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At least 1,693 records · Page 94

Mesh refinement in finite element analysis by minimization of the stiffness matrix trace

Most finite element packages provide means to generate meshes automatically. However, the user is usually confronted with the problem of not knowing whether the mesh generated is appropriate for the problem at hand. Since the accuracy of the finite element results is mesh dependent, mesh selection forms a very important step in the analysis. Indeed, in accurate analyses, meshes need to be refined or rezoned until the solution converges to a value so that the error is below a predetermined tolerance. A-posteriori methods use error indicators, developed by using the theory of interpolation and approximation theory, for mesh refinements. Some use other criterions, such as strain energy density variation and stress contours for example, to obtain near optimal meshes. Although these methods are adaptive, they are expensive. Alternatively, a priori methods, until now available, use geometrical parameters, for example, element aspect ratio. Therefore, they are not adaptive by nature. An adaptive a-priori method is developed. The criterion is that the minimization of the trace of the stiffness matrix with respect to the nodal coordinates, leads to a minimization of the potential energy, and as a consequence provide a good starting mesh. In a few examples the method is shown to provide the optimal mesh. The method is also shown to be relatively simple and amenable to development of computer algorithms. When the procedure is used in conjunction with a-posteriori methods of grid refinement, it is shown that fewer refinement iterations and fewer degrees of freedom are required for convergence as opposed to when the procedure is not used. The mesh obtained is shown to have uniform distribution of stiffness among the nodes and elements which, as a consequence, leads to uniform error distribution. Thus the mesh obtained meets the optimality criterion of uniform error distribution.

Kittur, Madan G.↗

Feedback-Based Projected-Gradient Method For Real-Time Optimization of Aggregations of Energy Resources: Preprint

This paper develops an online optimization method to maximize the operational objectives of distribution-level distributed energy resources (DERs) while adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claims are established in terms of tracking of the solution of a well-posed time-varying optimization problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Increasing Biomass Production on Limited Land Area Through an Optimal Planting Arrangement

Planting density is a primary consideration in silviculture; however, planting arrangement is often ignored. Most, if not all, forest plantations are arranged in rectangular or square lattices (i.e., grids). Using a simple mathematical model, we investigate the potential influence of planting arrangement on planting density, biomass yield, and rotation period by assuming that efficiently arranging trees is similar to packing congruent circles on a plane. The hexagonal lattice achieves the densest circle packing on a plane; therefore, a hexagonal or triangular lattice arrangement of stems provides the highest planting density for a given spacing. Using packing density to quantify arrangement efficiency, tree crowns in a hexagonal lattice fill approximately 90.7% of available area at initial canopy contact, while tree crowns in a square lattice fill approximately 78.5% of available area at initial canopy contact. The hexagonal lattice permits about a 15% higher density than the square lattice, which allows canopy closure to occur earlier without any change in individual tree growth. Short rotation woody crop (SRWC) systems are excellent candidates under the model's assumptions of level stand with even-age monoculture. If belowground resources are non-limiting, a hexagonal lattice arrangement shortens rotation period and thus optimizes the biomass yield per land area over time. Lastly, higher productivity over time is central to sustainable and efficient use of limited area for bioenergy and biomass products.

09 BIOMASS FUELS↗

Optimal local truncation error method for 3-D elasticity interface problems

The paper deals with a new effective numerical technique on unfitted Cartesian meshes for simulations of heterogeneous elastic materials. Here, we develop the optimal local truncation error method (OLTEM) with 27- point stencils (similar to those for linear finite elements) for the 3-D time-independent elasticity equations with irregular interfaces. Only displacement unknowns at each internal Cartesian grid point are used. The interface conditions are added to the expression for the local truncation error and do not change the width of the stencils. The unknown stencil coefficients are calculated by the minimization of the local truncation error of the stencil equations and yield the optimal second order of accuracy for OLTEM with the 27-point stencils on unfitted Cartesian meshes. A new post-processing procedure for accurate stress calculations has been developed. Similar to basic computations it uses OLTEM with the 27-point stencils and the elasticity equations. The post-processing procedure can be easily extended to unstructured meshes and can be independently used with existing numerical techniques (e.g., with finite elements). Numerical experiments show that at an accuracy of 0.1% for stresses, OLTEM with the new post-processing procedure significantly (by 10 5 -10 9 times) reduces the number of degrees of freedom compared to linear finite elements. OLTEM with the 27-point stencils yields even more accurate results than high-order finite elements with wider stencils.

42 ENGINEERING↗

Control theory based airfoil design using the Euler equations

This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using the potential flow equation with either a conformal mapping or a general coordinate system. The goal of our present work is to extend the development to treat the Euler equations in two-dimensions by procedures that can readily be generalized to treat complex shapes in three-dimensions. Therefore, we have developed methods which can address airfoil design through either an analytic mapping or an arbitrary grid perturbation method applied to a finite volume discretization of the Euler equations. Here the control law serves to provide computationally inexpensive gradient information to a standard numerical optimization method. Results are presented for both the inverse problem and drag minimization problem.

