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DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗

DOE OSTI2024

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗

DOE OSTI2024

Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models

As the power system continues to be flooded with intermittent resources, it becomes more important to accurately assess the role of hydro and its impact on the power grid. While hydropower generation has been studied for decades, dependency of power generation on water availability and constraints in hydro operation are not well represented in power system models used in the planning and operation of large-scale interconnection studies. There are still multiple modeling gaps that need to be addressed; if not, they can lead to inaccurate operation and planning reliability studies, and consequently to unintentional load shedding or even blackouts. As a result, it is very important thathydropower is represented correctly in both steady-state and dynamic power system studies. In this paper, we discuss the development and use of the Hydrological Dispatch and Analysis Tool (Hy-DAT) as an interactive graphical user interface (GUI), that uses a novel methodology to address the hydropower modeling gaps like water availability and inter-dependency using a database, and algorithms, to generate accurate representative models for power system simulation.

Wang, Dewei↗

DOE OSTI2021

Electromagnetic Transient Simulation Algorithms for Evaluation of Large-Scale Extreme Fast Charging Systems (Distribution Grid Models)

The distribution and transmission grids are observing an increased penetration of power electronics in loads and generations. For example, there is increasing interest in integrating in extreme fast charging (XFC) systems for fast charging of electrical vehicles. As these systems are integrated, developing high-fidelity electromagnetic transient model of XFC systems in distribution grids and evaluating their interactions with the power grid would be of significant interest. This model will be utilized for design of XFC systems, to identify upgrades in distribution and/or transmission grids, for planning purposes by transmission planners or operators or owners, among others. It can also be utilized in operations for improved reliable performance of the grid and/or XFC station. The challenge with simulating these models is the high computational complexity introduced by the large number of states present in the system and the time-step needed to simulate the system. In this paper, advanced simulations algorithms are applied to reduce the computational complexity of simulating large-scale XFC systems. The algorithms include numerical stiffness-based segregation, time constant-based segregation, clustering and aggregation on differential algebraic equations (DAEs), and multi-order integration approaches. While the first three algorithms split the matrix that needs to be inverted from a large matrix to much smaller matrices, the final algorithm reduces the computational burden of applying higher-order integration approaches in the complete system. The comparison made in the previous sentence is with respect to use of homogeneous integration approaches used in conventional electromagnetic transient simulators like power systems computer aided design (PSCAD). The approaches mentioned here have resulted in speed-up of 36x in the simulation of a single distribution system with 15 XFCs.

Debnath, Suman↗

DOE OSTI2024

Dispatching Grid-Forming Inverters in Grid-Connected and Islanded Mode

This paper explores the dispatchability of grid-forming (GFM) inverters in grid-connected and islanded mode. GFM inverters usually use droop control to automatically share power with other GFM sources (inverters and synchronous generators) and follow the change in the load demand; however, they can be dispatched like their grid-following (GFL) counterparts to output the target active and reactive power. This will help grid operators better manage their inverter-based resources (IBRs) to improve operation efficiency and reliability; therefore, this paper proposes an innovative concept of dispatching GFM sources (inverters and synchronous generators) to output the target power in both grid-connected and islanded mode by adjusting their droop intercepts. The fundamental principle is that the GFM inverter's active and reactive power is dictated by its frequency and voltage, and thus dispatching the active and reactve power of a GFM inverter can be achieved through dispatching its frequency and voltage. Moreover, the concept distinguishes the dispatch rules for grid-connected and islanded mode. Finally, the concept is validated with an example microgrid system with two GFM inverters, one diesel generator, one GFL inverter, and the load in both grid-connected and islanded mode. This pioneering work results in practical guidance for the development of energy management systems for future electric grids with GFM and GFL inverters.

dispatching↗

DOE OSTI2023

Model Agnostic Bayesian Framework for Online Anomaly/Event Detection in PMU Data

Phasor measurement units (PMU) are integral to the modernization and automation plan of the electric power industry. A PMU data signature contains system-level events (e.g., faults, generation/load change, etc.) and any measurement/device-related errors. Therefore, the reliable and resilient operation of power systems is equivalent to the quality of the PMU data and the situation awareness provided by its data signature. Despite recent progress, current state-of-the-art methods are not fool-proof and have certain limitations tracing an error/abnormality to sensor sub-components and grid systems. This is because of technical challenges imposed by the scarcity of the labeled information, loss of data quality, and non-stationarity of data. In this paper, we consider the online PMU data stream as an output of a stochastic process and pose the anomaly/event detection as a changepoint detection problem dealing with detecting parameter changes in the underlying stochastic processes. The proposed model-agnostic framework relies on: (a) feature extraction utilizing the minimum volume enclosing ellipsoids (MVEE) method from raw PMU observations and (b) a Bayesian framework of changepoint detection. The validity of the proposed methodology is discussed through numerical experiments on real-world utility-scale PMU data.

