Towards robust and scalable dispatch modeling of long-duration energy storage
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Guerra, Omar J.↗
SEARCH · Engineering Papers
Results for “grid optimization”
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Compact two-electron wave function for bond dissociation and Van der Waals interactions: A natural amplitude assessment
Electron correlations in molecules can be divided in short range dynamical correlations, long range Van der Waals type interactions, and near degeneracy static correlations. In this work, we analyze for a one-dimensional model of a two-electron system how these three types of correlations can be incorporated in a simple wave function of restricted functional form consisting of an orbital product multiplied by a single correlation function f (r{sub 12}) depending on the interelectronic distance r{sub 12}. Since the three types of correlations mentioned lead to different signatures in terms of the natural orbital (NO) amplitudes in two-electron systems, we make an analysis of the wave function in terms of the NO amplitudes for a model system of a diatomic molecule. In our numerical implementation, we fully optimize the orbitals and the correlation function on a spatial grid without restrictions on their functional form. Due to this particular form of the wave function, we can prove that none of the amplitudes vanishes and moreover that it displays a distinct sign pattern and a series of avoided crossings as a function of the bond distance in agreement with the exact solution. This shows that the wave function ansatz correctly incorporates the long range Van der Waals interactions. We further show that the approximate wave function gives an excellent binding curve and is able to describe static correlations. We show that in order to do this the correlation function f (r{sub 12}) needs to diverge for large r{sub 12} at large internuclear distances while for shorter bond distances it increases as a function of r{sub 12} to a maximum value after which it decays exponentially. We further give a physical interpretation of this behavior.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
MPC-Based Black Start and Restoration for Resilient DER-Rich Electric Distribution System
Not Available
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Stability Analysis of Parallel Connected Bidirectional WPT System
This paper presents a stability analysis of parallel-connected bi-directional series-series resonant network wireless power transfer (WPT), optimized for Electric Vehicle (EV) charging and vehicle-to-grid (V2G) applications. The study addresses critical stability challenges in systems integrated with diverse distributed energy resources (DERs), including photovoltaics, fuel cells, wind turbines, energy storage systems, and the AC grid. The stability of such integrated DC grid systems is paramount for ensuring reliable operation, particularly under varying power flow conditions and dynamic interactions between parallel WPT systems. The analysis included system impedance characterization, state-space modeling, and open and closed-loop stability evaluations. The results demonstrated that the integration of a robust control architecture effectively mitigates instability risks and supports scalable, efficient operation. This work underscores the converter's adaptability and its potential for large-scale deployment in wireless EV charging infrastructures and integrated DC grid systems.
Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
Heuristic Dynamic Programming for Adaptive Virtual Synchronous Generators
In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage and/or reactive power controllers in virtual inertia control schemes.
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A Robust Controller Design Methodology Addressing Challenges Under System Uncertainty
Not Available
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Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response
In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.
Gao, Wei [Argonne National Laboratory (ANL), Argon↗
A Spherical Active Coded Aperture for 4π Gamma-ray Imaging
Gamma-ray imaging facilitates the efficient detection, characterization, and localization of compact radioactive sources in cluttered environments. Fieldable detector systems employing active planar coded apertures have demonstrated broad energy sensitivity via both coded aperture and Compton imaging modalities. But, planar configurations suffer from a limited field-of-view, especially in the coded aperture mode. In order to improve upon this limitation, we introduce a novel design by rearranging the detectors into an active coded spherical configuration, resulting in a 4pi isotropic field-of-view for both coded aperture and Compton imaging. This work focuses on the low- energy coded aperture modality and the optimization techniques used to determine the optimal number and configuration of 1 cm 3 CdZnTe coplanar grid detectors on a 14 cm diameter sphere with 192 available detector locations.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Tycho 2, Version 0.1
Tycho 2 is a mini-app for testing performance of new algorithms and computational optimizations of neutral particle transport sweeps on 3D tetrahedral, unstructured grids.
Garrett, CharlesKris↗
An Implementation for Transforming a Home Energy Management System to a Multi-agent System
In the United States, 41% of produced energy is consumed by the building sector, i.e. residential and commercial buildings (Building Energy Data Book, Buildings Sector, US Department of Energy, Office of Energy Efficiency and Renewable Energy, Building Technologies Office, 2012. n.d.). The anticipation is that Home Energy Management Systems (HEMS) will support energy efficiency gains through control of the devices in an optimal fashion. New opportunities are offering the ability to integrate grid type controls and the most suitable way to perform these controls is through a multi-agent system (MAS). In this paper, approaches on supporting a HEMS and MAS integration are discussed.
Zandi, Helia↗
Packetized Energy Management: Coordinating Transmission and Distribution (Final Report)
The project Packetized Energy Management (PEM): Coordinating Transmission and Distribution was part of the ARPA-E NODES program from 2015 to 2023. The high-level goal of the project was to develop and demonstrate novel, scalable, and impactful technologies related to the coordination of networked distributed energy resources (DERs). By demonstrating responsive means by which fleets of DERs could be coordinated to enhance grid operation and reliability, the U.S. could accelerate renewable integration and electrification efforts and meet decarbonization goals.