Jameson, Antony↗

On data-driven energy flexibility quantification: A framework and case study

Building energy flexibility is an important resource for a sustainable and resilient power grid, and an important measure to reduce utility costs for building owners. Quantifying energy flexibility for existing buildings can provide critical insights in optimizing their operation. Data-driven methods for building energy modeling and analytics are gaining popularity due to the increasingly available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. However, their application in quantifying the energy flexibility of real buildings is still limited due to the heterogeneous data types and limited data availability. Here, this study proposes a framework for building-level data-driven energy flexibility quantification that considers different levels of data availability and use cases. Two case studies with real building data collected at different scales were conducted to demonstrate the proposed framework for different purposes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Novel Transactive Energy Control Mechanism for Collaborative Networked Microgrids

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes here a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, de-centralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Grid-Interactive Efficient Buildings Technical Report Series: Overview of Research Challenges and Gaps

These reports evaluate state-of-the-art and emerging building technologies that have significant potential to provide grid services. The reports also identify major research challenges and gaps facing the technologies as well as opportunities for technology-specific R&D. The GEB Technical Report Series will help inform and guide BTO's portfolio and serve as a foundational resource for the larger building research community. On-site behind-the-meter generation, battery storage, and electric vehicles are also an important part of the distributed energy resource (DER) optimization strategy for buildings. In general, the component technology reports do not focus on distributed generation or battery storage, but the Whole-Building Controls, Sensors, Modeling, and Analytics report discusses how a building can optimize across all DERs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

SU-F-T-451: Doses to Organs-At-Risk in the Presence and Absence of a 1.5 T Magnetic Field for NSCLC Patients Undergoing SABR

Purpose: To determine whether the electron return effect (ERE) has deleterious effects on lung SABR plans optimized in the presence of an orthogonal 1.5 T magnetic field. Methods: Data from five NSCLC-SABR patients were used. The Dose was modeled with a 2.5 mm dose grid in the presence and absence of a magnetic field using the Monaco (Elekta) TPS with the Monte Carlo GPUMCD (v5.1) algorithm. For each patient, two plans were generated, one using our conventional Elekta Agility linac beam model and another using the Elekta MRI Linac (MRL) model. Both plans were generated on the average CT using similar dose constraints and a 5 mm PTV. The optimization was performed using our clinic’s planning criteria, with normalization of the targets such that their V99% was equal to 99%. The OAR DVHs were compared for each patient. Results: The DVH plots revealed that there were limited differences when optimizing plans in the presence or absence of the magnetic field. The mean of the absolute differences, between the two planning types, in the equivalent uniform doses (EUDs) for the OARs were: 0.3 Gy (range of 0.0 - 1.0 Gy) for the esophagus, 0.6 Gy (range of 0.1 – 1.9 Gy) for the heart, 0.5 Gy (range of 0.2 – 0.8 Gy) for the lungs, and 0.6 Gy (range of 0.2 – 1.5 Gy) for the spinal canal. Regarding the maximum doses to the serial organs, the mean of the differences were 3.0 Gy (esophagus) and 0.9 Gy (spinal canal). No trends in the differences were observed. Conclusion: This study has demonstrated that there were no major differences between plans optimized using a conventional linac and those optimized using an MRI linac with an orthogonal 1.5 T magnetic field. This is attributed to the consideration of the ERE in the optimization. This project was made possible with the financial support of Elekta.

60 APPLIED LIFE SCIENCES↗

Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids

The increasing deployment of distributed energy resources (DERs) and microgrids (MGs) in power distribution systems has enabled the adjustment of reactive power consumption as seen at the substation, which can be used to provide voltage support for the bulk power system (BPS). Leveraging this new capability will provide greater resiliency to the power system as a whole. Here, the goal of this paper is to develop and compare three different algorithms, namely distributed optimal power flow, distributed consensus algorithm, and fully decentralized collaborative autonomy for unbalanced distribution systems for microgrid coordination. These algorithms use networked MGs to support the BPS voltage when a contingency at the bulk grid results in abnormally low voltages, which may be a precursor to voltage collapse. Our comparative analysis includes both qualitative and quantitative assessments of the three algorithms and a discussion of the trade-offs between the decentralized and distributed methods in normal and disrupted conditions. Each algorithm was evaluated on the modified IEEE 13-bus system and a real power distribution system at Chattanooga, Tennessee, that encompasses more than 4500 buses. Each algorithms excels differently and may be suited for different scenarios depending on the condition, operations, and priorities of the power and communication systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Combining model-based and model-free methods for stochastic control of distributed energy resources