Hossain, Ramij Raja↗

DOE OSTI2021

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Here, deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

42 ENGINEERING↗

DOE OSTI2024

Software-Defined Virtual Synchronous Condenser

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Furthermore, extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

17 WIND ENERGY↗

DOE OSTI2022

EXPERIMENTAL AND MODELLING ANALYSIS OF A LARGE-SCALE TWO-PHASE LOOP THERMOSYPHON

Liquid pumping requires large quantities of electrical energy, including about 7% of the energy of building heating, ventilation, and air conditioning (HVAC) systems. To replace pumped condenser-cooling tower water loops with a passive alternative system, we implemented a commercial-scale two-phase loop thermosyphon (TPLT). The unit consists of a 13 m riser integrated with a commercially available cooling tower and circulation water heater that simulates heat loads up to 25 kW. In addition to providing passive cooling capabilities, the cooling tower unit is also maintenance free, reliable, and can operate in both dry and wet modes. This study characterizes the performance (temperature difference between the evaporator and condenser) and the two-phase flow behavior of the loop under various refrigerant charges. Sight glasses installed throughout the loop are used to identify the operating flow regimes in the riser and downcomer. Over the range of operating conditions, we identified that there is an optimal refrigerant charge range for a specific heat load at which near-isothermal operation can be achieved. We further developed a model to predict the operating flow rate and gravitational height in the downcomer and compared it with the experimental data. The results show that the model agrees with the experimental data, in particular the threshold gravitational head height at which there will be subcooled liquid at the condenser exit, indicating that it can be used as a predictive tool for identifying the optimal loop charge for a given heat load.

Loop thermosyphon, passive two phase↗

DOE OSTI2022

Hydrogen and Battery Storage Technologies for Low-Cost Energy Decarbonization in Distribution Networks

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. Here, a proton exchange membrane electrolyzer can produce H 2 and serves as a utility controllable load. The produced H 2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H 2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H 2 at $\$1$ /kg are presented showing that the distribution network could produce 100% renewable electricity and H 2 could be produced at a cost of $\$1$ /kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.

25 ENERGY STORAGE↗

DOE OSTI2020

Impacts of Price Formation Efforts Considering High Renewable Penetration Levels and System Resource Adequacy Targets

Future markets with very high penetrations of renewable energy could have many low- to zero-cost periods, which would reduce energy revenues for the generation fleet. This can impact the ability of resources that are needed for long-term reliability to recover operating and capital costs. This paper explores the impact of an alternative pricing mechanism, relaxed minimum pricing, on revenue sufficiency on existing and future resource mixes, including those with high penetrations of wind and solar. This study evaluates eight scenarios encompassing three different sensitivity categories: (1) one alternative pricing mechanism versus traditional LMP pricing, (2) high versus low renewable penetration levels, and (3) using a resource mix that has been adjusted to a preset resource adequacy target versus one that contains a full set of resources. Results show renewable penetration has a greater impact on pricing and resulting profits than does the adjusted resource mix, the two pricing methods have modest variations in profits, and prices under the higher renewable penetration case were higher than under low renewable penetration. These conclusions are not intended to be direct predictions for future outcomes but rather to lead to additional research on the impacts of pricing on investment incentives and future resource adequacy targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Testing of Instrumentation and Control Sensors and Cables for Small Modular Reactors

As advanced reactor concepts like small modular reactors (SMRs) progress towards design maturity and commercial deployment, it is important to assess their instrumentation and control (I&C) system sensors and cables to assure their safe, reliable, and efficient operation throughout their service lifetimes. The I&C system of any nuclear power plant (NPP) is the central nervous system of the plant and is made up of field devices such as sensors which are connected via cables to analog and/or digital systems. These systems are responsible for control and/or protection of the plant. The performance of I&C sensors and cables is dependent on several factors, including plant operating conditions. In particular, the plant operation can affect the static (accuracy) and dynamic (response) performance of the sensor, whereas the surrounding ambient environment can impact the remaining useful life of the cables, connectors, and other associated components within I&C sensor circuits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

