Active Control of Static and Dynamic Stability in Lean Combustion via Plasma Actuation in a Novel Fuel Injector Design
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Quantum computing in power systems
Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Nonlinear power flow control for networked AC/DC microgrids
A method for designing feedforward and feedback controllers for integration of stochastic sources and loads into a nonlinear networked AC/DC microgrid system is provided. A reduced order model for general networked AC/DC microgrid systems is suitable for HSSPFC control design. A simple feedforward steady state solution is utilized for the feedforward controls block. Feedback control laws are provided for the energy storage systems. A HSSPFC controller design is implemented that incorporates energy storage systems that provides static and dynamic stability conditions for both the DC random stochastic input side and the AC random stochastic load side. Transient performance was investigated for the feedforward/feedback control case. Numerical simulations were performed and provided power and energy storage profile requirements for the networked AC/DC microgrid system overall performance. The HSSPFC design can be implemented in the Matlab/Simulink environment that is compatible with real time simulation/controllers.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Anomaly Detection and Mitigation in FACTS-based Wide-Area Voltage Control Systems using Machine Learning
With the increasing deployment of Flexible AC Transmission System (FACTS) devices in wide-area voltage control systems (WAVCS) for achieving improved voltage stability of bulk power systems, the possibility for cyber attacks on these systems is also increasing. Successful stealthy cyber attacks that are difficult to detect by traditional informational technology (IT)-based cybersecurity solutions or threshold-based bad data detectors can lead to a voltage collapse in power grid. This paper presents the testbed-based attacks implementation and real-time evaluation of machine learning (ML) algorithm for detecting and mitigating stealthy cyber attacks on FACTS-based WAVCS on a hardware-in-the-loop (HIL) testbed. Initially, we discuss the implementation of a fuzzy logic controller (FLC) that controls a Static VAR Compensator (SVC) device deployed in a two-area four-machine Kundur power system for improving transient voltage stability. Later, the ML-based Anomaly Detection and Mitigation (ADM) system is implemented on the cyber-physical HIL testbed to detect and mitigate various stealthy cyber attacks, which are injected in real-time over the wide-area network (WAN). The experimental results show accurate and effective performance of ADM system in detecting and mitigating anomalies while keeping the grid stable and within the system operating limits, as defined by the North America Electric Reliability Corporation (NERC).
24 POWER TRANSMISSION AND DISTRIBUTION↗
Evaluation of nonlinear control performance of air handling units under variable operation conditions using root distribution approach
The control systems at Air handling units (AHUs) have nonlinear nature, resulting potentially in sluggish, aggressive, or oscillatory responses under variable operation conditions. This paper is to develop an approach for evaluating the nonlinear control performance of AHUs under variable operation conditions and for identifying the impact of varying multiple operation parameters which guides controller design. To do so, an operation parameter-based root distribution technique, intended to sketch the paths traced by closed-loop poles as multiple operation parameters are varied, is first introduced. The technique—which may be viewed as an extension of conventional, single-variable root locus—is then applied to a fan speed control system that includes a motor with a variable frequency drive and a supply fan with an air duct under variable fan speed and duct static pressure. Using the technique, the system’s stability region is graphically identified. The system is also found to be aggressive and gradually lose its stability as the fan speed decreases and the duct static pressure increases, which is validated by the field tests. Here, the root distribution approach is an effective tool to evaluate the performance of any nonlinear control systems at AHUs with multiple variable operation parameters without requiring time-domain simulations.
42 ENGINEERING↗
Dynamic Symmetry Breaking in Chiral Magnetic Systems
The Dzyaloshinskii–Moriya interaction (DMI) in magnetic systems stabilizes spin textures with preferred chirality, applicable to next-generation memory and computing architectures. In perpendicularly magnetized heavy-metal/ferromagnet films, the interfacial DMI originating from structural inversion asymmetry and strong spin-orbit coupling favors chiral Néel-type domain walls (DWs) whose energetics and mobility remain at issue. Thus, a new effect is characterized in which domains expand unidirectionally in response to a combination of out-of-plane and in-plane magnetic fields, with the growth direction controlled by the in-plane field strength. These growth directionalities and symmetries with applied fields cannot be understood from static treatments alone. The authors theoretically demonstrate that perpendicular field torques stabilize steady-state magnetization profiles highly asymmetric in elastic energy, resulting in a dynamic symmetry breaking consistent with the experimental findings. This phenomenon sheds light on the mechanisms governing the dynamics of Néel-type DWs and expands the utility of field-driven DW motion to probe and control chiral DWs.
