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At least 163 records · Page 9

Model Predictive Fault-Tolerant Tracking Control for PDF Control Systems With Packet Losses

In this article, a fault-tolerant tracking control strategy is investigated for nonlinear probability density function (PDF) control systems with the actuator fault, uncertainties, unknown disturbance, and random packet losses. The control input signal dropout and measurement signal dropouts are described as the independent Bernoulli distribution. An adaptive fault diagnosis (FD) observer based on the Lyapunov function is given to simultaneously estimate the fault, disturbance, and state with packet losses. Furthermore, different from the traditional robust fault-tolerant control (FTC), a new active fault-tolerant tracking controller is designed based on the model predictive control framework, which has better adaptive fault-tolerant performance. Finally, the validity of the proposed FTC method has been proved by a simulation study of a papermaking process.

42 ENGINEERING↗

Locational Value of Distributed Energy Resources

This report focuses on potential DER benefits for distribution systems, primarily in their role as non-wires alternatives to defer, mitigate, or eliminate the need for some traditional system investments at locations where distribution capacity is insufficient to meet expected future needs. It describes approaches and tools to estimate the locational value of DERs. The report includes 24 case studies to illustrate how states and utilities are considering the locational value of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Constructing auxiliary dynamics for nonequilibrium stationary states by variance minimization

We present a strategy to construct guiding distribution functions (GDFs) based on variance minimization. Auxiliary dynamics via GDFs mitigates the exponential growth of variance as a function of bias in Monte Carlo estimators of large deviation functions. The variance minimization technique exploits the exact properties of eigenstates of the tilted operator that defines the biased dynamics in the nonequilibrium system. We demonstrate our techniques in two classes of problems. In the continuum, we show that GDFs can be optimized to study the interacting driven diffusive systems where the efficiency is systematically improved by incorporating higher correlations into the GDF. On the lattice, we use a correlator product state ansatz to study the 1D weakly asymmetric simple exclusion process. We show that with modest resources, we can capture the features of the susceptibility in large systems that mark the phase transition from uniform transport to a traveling wave state. Our work extends the repertoire of tools available to study nonequilibrium properties in realistic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

ARPA-E PNDIODES "PN Junctions by Ion Implantation" Project (Final Report)

Electricity accounts for nearly 40% of the energy generated in the United States, of which 65% is lost to electrical system inefficiency before reaching end-use sectors. Power electronics will play a growing role in this distribution and end-use, with up to 80% of electricity estimated to pass through power electronics from generation to consumption by 2030. This gives technical improvements in power electronics enormous potential to drive efficiency gains throughout the U.S. economy. Current Si-based power architectures including MOSFETs and IGBTs are limited by high losses, low switching frequency, and poor high-temperature performance. Given the material properties of GaN, devices can exhibit a given turn-on resistance and breakdown voltage with a minute fraction of the area of a Si device—all with higher switching speeds. While n-type doping of III-nitrides has reached a high level of maturity and integration into commercialized products, p-type doping presents distinct challenges that requires continued research. The success of the next generation of GaN-based power devices depends in large part on the ability to form selective-area p-type regions, which can be accomplished by ion implantation of the prevailing acceptor dopant, Mg. This approach is relatively inexpensive and is a fab-ready processing step for creation of current-generation power electronic devices including BJTs, IGBTs, and diode rectifiers. However, ion implantation induces lattice damage and creates point defects within the material, which negatively impacts dopant activation annealing. These forms of damage may be removed by high-temperature annealing, however short annealing time and higher pressures are required to prevent decomposition of the material. We approach this challenge through an innovative combination of approaches toward obtaining high-conductivity p-type GaN. By the novel employment of a gyrotron, a microwave source that can be shaped into a highly localized beam to dissipate very high power in a semiconductor target, a wafer of GaN can be annealed in short (<2 s) ‘pulses’ at temperatures of 1450 °C (2640 °F) to achieve p-type activation without appreciable surface degradation. The p-type activation of Mg-implanted GaN can be further aided by co-implantation with N, believed to enhance the removal of point defects that compensate the acceptors dopant. Elevation to such high temperatures for even a short time induces degradation of GaN-to overcome, we have implemented additional measures to retain high-quality crystalline GaN while removing implant damage and activating Mg dopant. A bilayer cap of AlN is deposited by a combination of chemical and physical vapor deposition to protect the GaN surface. To prevent decomposition of GaN loss of N during annealing, a custom vessel—to contain pressurized N while allowing injection of microwave energy—is employed. By detailed study of the interaction of various measures that are implemented to protect the sample surface (GaN), activate the implanted dopants, and mitigate damage of the lattice heating damage during annealing, we are able to achieve p-type GaN and observe recovery of the crystal and removal of substantial density of defects created during the implantation process.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Microgrid Energy Management System Integration with Advanced Distribution Management System