25 ENERGY STORAGE↗
High-Fidelity Self-Learning Tool for Residential Load and Load Flexibility Forecasting
In this project, we developed a set of high-fidelity modeling and optimization algorithms to enable conversion of individual homes into grid-integrated buildings, and a community of homes into a virtual power plant (VPP).
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Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that reduces overall generation costs. However, the incorporation of differential equations that govern the system dynamics makes DED an optimization problem that is computationally prohibitive to solve. In this work, we present a new data-driven approach based on differentiable programming to efficiently obtain offline parametric solutions to the underlying DED problem. In particular, we employ the recently proposed differentiable predictive control (DPC) for offline learning of explicit neural control policies based on identified Koopman operator (KO) model of the system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original optimization-based DED approach on a 9-bus test power grid network.
King, Ethan↗
Safety-assured, real-time neural active fault management for resilient microgrids integration
Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid's network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.
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On the use of co-ordinate stretching in the numeral computation of high frequency scattering
The scattering of the sound of a jet engine by an airplane fuselage is modeled by solving the axially symmetric Helmholtz equation exterior to a long thin ellipsoid. The integral equation method based on the single layer potential formulation is used. A family of coordinate systems on the body is introduced and an algorithm is presented to determine the optimal coordinate system. Numerical results verify that the optimal choice enables the solution to be computed with a grid that is coarse relative to the wavelength.
Bayliss, A.↗
An High Resolution Near-Earth Objects Population Enabling Next-Generation Search Strategies
Over the past decade, the dedicated search for kilometer-size near-Earth objects (NEOs), potentially hazardous objects (PHOs), and potential Earth impactors has led to a boost in the rate of discoveries of these objects. The catalog of known NEOs is the fundamental ingredient used to develop a model for the NEOs population, either by assessing and correcting for the observational bias (Jedicke et al., 2002), or by evaluating the migration rates from the NEOs source regions (Bottke et al., 2002). The modeled NEOs population is a necessary tool used to track the progress in the search of large NEOs (Jedicke et al., 2003) and to try to predict the distribution of the ones still undiscovered, as well as to study the sky distribution of potential Earth impactors (Chesley & Spahr, 2004). We present a method to model the NEOs population in all six orbital elements, on a finely grained grid, allowing us the design and test of targeted and optimized search strategies. This method relies on the observational data routinely reported to the Minor Planet Center (MPC) by the Catalina Sky Survey (CSS) and by other active NEO surveys over the past decade, to determine on a nightly basis the efficiency in detecting moving objects as a function of observable quantities including apparent magnitude, rate of motion, airmass, and galactic latitude. The cumulative detection probability is then be computed for objects within a small range in orbital elements and absolute magnitude, and the comparison with the number of know NEOs within the same range allows us to model the population. When propagated to the present epoch and projected on the sky plane, this provides the distribution of the missing large NEOs, PHOs, and potential impactors.
Tricaico, Pasquale↗
Stabilized Finite Elements in FUN3D
A Streamlined Upwind Petrov-Galerkin (SUPG) stabilized finite-element discretization has been implemented as a library into the FUN3D unstructured-grid flow solver. Motivation for the selection of this methodology is given, details of the implementation are provided, and the discretization for the interior scheme is verified for linear and quadratic elements by using the method of manufactured solutions. A methodology is also described for capturing shocks, and simulation results are compared to the finite-volume formulation that is currently the primary method employed for routine engineering applications. The finite-element methodology is demonstrated to be more accurate than the finite-volume technology, particularly on tetrahedral meshes where the solutions obtained using the finite-volume scheme can suffer from adverse effects caused by bias in the grid. Although no effort has been made to date to optimize computational efficiency, the finite-element scheme is competitive with the finite-volume scheme in terms of computer time to reach convergence.
Anderson, W. Kyle↗
Nuclear hybrid energy infrastructure
The nuclear hybrid energy concept is becoming a reality for the US energy infrastructure where combinations of the various potential energy sources (nuclear, wind, solar, biomass, and so on) are integrated in a hybrid energy system. This paper focuses on challenges facing a hybrid system with a Small Modular Reactor at its core. The core of the paper will discuss efforts required to develop supervisory control center that collects data, supports decision-making, and serves as an information hub for supervisory control center. Such a center will also be a model for integrating future technologies and controls. In addition, advanced operations research, thermal cycle analysis, energy conversion analysis, control engineering, and human factors engineering will be part of the supervisory control center. Nuclear hybrid energy infrastructure would allow operators to optimize the cost of energy production by providing appropriate means of integrating different energy sources. The data needs to be stored, processed, analyzed, trended, and projected at right time to right operator to integrate different energy sources.
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