36 MATERIALS SCIENCE↗
Multi-Agent Graph-Attention Deep Reinforcement Learning for Post-Contingency Grid Emergency Voltage Control
Grid emergency voltage control (GEVC) is paramount in electric power systems to improve voltage stability and prevent cascading outages and blackouts in case of contingencies. While most deep reinforcement learning (DRL)-based paradigms perform single agents in a static environment, real-world agents for GEVC are expected to cooperate in a dynamically shifting grid. Moreover, due to high uncertainties from combinatory natures of various contingencies and load consumption, along with the complexity of dynamic grid operation, the data efficiency and control performance of the existing DRL-based methods are challenged. To address these limitations, we propose a multi-agent graph-attention (GATT)-based DRL algorithm for GEVC in multi-area power systems. Here, we develop graph convolutional network (GCN)-based agents for feature representation of the graph-structured voltages to improve the decision accuracy in a data-efficient manner. Furthermore, a cutting-edge attention mechanism concentrates on effective information sharing among multiple agents, synergizing different-sized subnetworks in the grid for cooperative learning. We address several key challenges in the existing DRL-based GEVC approaches, including low scalability and poor stability against high uncertainties. Test results in the IEEE benchmark system verify the advantages of the proposed method over several recent multi-agent DRL-based algorithms.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Deployment of Dynamic Neural Network Optimization to Minimize Heat Rate During Ramping for Coal Power Plants (Final Technical Report)
Much success was achieved throughout the course of this project. A successful implementation of Dynamic Neural Network Optimization (D-NNO) was coupled with Adaptive Predictive Controls (APC) and a novel hardware installation comprised of an advanced sensor network (ASN) measuring mass-weighted averages of flue gas constituents above the horizontal superheater of a coal-fired utility boiler. From 2019 through 2023 (including an extension due to COVID delays), the team was able to prototype, evaluate, deploy, iterate, and ultimately finalize an advanced closed-loop control D-NNO system which demonstrated the ability to: •improve unit efficiency ~2.0% relative to unoptimized operation (represented as total fuel fired per MWh generated) •improve unit NOx emission rates 10%+ beyond static optimization baselines •improve unit temperature stability as much as 58% and on average 12% •improve operating load stability as much as 35% The culmination of this project has generated an advanced methodology of deploying specially designed recurrent neural networks (long short-term memory, gated recurrent unit, encoder-decoder networks, transformers, etc.), customized trajectory planning and closed-loop optimization modules capable of adapting to live electric grid responses and demands, self-tuning and adaptive expert controls constantly adjusting prediction parameters to real-time unit behavior, and a hardware/software package able to reliably calculate net unit heat rate (NUHR) in real-time using flue gas constituents, machine learning, and known combustion relationships. Through this real-time NUHR value, immediate feedback on system adjustments relative to operating efficiency was available, allowing for rapid improvements to system performance. In addition to development and deployment of the advanced D-NNO system, the approach methodology has been readily commercialized through the project platform Griffin Open Systems, LLC, the D-NNO software platform host. Similar methodologies to those developed by this project have already been deployed at 5 other units across the United States, with another 6 implementations scheduled, and more expected. Over the course of the project, multiple academic papers were submitted and accepted for publication within esteemed academic journals, and PhD students were trained and graduated, as well as undergraduate students becoming involved and participating to project objectives.
01 COAL, LIGNITE, AND PEAT↗
Microgrid Modeling for Stability Analysis
Here this document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid (MG) Dynamic Modeling, IEEE Power and Energy Society, Tech. Rep. PES-TR106, 2023. In this paper, the major issues and challenges in microgrid modeling for stability analysis are discussed, and a review of state-of-the-art modeling approaches and trends is presented. In the context of the IEEE 1547 standard, the document covers issues associated with component models for MG dynamic studies and simulations, including generator and grid modeling, full and average converter models, unbalanced and balanced system conditions, dynamic and static loads, protection requirements, and detailed and simplified controls considering communications delays, packet losses, and security issues. Considering the future integration of grids and MGs to form broad integrated networks, a discussion is presented of the use of phasor vis-à-vis electromagnetic transient simulation tools for MG dynamic stability studies, as well as modeling scale-up issues and MG equivalent models. Specifically white-, grey-, and black-box models, are presented. This TF paper and companion report constitute a modeling guide for R&D groups working on developments and standards of MGs with a focus on stability issues.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM
Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Stability Enhancement in Na 0.67 Fe 0.2 Mn 0.8 O 2 Positive Electrodes via Spherical Coprecipitated Hydroxide Precursor Synthesis for Na-Ion Batteries
Mn/Fe-based layered transition metal oxides (LTMOs) are promising positive electrode materials for sodium-ion batteries (SIBs) due to their high abundance, low cost, and stable price fluctuations. At commercial scale, the fabrication of these materials commonly employs coprecipitation of hydroxide precursors, which allows for the scalable synthesis of uniform, dense particles with a tunable morphology. However, the common chelating agent (ammonia) forms unstable complexes with Fe 2+ ions, resulting in uncontrollable particle morphology and poor electrochemical properties. Here, in this study, three chelation strategies (no chelation, ammonia, oxalate) for Fe/Mn-based hydroxides are evaluated. It was found that oxalate chelation produced uniform, dense spherical hydroxide particles while particles via ammonia / no chelate routes exhibited no morphological control. The LTMOs synthesized from the oxalate-chelated hydroxide precursor formed uniform spherical particles, while the other two LTMOs showed greater variation in particle morphology. The oxalate-chelated LTMO electrode exhibited increased cycling stability due to reduced parasitic reactions with the electrolyte, as characterized by static leakage current measurements and electrochemical impedance spectroscopy.