The Integrated Distribution Management System (IDMS) project was initiated to demonstrate the interactive operation of microgrid systems and the distribution systems with which they interconnect. The key technologies for this are the microgrid management system and the utility distribution management system. The IDMS project successfully demonstrated that a utility’s advanced distribution management system/distributed energy resources management system (ADMS/DERMS) could effectively manage microgrids to provide visibility and control functionalities as well as use the microgrid as a dispatchable resource to support the utility grid. The DERMS accomplishes this by determining active and reactive power needs at the point of common coupling (PCC) using advanced applications like volt/VAR watt optimization (VVWO) and load relief (LR) with the underlying core applications state estimation (SE) and load flow (LF). The ADMS/DERMS can use the microgrid as a resource to resolve and prevent violations in the grid and to optimize the operational working state of the grid. The IDMS project demonstrated that a utility-operated ADMS with embedded DERMS functionality can flexibly manage a variety of microgrids and other aggregated distributed energy resources (DER) in concert with the wider distribution grid. Microgrids can provide grid services in any number of different ways to meet the operational needs of the distribution utility. The manner of aggregation — microgrid or virtual power plant — is not necessarily relevant to the utility as long as the grid services from the aggregated DER are available and can be managed by its ADMS/DERMS for the stability and reliability of the grid. The IDMS project demonstrated this integrated ADMS concept by combining hardware/software-in-the-loop testing with commercial products from different vendors and a utility’s network model. The project integrated the Schneider Electric EcoStruxure™ ADMS with DERMS with the Schweitzer Engineering Laboratories (SEL) POWERMAX ® Microgrid Control System, and the simulated resources with energy company PECO’s model of a utility-owned microgrid, establishing an operational relationship in which the utility manages the operational functionality of a microgrid at the PCC and that provides the utility with the capability to control the comprehensive power system, inclusive of the macrogrid and microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Dynamic Power Network State Estimation with Asynchronous Measurements

The operation of distribution networks is becoming increasingly volatile, due to fast variations of renewables and, hence, net-loading conditions. To perform a reliable state estimation under these conditions, this paper considers the case where measurements from meters, phasor measurement units, and distributed energy resources are collected and processed in real time to produce estimates of the state at a fast time scale. Streams of measurements collected in real time and at heterogenous rates render the underlying processing asynchronous, and poses severe strains on workhorse state estimation algorithms. In this work, a real-time state estimation algorithm is proposed, where data are processed on the fly. Starting from a regularized least-squares model, and leveraging appropriate linear models, the proposed scheme boils down to a linear dynamical system where the state is updated based on the previous estimate and on the measurement gathered from a few available sensors. The estimation error is shown to be always bounded under mild condition. Numerical simulations are provided to corroborate the analytical findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Grid Topology Discovery Algorithm Evaluation of Suitability for Utility Deployment (CRADA 606 Final Report)

This work presents the results of a field-informed demonstration aimed at evaluating the practical suitability of a topology discovery algorithm for utility environments. We demonstrated an algorithm that uses a graph-theory-informed state estimation approach for model selection. In collaboration with Survalent and Peninsula Light Co., the algorithm was applied to real feeder models and field measurements from supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) systems to identify the operational topology of a power distribution system. The demonstration assessed the algorithm’s performance under realistic data conditions, including sparse and noisy measurements, and examined its ability to identify the most likely network configurations. The results confirmed that the approach can effectively narrow down feasible topologies, providing operators with improved situational awareness of network status. Key lessons learned emphasize the need for systematic data validation and strategic sensor placement to enhance observability. These insights inform future deployment strategies and guide refinements for broader adoption in utility operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Minnesota Solar Pathways: Illuminating Pathways to 10% Solar (Final Technical Report)

The Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, was a three-year project designed to explore least-risk, best-value strategies for meeting the State of Minnesota’s solar goals. As part of this aim, the Pathways Team modeled renewable generation costs, examined ways to streamline interconnection, and evaluated technologies that can increase solar hosting capacity on the distribution grid. Midway through the project period, the Pathways Team expanded the scope of study using the same modeling framework from the Minnesota Solar Potential Analysis to estimate and compare generation costs for high renewables penetration scenarios across the Midcontinent Independent System Operator (MISO) region. The analysis concludes that it can be cheaper to overbuild solar + wind and curtail surplus than to store all generation. In addition, the analysis of individual MISO subregions compared with the whole region highlights the opportunities of enabling market potential compared with impact of ongoing transmission constraints. Beyond the analysis of future scenarios, the partnerships initiated and supported through this project underscored the ongoing need for work with stakeholders to address key issues, particularly around siting, and leverage symbiotic strategies to meet state goals.

14 SOLAR ENERGY↗

Semi-Annual Report for Modular Integrated Gas High Temperature Reactor Development during Performance Period October 2021 - March 2022

Modular Integrated Gas High Temperature Reactor (MIGHTR) is being designed by a multidisciplinary team of nuclear, mechanical, and structural engineers under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed MIGHTR in 3 years and support its commercialization as a safe and low-cost HTGR. Argonne National Laboratory is responsible for the design and analysis of the reactor cavity cooling system (RCCS) as a safety system for passive decay heat removal of the reactor concept. Work performed by Argonne also includes the thermal hydraulics analysis of the primary heat transport system including reactor pressure vessel (RPV) internals for normal operation, shutdown, and accident conditions. This first semi-annual report summarizes the progress made at Argonne on the two tasks since the start of the project. For the RCCS design task, high-level design work was performed including identification of design requirements for the conceptual MIGHTR RCCS and initial design calculations. The baseline dimensions of the MIGHTR RCCS were derived from scoping analysis results and preliminary steady state performance was estimated. At the initial stage of the primary system analysis task, the primary system thermal fluids model for the MIGHTR core was outlined to use a simplified two-dimensional model to predict the temperature distribution and coolant pressure losses in the fuel assembly blocks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NPFTURBULENCE: Best Estimate Aerosol Size Distribution by airborne measurements

The original data were collected during the field campaign of “Turbulent layers promoting New Particle Formation” experiment (NPFTURBULENCE; https://www.arm.gov/research/campaigns/aaf2024npfturbulence) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/sgp ) in north-central Oklahoma. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS) was based at Blackwell–Tonkawa Municipal Airport (IATA: BWL, ICAO: KBKN, FAA LID: BKN, 36.74475° N, 97.34918° W, 313.9 m MSL), for the field campaign from May 5 through May 29, 2024. The ArcticShark UAS performed 11 flights, including 10 research flights over the Central Facility of the ARM SGP to measure atmospheric state, turbulence, surface IT temperature and imagery, aerosol number concentration and size distribution. The current data set presents Best Estimate Aerosol Size Distribution: a merged aerosol size distribution composed of the data from 2 sensors: miniaturized Scanning Electrical Mobility Sizer (mSEMS) and Portable Optical Particle Spectrometer (POPS). The mSEMS data were interpolated to 1 second from “native” time resolution of about 15 second to match the other probe. The POPS data were converted from equivalent optical size into geometric size using value of aerosol refractive index of 1.477 from the HISCALE field campaign (same geographical area, altitudes, and time of year; http://www.arm.gov/campaigns/aaf2016hiscale ).

54 ENVIRONMENTAL SCIENCES↗

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗

Spatial Heterogeneity in Cloud Condensation Nuclei (SHINE) Field Campaign Report

Multiple land-atmosphere coupling processes influence the spatial distribution of boundary-layer energy, moisture, aerosol, or cloud condensation nuclei (CCN) properties. Accurately quantifying the spatial and temporal distributions of CCN concentrations is essential for investigating aerosol-cloud interactions and reducing uncertainties in aerosol radiative forcing estimates of Earth's system.

