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DOE OSTI2021

Smart Inverter Stability Enhancement in Weak Grids Using Adaptive Virtual-Inductance

An adaptive virtual-inductance feedforward scheme is presented in this article to enhance the stable operation of smart inverters in weak grids. In the developed scheme, the virtual inductance is updated in real time according to an adaptation law to ensure that the inverter follows the dynamic response of a robust reference model, thereby ensuring stability without the need for any grid impedance estimation. In this article, the developed scheme is validated with experimental results obtained from testing a small-scale two-level, 208-V, 2-kW inverter.

42 ENGINEERING↗

DOE OSTI2020

Generic characterization of electrical test benches for AC- and HVDC-connected wind power plants

The electrical test and assessment of wind turbines go hand in hand with standards and network connection requirements. In this paper, the generic structure of advanced electrical test benches, including grid emulator or controllable grid interface, wind torque emulator, and device under test, is proposed to harmonize state-of-the-art test sites. On the other hand, modern wind turbines are under development towards new features, concerning grid-forming, black-start, and frequency support capabilities as well as harmonic stability and control interaction considerations, to secure the robustness and stability of renewable-energy-based power systems. Therefore, it is necessary to develop new and revised test standards and methodologies to address the new features of wind turbines. This paper proposes a generic test structure within two main groups, including open-loop and closed-loop tests. The open-loop tests include the IEC 61400-21-1 standard tests as well as the additional proposed test options for the new capabilities of wind turbines, which replicate grid connection compliance tests using open-loop references for the grid emulator. In addition, the closed-loop tests evaluate the device under test as part of a virtual wind power plant and perform real-time simulations considering the grid dynamics. The closed-loop tests concern grid connection topologies consisting of AC and HVDC, as well as different electrical characteristics, including impedance, short-circuit ratio, inertia, and background harmonics. The proposed tests can be implemented using available advanced test benches by adjusting their control systems. The characteristics of a real power system can be emulated by a grid emulator coupled with real-time digital simulator systems through a high-bandwidth power-hardware-in-the-loop interface.

17 WIND ENERGY↗

DOE OSTI2021

Materials for Harsh Environments

The 2020 Materials for Harsh Service Conditions (M4HSC) Workshop was coordinated by three different U.S. Department of Energy (DOE) Offices: the Advanced Manufacturing Office (AMO), the Office of Fossil Energy (FE), and the Office of Nuclear Energy (NE). The event was held virtually October 27–30, 2020. The workshop brought together stakeholders from academia, industry, national laboratories, and DOE offices to identify the opportunities (paths for obtaining desirable goals), challenges (actionable tasks taken up along these paths), barriers (obstacles impeding advancement along the paths), and research and development (R&D) needs for enabling development of technology readiness level (TRL) 3–6 materials and materials systems, as well as their advancement into widespread commercial application.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Polysulfamates as “Macroisosteres” of Polyurethanes with Improved Degradability

Abstract Addressing the environmental persistence of plastics requires the development of next‐generation polymers that combine high performance with enhanced degradability. Progress toward this grand challenge has been impeded, in part, by the absence of a general blueprint for the macromolecular design of such materials. Herein, we introduce a “macroisostere” design strategy, where the carbonyl group (–CO–) in polyurethanes (PUs) is replaced with a sulfonyl group (–SO 2 –), resulting in a virtually unknown family of polymers called polysulfamates. This approach, inspired by the use of bioisosteres in drug discovery, aims to preserve key interchain interactions that contribute to thermomechanical performance while enhancing the hydrolytic lability of the polymer backbone. The optimization of a Sulfur(VI) Fluoride Exchange (SuFEx) polymerization allowed the synthesis of ten polysulfamates structurally analogous to common PUs. Comparative analysis of one PU and its polysulfamate analog showed that this isosteric substitution increases thermal stability, slightly lowers the glass transition temperature, and retains similar hardness and reduced Young's modulus. Notably, the S(VI)‐based polysulfamate demonstrated significantly enhanced hydrolytic degradability. These results highlight the potential of the “macroisostere” approach as a generalizable strategy for designing high‐performance, degradable alternatives to traditional plastics.