25 ENERGY STORAGE↗
Dynamic, hollow nanotubular networks with superadjustable pH-responsive and temperature resistant rheological characteristics
Recently, the interest in stimuli-responsive and adaptable materials has continuously grown in various fields and applications. For such responsive systems, different triggers, including pH, light, pressure, temperature, and electric field, have been utilized to control dynamics and assembly. Among these, pH is one of the most convenient, energy-efficient, and economic modalities. Besides, plenty of traditional materials have poor thermal and salt stability, limiting their applications. Herein, we report a new design of a pH-responsive viscoelastic supramolecular complex (VSC) based on commplexation of a new long-chain amino-amide and maleic acid. In this study, the system demonstrated a sol–gel-sol transition from pH 2 to 10, with the largest static viscosity occurring at pH 6 (~1000 Pa·s) and the smallest viscosity at pH 4 (~3.3 Pa·s), indicating ~ 300-fold control over the viscosity. For a given concentration, the static viscosity of VSC was about 15 times larger than that of CTAB/NaSal, a well-established dynamic viscoelastic system, and no pH-responsiveness was observed for the traditional system. In addition, the VSC demonstrated a superior temperature tolerance and lower temperature dependence. The potential of these intriguing dynamics viscoelastic systems was evaluated for hydraulic fracturing and enhanced oil recovery applications. Proppant settling velocity of DMAA/MA was 500 ~ 1000 times lower than that of CTAB/NaSal and common traditional polymers. Likewise, the oil recovery percentage could be significantly improved with the utilization of DMAA/MA compared to the CTAB/NaSal (86 % vs 52 %). Aside from applications in hydraulic fracturing and enhanced oil recovery, we anticipate that the intriguing rheological properties of this viscoelastic system can be beneficial for other chemical engineering applications including personal care products, cosmetics, lubricants, and biomedical gels.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Active control of resistive wall mode via modification of external tearing index
Modification of the external tearing index, Δ ext ′ , by magnetic feedback is analytically investigated for the purpose of controlling the resistive plasma resistive wall mode (RP-RWM). The matching method is pursued by deriving expressions for the close-loop Δ ext ′ and by linking it to the counterpart from the inner layer. Various feedback coil configurations are found to generally reduce Δ ext ′ and stabilize the RWM, with either proportional or derivative control. Feedback modification of Δ ext ′ is found to be generally independent of the inner layer resistive interchange index D R , confirming that feedback action primarily modifies the solution in the outer ideal region for the RP-RWM. Exception occurs when either the inner layer favorable curvature effect becomes sufficiently large or the feedback action is sufficiently strong to introduce a rotating RP-RWM in the static plasma, leading to complex-valued close-loop Δ ext ′ . The perturbed magnetic energy dissipation in the outer region, associated with the eddy current in the resistive wall, is identified as the key physics reason for feedback induced complex Δ ext ′ . Similar results are also obtained for active control of the external kink instability, whose open-loop growth rate is significantly reduced by inclusion of the plasma resistivity. Within the single poloidal harmonic approximation, which is most suitable for the matching approach, external active coils combined with poloidal sensors are often found to be more efficient for feedback stabilization of the mode at large proportional gain values. This counter-intuitive result is explained as the lack of (non-resonant) poloidal harmonics for proper description of the feedback coil geometry.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Machine Learning Solutions for a Stable Grid Recovery
Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Thermal stability of 3D interface Cu/Nb nanolaminates
Nanocrystalline alloys are promising structural materials yet lack thermal stability in many cases. Recent work shows that interface structure has an outsize effect on the thermal behavior of nanostructured alloys. Here, this work focuses on the role of controlled heterophase interface structure in the thermal evolution of model Cu/Nb nanolaminates. We introduce 3D interfaces containing nanoscale heterogeneities in all spatial dimensions between Cu and Nb, forming 3D Cu/Nb. TEM, nanoindentation, and DSC are used in tandem to establish thermal stability and to identify shifts in microstructure as a function of static annealing temperature. 3D interfaces are shown to survive annealing to 300 °C for 1 hr., while 3D Cu/Nb microstructure evolves to form low-density and voided regions correlating to the onset of layer pinch-off between 500 and 600 °C annealing temperatures. A diffusivity- and vacancy energetics-based mechanism is developed to explain void formation driven by 3D interface degradation at elevated temperature.