54 ENVIRONMENTAL SCIENCES↗

Transport of Highly Volatile Gases Related to Noble Gases and Tritium in the MSRE

This study aims to comprehend mass transfer in the closed-loop circulation of highly volatile gases, including noble gases and tritium. We explore the impact of steady-state xenon-135 and tritium on the MSRE and reveal their isotopic distributions using online noble gas stripping of fuel salts. The MSRE was engineered to extract fission product gases from fuel salts and efficiently eliminate inert gases with the help of helium bubbles within a circulating fuel pump. These reactors introduce significant theoretical challenges in estimating interfacial area and mass transfer coefficients, crucial for modeling mass transfer processes. An essential component of our analysis is the mass transfer coefficient. These coefficients are important for understanding how radionuclides move during various phase transitions within a nuclear reactor. Xenon-135 and tritium are found in both liquid and gas phases within the reactor system. In the liquid phase, they dissolve in molten salts, while in the gas phase, they manifest as bubbles. These elements have significant adverse effects on reactor operation due to their strong neutron absorption properties, influencing both safety and performance. The Mole code, which predicts the behavior of chemical species under steady-state conditions, facilitates multiphysics coupling with Griffin to update species distributions and address inherent MSR safety.

Lee, Kyoung↗

Dynamic Parameter Estimation with Physics-based Neural Ordinary Differential Equations

Accurate estimation of dynamic parameters of gen-erators is crucial to building a reliable model for dynamical studies and reliable operation of the power system. This paper develops a physics-based neural ordinary differential equations (ODE) approach to learn the parameters of generator dynamic model using phasor measurement units (PMU) data. We design a physics-based neural network to represent the swing equations of the power system dynamics. A loss function is defined as the difference between dynamic simulation results from the physics-based neural networks and pseudo PMU measurements. The parameters of generator dynamic model are iteratively updated using the neural ODEs and the adjoint method. By exploiting the mini-batch scheme in neural ODE training, the parameter estimation performance is significantly improved. Numerical study results on a 3-machine 9-bus system show that the proposed algorithm outperforms state-of-the-art baseline method in both computation time and dynamic parameter estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cyber Resilient Flexible Alternating Current Transmission Systems (XFACTS)

This report summarizes the activities conducted under the DOE-OE funded project DEOE0000897, Cyber Attack Resilient Flexible AC Systems – XFACTS. Hitachi Energy (HE), in collaboration with ABB Inc. (ABB), Bonneville Power Administration (BPA), University of Illinois at Urbana-Champaign (UIUC), Iowa State University (ISU), and University of Idaho (UI) pursued the development of a system of defense for Flexible Alternating current Transmission Systems against cyber-attacks (XFACTS). A FACTS substation enhanced with XFACTS defense mechanisms will be capable of mitigating cyberattacks especially those that seek to control electrical parameters like voltage or current and interrupt the power flow in AC lines. It empowers existing FACTS controllers and associated intelligent electronic devices to detect and mitigate malicious intents to depress system voltages, destabilize power flows, trip AC circuit breakers, corrupt currents, and voltages, even if the malicious commands and the measurements have correct syntax. The XFACTS functions utilize the physics of active power electronic systems, control and protection, electric power engineering principles, and state estimation to bring more in-depth cyber defense closer to the protected FACTS substation devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Inferring the Dynamics of the State Evolution During Quantum Annealing

To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer, which then returns a user-specified number of low-energy solutions. It would be useful to know what happens in the quantum processor during the anneal process so that one could design better algorithms or suggest improvements to the hardware. However, existing quantum annealers are not able to directly extract such information from the processor. Hence, in this work we propose to use advanced features of D-Wave 2000Q to indirectly infer information about the dynamics of the state evolution during the anneal process. Specifically, D-Wave 2000Q allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the anneal. Furthermore, using this feature, we design a set of modified anneal schedules whose outputs can be used to generate information about the states of the system at user-defined time points during a standard anneal. With this process, called "slicing", we obtain approximate distributions of lowest-energy anneal solutions as the anneal time evolves. We use our technique to obtain a variety of insights into the annealer, such as the state evolution during annealing, when individual bits in an evolving solution flip during the anneal process and when they stabilize, and we introduce a technique to estimate the freeze-out point of both the system as well as of individual qubits.

42 ENGINEERING↗