Chemistry↗

DOE OSTI2023

Impedance Modeling and Stability Analysis for the PLL-less and Voltage Sensor-less Grid-tied Converters

The newly developed control methodology, which omits the phase-locked loop (PLL) and voltage sensor, has garnered considerable attention for its rapid dynamic response and reduced hardware expenses. Nonetheless, its stability concerns have not yet been thoroughly scrutinized. In this paper, the impedance modeling of the PLL-less and voltage sensorless control strategy has been established. The impedance model has been contrasted with the traditional PLL-based control scheme. Our findings demonstrate that the PLL-less and voltage sensorless control approach lacks the negative resistance behavior that is frequently observed in the traditional scheme, which results in unstable operations. Furthermore, the role of virtual resistance has been examined. To substantiate the aforementioned findings, simulation results are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Adaptive critic design-based reinforcement learning approach in controlling virtual inertia-based grid-connected inverters

In this report, an adaptive critic design (ACD) approach is proposed to control the phase and voltage of a grid-connected virtual synchronous generator (VSG). The penetration of fast responding inertia-less power converters significantly affect the stability of the power system, especially weak systems such as micro grids. The concept of virtual inertia addresses this concern by virtually emulating the behavior of a synchronous generator. However, the conventional VSG is designed based on two conditions: (i) fixed operating point and (ii) inductive grid connections. The performance of VSGs in low-voltage semi-resistive microgrids is far from optimal. To overcome the aforementioned concerns, a heuristic dynamic programing (HDP) approach is proposed to optimally control grid-connected VSGs. The neural-network-based inherence of the HDP enables the proposed technique to adapt to any impedance angle. The HDP controller includes two subnetworks: (i) the action network that controls the system optimally and (ii) the critic network, which evaluates the effectiveness of the action network. The simulation and experimental results are provided to evaluate the effectiveness of the proposed technique. As shown, the HDP-based approach illustrates a better performance in comparison with the conventional PI-based VSG in various operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Adaptation of virtual synchronous generators to dynamic conditions in power grids

Virtual synchronous generators (VSGs) are widely adopted as grid-forming controls for inverter-based resources. However, when grid conditions vary significantly as characterized by changes in short-circuit ratio (SCR) and the reactance-to-resistance (X/R) ratio, fixed-gain designs and the commonly used P–Q decoupling assumption can become inaccurate. Such conditions can degrade transient power performance, leading to oscillations, prolonged settling, and overshoot, particularly in stiff-grid operating points. This paper quantifies how grid strength and impedance-dependent coupling affect the active–reactive power dynamics of a conventional VSG over a broad range of SCR and X/R values. An adaptive VSG tuning framework is then developed by combining (i) a coupling-explicit, impedance-parameterized state-space model to enable systematic controller synthesis, (ii) a full-state-feedback law designed via pole placement to meet prescribed damping and settling-time specifications, and (iii) a physics-informed neural network (PINN)–based online grid-impedance estimator that updates controller gains in real time as grid conditions vary. Offline simulations in MATLAB/Simulink and real-time validation on an OPAL-RT platform show that the proposed method preserves consistent damping and settling behavior with reduced overshoot across wide SCR and X/R ranges, compared with fixed-gain VSG baselines.

Adaptive control↗

DOE OSTI2025

Stability Analysis of Power Systems with High Penetration of State-of-the-Art Inverter Technologies

With the increasing level of inverter-based resources (IBRs) in modern power systems, this paper presents a small-signal stability analysis for power systems comprising synchronous generators (SGs) and IBRs. Four types of inverter controls are considered: two grid-following (GFL) controls, with or without grid support functions; droop-based grid-forming (GFM) controls; and virtual oscillator control-based GFM. We also analyze the impact of STATCOM and synchronous condensers on system stability to assess their role in the energy mix transition. With the small-signal dynamic behavior of the major technologies modeled, this paper provides stringent stability assessments using the IEEE 39-bus benchmark system modified to simulate future power systems. The exhaustive test cases allow for (a) assessing the impacts of different types and controls of generation and supplementary grid assets, as well as system inertia and line impedance on grid stability, and (b) elucidating pathways for the stabilization of IBR-dominated power systems. The analysis also indicates that future power systems can be stabilized with only a fraction of the total generation as voltage sources without SGs or significant system inertia if they are well distributed. This study provides insights into future power system operations with a high level of IBRs that can also be used for planning and operation studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A consumer-centric approach to quantify efficiency of receiving goods purchased via online

