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At least 523 records · Page 29

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DOE OSTI2018

Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

Distribution system operators (DSOs) world-wide foresee a rapid roll-out of distributed energy resources. From the system perspective, their reliable and cost effective integration requires accounting for their physical properties in operating tools used by the DSO. This work describes an decomposable approach to leverage the dispatch flexibility of thermostatically controlled loads (TCLs) for operating distribution systems with a high penetration level of photovoltaic resources. Each TCL ensemble is modeled using the Markov Decision Process (MDP). The MDP model is then integrated with a chance constrained optimal power flow that accounts for the uncertainty of PV resources. Since the integrated optimization model cannot be solved efficiently by existing dynamic programming methods or off-the-shelf solvers, this paper proposes an iterative Spatio-Temporal Dual Decomposition algorithm (ST-D2). Finally, we demonstrate the merits of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm

Existing generator parameterization methods, typically developed for large turbine generator units, are difficult to apply to small kW-level diesel generators in microgrid applications. Here, this article presents a model parameterization method that estimates a complete set of kW-level diesel generator parameters simultaneously using only load-step-change tests with limited measurement points. This method provides a more cost-efficient and robust approach to achieve high-fidelity modeling of diesel generators for microgrid dynamic simulation. A two-stage hybrid box-constrained Levenberg-Marquardt (H-BCLM) algorithm is developed to search the optimal parameter set given the parameter bounds. A heuristic algorithm, namely Generalized Opposition-based Learning Genetic Algorithm (GOL-GA), is applied to identify proper initial estimates at the first stage, followed by a modified Levenberg-Marquardt algorithm designed to fine tune the solution based on the first-stage result. The proposed method is validated against dynamic simulation of a diesel generator model and field measurements from a 16kW diesel generator unit.

42 ENGINEERING↗

DOE OSTI2021

An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Fast Tuning-Free Distributed Algorithm for Solving the Network-Constrained Economic Dispatch

With the increasing penetration of distributed energy resources (DERs) and their participation in the electricity market, it becomes more desirable to apply distributed algorithms for resource allocation in order to address the resulting computational and communicational challenges. Most of the existing distributed algorithms for solving the network-constrained economic dispatch (NCED) problem require the tuning of certain auxiliary parameters. As a result, the robustness of these algorithms against the varieties in DERs is greatly undermined. In this paper, a new distributed algorithm, optimality condition consensus (OCC), is proposed to solve the NCED problem by using distributed power flow (DPF) and ratio consensus as fundamental tools. It inherits the advantages of existing distributed algorithms for the NCED problem but removes the need for parameter tuning to improve performance in practice. In conclusion, the effectiveness of the proposed distributed algorithm in terms of efficiency, scalability, and robustness is demonstrated through detailed case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding’s inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memory-efficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment

To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent sub-problems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.

42 ENGINEERING↗

DOE OSTI2020

Landscape responses to intraplate deformation in the Kalahari constrained by sediment provenance and chronology in the Okavango Basin

The structural depression that occupies the Okavango Basin in southern Africa comprises a depo-centre within the intracratonic Kalahari Basin where sediments of the Cenozoic Kalahari Group have accumulated. The Okavango Basin has been formed due to stretching and subsidence at an area of diffused deformation, southwestwards to the main East African Rift System (EARS). Sediments from two full Kalahari Group sequences, located on opposite sides of the Gumare Fault that forms a major fault within the Okavango Basin, were studied to determine their provenance and chronology. Terrestrial Cosmogenic Nuclide (TCN) 26 Al/ 10 Be burial dating was used to constrain a chronostratigraphical framework, and Pb, Sr, and Nd isotopic ratios combined with geochemical and sedimentological analyses were applied to track the source areas of the sediments.Results indicate the following sequence of basin filling: (a) Accumulation between ca. 4–3 Ma during which the currently downthrown (southern) block received a mixture of sediments mostly from the Choma-Kalomo, Ghanzi-Chobe, and Damara terranes, and possibly from the Lufilian Belt and/or Karoo basalts during earlier stages of deposition. Simultaneously, the upthrown (northern) block received sediments from more distant Archean sources in the Zimbabwe and/or Kasai cratons, (b) Hiatus in sedimentation occurred at both sites between ca. 3–2 Ma, (c) Sediments on both sides of the Gumare Fault share a similar source (Angolan Shield) with minor distinct contributions to the downthrown block from the Kasai Craton and local sources input to the upthrown block, and (d) Regional distribution of aeolian sand since at least 1 Ma. The change in source areas is attributed to rearrangements of the drainage systems that were probably linked to vertical crustal movements on the margins of the Okavango Basin. The tectonically induced morphodynamics controlled the landscape evolution of the endorheic basin where vast lakes, wetlands and salt pans have developed through time.