03 NATURAL GAS↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2021

Mutual Inductance Level Sensor for Use in Liquid Metals

Electromagnetic instrumentation capable of measuring the level of liquid metal has been developed at Argonne National Laboratory’s (ANL) Mechanisms Engineering Test Loop (METL). The mutual inductance level sensor (MILS) utilizes the electromagnetic coupling between two coil conductors and a surrounding liquid metal to determine the level of the liquid metal. Mineral insulated cables wrapped on a stainless steel core provide a durable sensor construction. The use of a sealed stainless steel thimble isolates the sensor from the high-temperature liquid metal, allowing for easy sensor maintenance. Modern digital electronics allow for reliable and accurate operation of the sensor. Two sensor variations have been designed and fabricated, and the first variation has seen extensive testing in a non-sodium testing stand as well as in the high-temperature sodium environment of METL. Electromagnetic finite-element analysis studies have been performed using the COMSOL Magnetic Fields Solver. Regular operation of the MILS will commence following in-situ calibration of the sensors in the METL environment.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Hydropower Modeling Gaps in Planning and Operational Studies

In the evolving power system, where new renewable resources displace continually conventional generation and hydro becomes a single traditional resource that is fully controllable, it is timely to examine hydrogeneration’s role in the power system of the future and its representation in power system operation and planning reliability studies. This paper identifies some of the gaps in hydro-based generation representation in steady-state and dynamic models and provides recommendations for their mitigation.

13 HYDRO ENERGY↗

DOE OSTI2022

Assessment of Acoustic Sensor Application to Structural Health Monitoring of Reactor Components

The objectives of SHM of advanced fission reactors include the following: (1) Maintain safe, reliable, and efficient operation of structures, systems, and components in accordance with design intent; (3) Reduce cost; (4) Improve the comprehensive life management of structures, systems, and components; and (5) Extend the operational life of a power system through retirement for cause. Advanced reactor designs currently being considered are expected to operate at higher temperatures than light water-cooled reactors and to support missions beyond baseload electricity generation. Methods to monitor and detect for these mechanisms will require structural health monitoring (SHM) techniques, with ultrasonic methods an ideal candidate given their widespread use for NDE. This document summarized the state of technology for ultrasonic technologies, as part of an assessment of technology gaps and needed research. The great majority of sensor development to date addresses elevated temperatures and radiation tolerance, and more work is needed in both areas. Little data exists regarding corrosion effects of advanced coolants on sensor materials and couplants. Of high importance is the need for ARDs and regulatory bodies to engage and define what level of SHM is needed for autonomous or semi-autonomous control of reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Recent Beam Stability Analysis for the EIC

This document summarizes work done in WBS 6.02.02 during FY23. Additional details can be found in the EIC overview paper presented at IPAC’23 [1], and the references therein. Progress has been made in the ESR and HSR lattice design, dynamic aperture optimization, and the RCS lattice design. The impedance and Collective effects are progressing and include the impedance optimization process of the vacuum systems for RCS, HSR, and ESR; collective effects studies; collective effects and beam-beam interaction; coupled bunch instabilities and the crab cavities; low-level RF feedback system design and beam-ion instability. The reversed phasing RF system has been numerically studied for the ESR to mitigate Robinson instability and demonstrate reliable stable beam operation. Various codes, including C++, SPACE [2], ELEGANT [3], and Mbtrack2 [4] have been employed to benchmark the results. The simulation of HSR bunch splitting with beam loading has been performed at 275 GeV energy. The evaluation of Beam Position Monitors aimed to validate their expected performance, with the primary objective being to verify their accuracy. Calculations of Electron Polarization in the ESR, RCS, and HSR are showing good progress. In the RCS, preliminary studies indicate excellent polarization transmission over intrinsic spin resonances, achieving over 90% transmission with improved performance compared to the previous lattice. Preliminary simulations in the ESR indicate encouraging results in minimizing depolarization and improving equilibrium polarization.

43 PARTICLE ACCELERATORS↗