Virtual participation in shopping activities has increased exponentially in the past four years compared to the last couple of decades. E-tailing or online shopping offers the convenience of goods reaching a consumer instead of a consumer traveling to a store, but it has downsides like geographical service variability and negative social externalities such as increased energy consumption and emissions. This study proposes a novel approach to quantify e-tailing efficiency from the consumers’ viewpoint. The methodology is innovative in its integration of accessibility theory with energy and cost impedance factors and consideration of delivering and picking up goods purchased via online. The methodology is implemented for the San Francisco Bay Area and is subject to scenarios that see enhancements to various facets of online shopping delivery. Results demonstrate that increasing the frequency of e-commerce deliveries helps improve e-tailing efficiency in rural locations, while improvements in energy and cost aspects of delivery modes are seen to improve e-tailing efficiencies in the central parts of the region. The approach proposed can provide valuable insights on where people have limited benefits from online shopping and how emerging delivery mechanisms can change the quality of the e-commerce experience within a city. This research offers a replicable framework for assessing e-commerce systems in diverse geographic contexts, contributing to the development of equitable and environmentally sustainable urban freight systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Reinforcement Learning Approach to Cybersecurity in Space (RELACSS)

Securing satellite groundstations against cyber-attacks is vital to national security missions. However, these cyber threats are constantly evolving. As vulnerabilities are discovered and patched, new vulnerabilities are discovered and exploited. In order to automate the process of discovering existing vulnerabilities and the means to exploit them, a reinforcement learning framework is presented in this report. We demonstrate that this framework can learn to successfully navigate an unknown network and detect nodes of interest despite the presence of a moving target defense. The agent then exfiltrates a file of interest from the node as quickly as possible. This framework also incorporates a defensive software agent that learns to impede the attacking agents progress. This setup allows for the agents to work against each other and improve their abilities. We anticipate that this capability will help uncover unforeseen vulnerabilities and the means to mitigate them. The modular nature of the framework enables users to swap out learning algorithms and modify the reward functions in order to adapt the learning tasks to various use cases and environments. Several algorithms, viz., tabular Q learning, deep Q networks, proximal policy optimization, advantage actor-critic, generative adversarial imitation learning, are explored for the agents and the results highlighted. The agent learns to solve the tasks in a light-weight abstract environment. Once the agent learns to perform sufficiently well, it can be deployed in a minimega virtual machine environment (or a real network) with wrappers that map abstract actions to software commands. The agent also uses a local representation of the actions called a ‘slot-mechanism’. This allows the agent to learn in a certain network and generalize it to different networks. The defensive agent learns to predict the actions taken by an offensive agent and uses that information to anticipate the threat. This information can then either be used to raise an alarm or to take actions to thwart the attack. We believe that with the appropriate reward design, a representative environment, and action set, this framework can be generalized to tackle other cybersecurity tasks. By sufficiently training these agents, we can anticipate vulnerabilities leading to robust future designs. We can also deploy automated defensive agents that can help secure satellite groundstation and their vital national security missions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Data-driven building energy modeling with feature selection and active learning for data predictive control

Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this work, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters

In this paper, a neural network adaptive critic design (ACD) method based on value gradient learning (VGL) is used to optimally control a grid-connected synchronverter. The main drawback of the traditional synchronverters is their infeasibility to face non-inductive grids. To be able to implement a synchronverter technique in any impedance angle, a neural network-based adaptive controller is used. The advantage of adaptive dynamic programing is its ability to adjust itself when it faces changes and uncertainties in the power system. The proposed VGL consists of two subnetworks: the critic network and the action network. The action network is trained during the operation, and the critic network can be pretrained offline or can be simultaneously trained with the action network. To compare the effectiveness of the traditional synchronverter to the VGL-based synchronverter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Understanding the Mechanisms of Self-Organization in Atmospheric Pressure DC Glows