58 GEOSCIENCES↗

DOE OSTI2018

In situ observations of cracking in constrained sintering

Here in this article, we examine the long-standing problem of cracking during constrained sintering of a powder aggregate. Using binder jet 3D printing, we prepare ceramic green bodies in the form of center-notched panels, then use in situ imaging to observe how cracks nucleate and grow from the notch as the material sinters under restraint. Quantitative image analysis allows us to identify important characteristics of the sinter-cracking process, indicating a framework for analyzing the problem and developing methods for avoiding it, including representation of sinter-cracking as a creep crack growth process, use of fracture mechanics parameters to design specimen geometries that do not exceed critical stress intensities, and the possibility of exploiting the inherently ductile nature of sinter-cracking to mitigate damage.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart

In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. In conclusion, the developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

09 BIOMASS FUELS↗

DOE OSTI2017

Extended gamma-ray sources around pulsars constrain the origin of the positron flux at Earth

The unexpectedly high flux of cosmic-ray positrons detected at Earth may originate from nearby astrophysical sources, dark matter, or unknown processes of cosmic-ray secondary production. We report the detection, using the High-Altitude Water Cherenkov Observatory (HAWC), of extended tera–electron volt gamma-ray emission coincident with the locations of two nearby middle-aged pulsars (Geminga and PSR B0656+14). The HAWC observations demonstrate that these pulsars are indeed local sources of accelerated leptons, but the measured tera–electron volt emission profile constrains the diffusion of particles away from these sources to be much slower than previously assumed. Furthermore, we demonstrate that the leptons emitted by these objects are therefore unlikely to be the origin of the excess positrons, which may have a more exotic origin.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

129 I and 247 Cm in meteorites constrain the last astrophysical source of solar r-process elements

The composition of the early Solar System can be inferred from meteorites. Many elements heavier than iron were formed by the rapid neutron capture process (r-process), but the astrophysical sources where this occurred remain poorly understood. We demonstrate that the near-identical half-lives (≃15.6 million years) of the radioactive r-process nuclei iodine-129 and curium-247 preserve their ratio, irrespective of the time between production and incorporation into the Solar System. We constrain the last r-process source by comparing the measured meteoritic ratio 129 I/ 247 Cm = 438 ± 184 with nucleosynthesis calculations based on neutron star merger and magneto-rotational supernova simulations. Moderately neutron-rich conditions, often found in merger disk ejecta simulations, are most consistent with the meteoritic value. Uncertain nuclear physics data limit our confidence in this conclusion.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Condensed Constrained Nonconforming Mortar-Based Approach for Preconditioning Finite Element Discretization Problems

This paper presents and studies an approach for constructing auxiliary space preconditioners for finite element problems using a constrained nonconforming reformulation that is based on a proposed modified version of the mortar method. The well-known mortar finite element discretization method is modified to admit a local structure, providing an element-by-element or subdomain-by-subdomain assembly property. This is achieved via the introduction of additional trace finite element spaces and degrees of freedom (unknowns) associated with the interfaces between adjacent elements or subdomains. The resulting nonconforming formulation and a reduced-via-static-condensation Schur complement form on the interfaces are used in the construction of auxiliary space preconditioners for a given conforming finite element discretization problem. Overall, the properties of these preconditioners are studied and their performance is illustrated on model second order scalar elliptic problems utilizing high order elements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

ALESQP: An Augmented Lagrangian Equality-Constrained SQP Method for Optimization with General Constraints

Here we present a new algorithm for infinite-dimensional optimization with general constraints, called ALESQP. In short, ALESQP is an augmented Lagrangian method that penalizes inequality constraints and solves equality-constrained nonlinear optimization subproblems at every iteration. The subproblems are solved using a matrix-free trust-region sequential quadratic programming (SQP) method that takes advantage of iterative, i.e., inexact linear solvers, and is suitable for large-scale applications. A key feature of ALESQP is a constraint decomposition strategy that allows it to exploit problem-specific variable scalings and inner products. We analyze convergence of ALESQP under different assumptions. We show that strong accumulation points are stationary. Consequently, in finite dimensions ALESQP converges to a stationary point. In infinite dimensions we establish that weak accumulation points are feasible in many practical situations. Under additional assumptions we show that weak accumulation points are stationary. We present several infinite-dimensional examples where ALESQP shows remarkable discretization-independent performance in all of its iterative components, requiring a modest number of iterations to meet constraint tolerances at the level of machine precision. Also, we demonstrate a fully matrix-free solution of an infinite-dimensional problem with nonlinear inequality constraints.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Constrained Local Approximate Ideal Restriction for Advection-Diffusion Problems

