their potential benefits and drawbacks. Lastly, the report discusses the needs, types, and functionalities of a monitoring and visualization system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Fully Decentralized Modulation Scheme for Modular Multilevel Converters

As the number of submodules rapidly increase, control architecture of Modular Multilevel Converters (MMCs) is transforming from centralized to distributed in order to address the challenges of tremendous computational burden and massive wiring. In this digest, we propose a modulation scheme for the MMCs with distributed controls, where each submodule is able to handle both controls and modulation in a decentralized manner. It enables the distributed controllers to synchronize in terms of fast-switching pulse width modulation (PWM). Aiming at this key technical challenge, this research develops a physics-informed PWM control strategy of mirroring the spontaneous synchronization feature of coupled oscillator’s behavior at switching frequency. Thanks to less reliance on wirings and communications, the proposed approach has the potential of i) improving reliability of MMC systems, and ii) reducing cost and space requirements for MMCs. The proposed approach has been preliminarily verified through a simulation tool for power electronics systems.

Lu, Minghui [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗
DOE OSTI2026

Solar Energy CommUnity Resiliency (Final Technical Report)

Final Technical Report for DE-EE0009336 Solar Energy CommUnity Resiliency (SECURE) The Solar Energy CommUnity Resiliency (SECURE) project aimed to support development of resilient community microgrids to improve grid reliability and maintain power during times of crisis. The SECURE project approached this objective by addressing key technical and business challenges impeding implementation of resilient community microgrids. To achieve the goals of this project, we pursued several specific objectives through comprehensive design and requirements development.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Networked Microgrid Energy Management Considering Ownership and Control Structures: A Comparison

With the growing deployment of microgrids, networked microgrids have emerged for their additional advantages of economy, reliability and resilience by coordinating the operation of multiple microgrids. As microgrids are operated with different ownership, objectives and functionalities, the formed networked microgrids show characteristics of mixed ownership, inconsistent objectives and various functionalities. To enable the coordinated operation of networked microgrids, three control structures, i.e., centralized, distributed and decentralized, have been constructed in the literature. However, the data sharing enabling these different paradigms and the resulted value propositions are not well defined, leading to poor resource management and resilience, etc. To solve this issue, a complete comparison of networked microgrid energy management under centralized, distributed and decentralized structures are performed. As a novel contribution, the required minimum data exchange of networked microgrid energy management under three control structures are identified, respectively. The value propositions are calculated and compared against each other by the results of case studies.

control structures↗
DOE OSTI2025

Rotated-Droop Control for Enhanced Stability and Power Decoupling in Microgrids With Complex Line Impedances

Classical droop control in microgrids predominantly assumes inductive line impedance, which simplifies implementation but causes power coupling and steady-state errors in systems with resistive and inductive lines. This paper proposes a rotated-droop control strategy for grid-forming inverters that reformulates the power equations by incorporating the magnitude and angle of the line impedance within a rotated reference frame. This method enhances the decoupling of active and reactive power dynamics without increasing complexity or requiring communication links. A small-signal state-space model was created to capture the dynamic behavior of the system under varying impedance parameters, preserving the original droop gains by rotating the power control structure. This allows eigenvalue-based stability analysis and enhances damping and transient performance. Simulation and experimental results validated the improved power-sharing performance, faster response, and robustness of the proposed method under different impedance conditions. This approach maintains the decentralized structure of the conventional droop control while enabling greater adaptability and scalability, making it suitable for modern inverter-based microgrids with dynamic topologies.

Campo-Ossa, Daniel Dario [Univ. of Puerto Rico, Ag↗
DOE OSTI2024

Deep Multi-Agent Reinforcement Learning for Real-World Signalized Traffic Corridor Control

Signalized traffic control problem has been addressed recently with deep Reinforcement Learning (RL) approaches involving diverse state, action, and reward structures. While significant progress has been noted in the literature, open challenges still remain in the areas of adaptive signal phase timing, coordination in a multi-intersection corridor setting, and consideration of real-world traffic conditions. In the context of deep RL-based problem framing, extensions are needed that enable adaptive signal phase timings in an intersection agent's action space, computationally efficient information sharing among neighboring signalized intersection agents along a corridor, and experimentation in realistic simulation environments. In this paper, we develop a deep Advantage Actor Critic (A2C) multi-agent RL (MARL) approach capturing the research extensions above and apply it within a real-world calibrated Aimsun Next traffic corridor simulation model based on traffic data from the City of Coral Gables, Florida. For a multi-intersection corridor control setting, our numerical simulation experiments with a decentralized A2C MARL algorithm applied at different time periods led to a total average corridor travel delay reduction (expressed in seconds/mile averaged over vehicles) from 4.9% to 19.9% compared to state-of-the-art actuated control.

Shuvo, Salman S. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2025

Virtual Self-Excited Induction Generator-Based Grid-Forming Inverter Control for Robust Voltage Regulation Under Nonideal Loading

This paper presents a generator-inspired control methodology for grid-forming (GFM) inverters that deliberately emulates a self-excited induction generator so that the inverter can hold its voltage and frequency under difficult loading and severe terminal disturbances across wide voltage and frequency ranges. The design integrates a Lyapunov energy function-based inner loop to provide high bandwidth and strong disturbance rejection, and it complements this with a passivity-based argument that furnishes a coherent large-signal stability guarantee beyond small-signal limits. Analytical insights are developed via the Krylov-Bogoliubov-Mitropolsky averaging method, which reveals an intrinsic resistive droop characteristic; these closed-form relations both explain the observed dynamics and yield simple, decentralized tuning rules. The methodology is validated on a controller-hardware-in-the-loop platform and exercised in real time across balanced, unbalanced, and nonlinear loads, as well as during parallel operation. Across these scenarios, the inverter maintains balanced three-phase voltages, limits harmonic content, settles quickly with well-damped transients, and remains resilient when multiple units operate in parallel. The contributions are a self-excited-machine-inspired GFM controller with enhanced dynamic performance and robustness, a single stability rationale grounded in passivity, closed-form expressions that guide tuning, and comprehensive hardware-in-the-loop validations demonstrating effectiveness and superiority under challenging operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Unified Universal Control and Coordination of Inverter-Based Resources, and Validation for a PV + Battery Hybrid Plant

As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.

14 SOLAR ENERGY↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗
DOE OSTI2025

Performance Evaluation of Vertical Federated Machine Learning Against Adversarial Threats on Wide-Area Control System: Preprint

Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.

adversarial threats↗