3D interfaces↗
Impact Analysis of Data Integrity Attacks on FACTS-based Wide-Area Voltage Control System
Energy management system (EMS) consists of several wide-area control applications that serve as a backbone for security, stability, and reliability of the power system. Wide-area voltage control system (WAVCS), one of the critical wide-area applications, operates in coordination with local Flexible AC Transmission System (FACTS) devices to provide voltage security and optimal management of active and reactive power resources. Since the WAVCS relies on wide-area communication and data sharing devices, possible cybersecurity vulnerabilities have to be addressed to ensure the closed-loop operation of WAVCS. In this paper, we present a methodology for performing an impact analysis of cyber-attacks in WAVCS cybersecurity. In particular, different types of data integrity attacks, such as malicious tripping, fault replay, and signal altering attacks, are considered, and detailed impact analysis is conducted in a testbed environment using the Kundur's four machine two-area system. For performing an impact analysis, the transient voltage stability of the sensitive bus voltage is studied, followed by the quantitative assessment and severity ranking using the voltage profile index. Our experimental evaluation reveals that the data integrity attacks on control signals exhibit a higher attack severity than on the measurement signals. Further, the severity of these attacks varies with nature (static or dynamic), location, and types of attacks.
24 POWER TRANSMISSION AND DISTRIBUTION↗
On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability
Artificial intelligence technologies have emerged as a game changer not only in specific applications such as image recognition and machine translation but also in many scientific domains. In particular, as deep reinforcement learning (DRL) has shown great success in complex control problems, DRL-based control has been considered as a potential solution to efficiently control and manage building systems. However, broad assessment of DRL-based building control is still required to characterize their pros and cons in comparison with conventional building control methods (e.g., rule-based feedback controls). In this paper, we assessed DRL-based controls with on-policy learning-based algorithms and continuous control actions for cooling control of large office buildings in the summer season to minimize whole-building energy use and occupant discomfort. We compared DRL-based control methods with two baseline control methods: (1) a pre-determined schedule with supply temperature and static pressure setpoints, and (2) advanced reset method that adjusts setpoints based on heuristic rules, i.e., ASHRAE Guideline 36. We also tested the DRL algorithms to evaluate their performances in multiple climate locations. We found that DRL-based control methods outperformed the baseline control methods in terms of energy savings while maintaining a thermal comfort. DRL reduced energy use between ~4%–22% on average compared to the baseline methods, depending on climate location. We also evaluated DRL-based control in terms of control stability and showed that DRL-based methods should address the span of hardware lifetimes in practical operations.
control stability↗
Semiglobal Safety-Filtered Extremum Seeking With Unknown CBFs
We introduce a safe extremum-seeking (Safe ES) algorithm which achieves the minimization of an unknown objective function while ensuring that an unknown, yet measured, control barrier function (CBF) remains above an arbitrarily small negative value for all time. In other words, “practical safety” is maintained during the entire period of convergence to the constrained extremum. Our design is based on quadratic program (QP) CBF style filters for safety, which is applied in an average and estimated sense. Using nonsmooth analysis tools, we guarantee semiglobal practical asymptotic (SPA) stability of the global constrained optimum, practical convergence to the safe set if starting in a condition violating the CBF, and practical safety for all time—semiglobally—if starting in safe set. The safety result of the paper is analogous with modern notions of SPA stability, guaranteeing that, for any small violation of safety, there exist design coefficients which guarantee that such a small violation is not exceeded. The paper outlines a set of sufficient conditions on the barrier and objective functions, and by way of a Lyapunov argument, we demonstrate that nonconvex constrained optimization problems can be solved. We present these results in the setting of a static map and a dynamical system. A simulation example illustrates the results.
97 MATHEMATICS AND COMPUTING↗