Herein this paper focuses on developing a reduction-based algebraic multigrid (AMG) method that is suitable for solving general (non)symmetric linear systems and is naturally robust from pure advection to pure diffusion. Initial motivation comes from a new reduction-based AMG approach, $\ell \text{AIR}$ (local approximate ideal restriction), that was developed for solving advection-dominated problems. Though this new solver is very effective in the advection-dominated regime, its performance degrades in cases where diffusion becomes dominant. This is consistent with the fact that in general, reduction-based AMG methods tend to suffer from growth in complexity and/or convergence rates as the problem size is increased, especially for diffusion-dominated problems in two or three dimensions. Motivated by the success of $\ell \text{AIR}$ in the advective regime, our aim in this paper is to generalize the AIR framework with the goal of improving the performance of the solver in diffusion-dominated regimes. To do so, we propose a novel way to combine mode constraints as used commonly in energy-minimization AMG methods with the local approximation of ideal operators used in $\ell \text{AIR}$. The resulting constrained $\ell \text{AIR}$ algorithm is able to achieve fast scalable convergence on advective and diffusive problems. In addition, it is able to achieve standard low complexity hierarchies in the diffusive regime through aggressive coarsening, something that was previously difficult for reduction-based methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Study of the ${{^{25}}}$Mg(d,p)${{^{26}}}$Mg reaction to constrain the ${{^{25}}}$Al(p,${\gamma }$)${{^{26}}}$Si resonant reaction rates in nova burning conditions

The rate of the 25 Al( p,γ) 26 Si reaction is one of the few key remaining nuclear uncertainties required for predicting the production of the cosmic γ-ray emitter 26 Al in explosive burning in novae. This reaction rate is dominated by three key resonances (J π = 0 + , 1 + and 3 + ) in 26 Si. Only the 3 + resonance strength has been directly constrained by experiment. A high resolution measurement of the 25 Mg(d, p) reaction was used to determine spectroscopic factors for analog states in the mirror nucleus, 26 Mg. A first spectroscopic factor value is reported for the 0 + state at 6.256 MeV, and a strict upper limit is set on the value for the 1 + state at 5.691 MeV, that is incompatible with an earlier ( 4 He, 3 He) study. These results are used to estimate proton partial widths, and resonance strengths of analog states in 26 Si contributing to the 25 Al( p,γ) 26 Si reaction rate in nova burning conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Constraining the nuclear spin distribution using improved 197 Au neutron resonance parameters

New neutron transmission data at resonance energies using a 197 Au sample were measured using an early version of the Device for Indirect Capture Experiments on Radionuclides (DICER), which is under development at the Los Alamos Neutron Science Center (LANSCE). These data were combined with previous neutron transmission and capture data in a simultaneous R-matrix analysis to extract improved neutron resonance parameters for this nuclide. As a result, total radiation widths, Γ γ , were obtained for 33 J=1 and 44 J=2 197 Au+n resonances. Γ γ distributions for these two spins states were compared to distributions calculated according to the nuclear statistical model using published nuclear level density (NLD) and photon strength functions (PSF) measured using the Oslo technique. The calculated distributions were found to be narrower and the average values for the two spins states closer together than the data. The calculation can be brought into agreement with the data by substantial modifications to the spin distribution in 198 Au as a function of excitation energy. As far as we know, the spin distribution currently is otherwise poorly constrained. The modified spin distribution changes the shapes of the NLD and PSF extracted using the Oslo technique and so could have broad implications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

The Constrained NMSSM with right-handed neutrinos

In this article, we demonstrate that the inclusion of right-handed neutrino superfields in the Next-to-Minimal Supersymmetric Standard Model (NMSSM) makes it possible to impose universality conditions on the soft supersymmetry-breaking parameters at the Grand Unification scale, alleviating many of the problems of the so-called Constrained NMSSM. We have studied the renormalization group equations of this model, showing that right-handed neutrinos greatly contribute to driving the singlet Higgs mass-squared parameter negative, which makes it consider-ably easier to satisfy the conditions for radiative electroweak symmetry breaking. The new fields also lead to larger values of the Standard Model Higgs mass, thus making it easier to reproduce the measured value. As a consequence, all bounds from colliders and low-energy observables can be fulfilled in wide areas of the parameter space. However, the relic density in these regions is generally too high requiring some form of late entropy production to dilute the density of the lightest supersymmetric particle.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