One of the most challenging areas in atmospheric pressure plasmas is controlling uniformity and contact area. Indeed it is this problem which is closely related to scale up that has impeded the widespread applications of these plasmas. The atmospheric pressure plasma glow is a type of atmospheric pressure discharge akin to a traditional low pressure glow particularly when compared with discharge morphology. But this is where the similarities end. Unlike low pressure glows, atmospheric pressure glows do not rely on electrode processes such as secondary electron emission to make the discharge self-sustaining, rather the discharge is sustained by typically processes such as the presence of a resistive layer or through favorable atomic collisional processes such as that which prevail in helium that stabilizes the discharge so that the thermal instability is mitigated. These discharges can feature either solid metal electrodes or geometries where at least one electrode is a liquid water electrolyte. Regardless of the implementation, DC 1 Atm glows are fascinating in that the plasma attachment under certain conditions can self-organize. The attachment surface area therefore changes as a result. Therefore, self-organization could be the key to optimizing plasma contact area and thereby open the door to applications in the real world. The plasma physical processes prevailing at the surface where the self-organization takes place remain largely unexplored. There is very little published research on the conditions that prevail at the surface and virtually no scholarship on measured plasma parameters of the self-organized attachments themselves or the surface electric field. This effort breaks new ground by elucidating these largely unexplored physical processes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Initial Mobility Analysis for ORNL VA-EDH Synthetic Populations

Travel burdens are a major barrier to healthcare access among US Veteran patient populations, particularly those residing in rural areas. Spatial accessibility to points of care for US Veteran populations is commonly assessed in two ways. The first approach uses open data from the US Census to represent collective travel burdens, for example the distance between population-weighted census tract centroids and VHA points of care. The second approach uses restricted-access VHA patient data to measure travel costs (e.g., distance, time) for accessing points of care with respect to geolocated patient addresses and real or approximated transportation networks. While the advantage of the open data approach lies in its reproducibility, it has notable limitations in its tendency to infer individual travel behavior from aggregate population characteristics, a problem known as ecological fallacy. Conversely, while the patient data approach is able to account for individual travel behavior, its ability to account for localized access disparities (e.g., a neighborhood with exceptionally high transportation costs) and patient demographics is limited as protecting individual patient data requires their storage in closed systems with limited capacity for adequately modeling real-world travel patterns or for supplementing patient attributes. Additionally, the patient data approach cannot account for veterans who are not enrolled in the VHA system but who may be eligible for care. These challenges limit the ability to perform “what if” analyses on the effects of place-specific interventions on veteran populations with high access barriers to healthcare. To address these challenges, we explore the application of realistic synthetic populations to examine travel burdens and spatial accessibility issues among veteran patient populations. Synthetic populations provide a virtual, individually-resolved and cross-sectional representation of the veteran patient population that enables investigation of spatial access to points of care in ways in which aggregate data and patient data do not. First, synthetic populations allow one to directly assess how individuals access points of care, from synthesized residential locations to outpatient facilities on real-world transportation networks. Modeling access to points of care at the individual scale addresses the ecological fallacy problem associated with using aggregated census data to represent veteran populations and patterns of movement. Second, synthetic populations provide a means of completely representing an area’s veteran population using only publicly available, anonymized census microdata from the American Community Survey (ACS) to ensure the privacy of real-world individuals. Generating synthetic populations from the ACS also expands descriptive characteristics beyond what patient data typically offers to include socio-demographic, economic, housing, and mobility attributes. More detailed profiles of both VHA patient populations and veterans not enrolled in the VA system will provide a comprehensive picture of groups that may benefit from interventions or outreach. As an initial exercise for using synthetic populations to measure veteran travel burdens to VA care, we apply Oak Ridge National Laboratory’s (ORNL) UrbanPop capability to generate a series of synthetic VHA patient populations for 9 Veterans Integrated Services Networks (VISN) market areas in 9 Census Divisions across the continental United States, which are listed in Table 1. We use UrbanPop to produce synthetic populations for the VISN markets selected for each US Census Division, then assign VA outpatient clinic destinations to synthetic VHA patients based on travel about each VISN market’s road network. To demonstrate using the synthetic populations to evaluate healthcare travel burdens, we compare the time-based impedance between simulated home locations and VA outpatient clinics in each VISN market. We then perform validation exercises on the synthetic populations with respect to neighborhood (block group) demographic composition as well as patient mobility, comparing aggregate origin-destination statistics for the synthetic population to outpatient visits available in restricted patient data from the VA’s Corporate Data Warehouse (CDW) database.

97 MATHEMATICS AND COMPUTING↗