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. Here, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Fast, Dynamic, and Scalable Algorithms for Large-Scale Constrained Optimization

Scientists and engineers constantly aim to optimize an objective subject to physical, environmental, and/or resource constraints. The technique of using mathematical models to formulate and find real solutions of such problems is known as mathematical optimization, a process that has become invaluable for design and discovery in numerous scientific fields. This project has focused on the design, analysis, and implementation of high-performance computing algorithms for solving cutting-edge optimization problems. These include problems that involve (1) data uncertainties, such as those in the future supply, demand, and capacity of a given power system, (2) extreme numbers of alternatives, such as in the design of electrical power grids to avoid network vulnerabilities, and (3) real-time decisions, such as in the control of chemical reactors. The key features of the new algorithms are that they are fast, dynamic, and scalable to meet the computational requirements of scientists and researchers working to optimize large-scale, complex systems. The algorithms that have been designed, analyzed, and tested as part of the project can be characterized based on the types of optimization problems they have been designed to solve. Much of the work has focused on algorithms for solving large-scale constrained optimization problems. Contributions have also been made in the design of algorithms for unconstrained optimization, with some specialized methods for solving problems that have inherent nonsmoothness. Contributions have also been made in areas that have grown to prominence in the field over the five-year award period. These include the design of algorithms for solving large-scale stochastic optimization problems, such as those that arise in large-scale machine learning, and in complexity analyses for solving nonconvex problems, which has been a focal point of much recent work in continuous optimization. Overall, the project has supported work that has led to almost 30 articles that have been published or accepted to top journals in the field of mathematical optimization.

97 MATHEMATICS AND COMPUTING↗

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A novel system for ammonia-based sustainable energy and agriculture: Concept and design optimization

Here this work presents a new vision for sustainable energy supply and agriculture: an ammonia-based sustainable energy agriculture (ABSEA) system. This intensified system uses renewables to produce ammonia both as fertilizer and as fuel for tractors and grain drying, to meet local electrical power demand and to provide predictable, consistent power export to the grid. The conceptual design of this system exploits synergies between ammonia production and energy supply to allow for economic viability, specifically time-varying chemical production as well as the potential of ammonia and hydrogen as energy storage media. A framework for combined optimization of the ABSEA design and its inherently time-varying operating schedule is proposed with a view on further improving economic competitiveness. A case study which considers the use of wind turbines to meet ammonia fertilizer and fuel demands of 40,300 kg/year and an annual hourly average power demand of 985 kW demonstrates the promise of the ABSEA system and the associated design-scheduling optimization framework. The annual net present cost is approximately $\$$75,000, which corresponds to an emissions reduction cost of $\$$17.60/tonCO2. The ABSEA optimal design uses both hydrogen and ammonia, but not a battery, for energy storage, illustrating their efficacy in this synergistic application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Design and Evaluation of a Secure Virtual Power Plant.

For three years, Sandia National Laboratories, Georgia Institute of Technology, and University of Illinois at Urbana-Champaign investigated a smart grid vision in which renewable-centric Virtual Power Plants (VPPs) provided ancillary services with interoperable distributed energy resources (DER). This team researched, designed, built, and evaluated real-time VPP designs incorporating DER forecasting, stochastic optimization, controls, and cyber security to construct a system capable of delivering reliable ancillary services, which have been traditionally provided by large power plants or other dedicated equipment. VPPs have become possible through an evolving landscape of state and national interconnection standards, which now require DER to include grid-support functionality and communications capabilities. This makes it possible for third party aggregators to provide a range of critical grid services such as voltage regulation, frequency regulation, and contingency reserves to grid operators. This paradigm (a) enables renewable energy, demand response, and energy storage to participate in grid operations and provide grid services, (b) improves grid reliability by providing additional operating reserves for utilities, independent system operators (ISOs), and regional transmission organization (RTOs), and (c) removes renewable energy high-penetration barriers by providing services with photovoltaics and wind resources that traditionally were the jobs of thermal generators. Therefore, it is believed VPP deployment will have far-reaching positive consequences for grid operations and may provide a robust pathway to high penetrations of renewables on US power systems. In this report, we design VPPs to provide a range of grid-support services and demonstrate one VPP which simultaneously provides bulk-system energy and ancillary reserves.

24 POWER TRANSMISSION AND DISTRIBUTION↗

PLUM: Parallel Load Balancing for Unstructured Adaptive Meshes

Dynamic mesh adaption on unstructured grids is a powerful tool for computing large-scale problems that require grid modifications to efficiently resolve solution features. Unfortunately, an efficient parallel implementation is difficult to achieve, primarily due to the load imbalance created by the dynamically-changing nonuniform grid. To address this problem, we have developed PLUM, an automatic portable framework for performing adaptive large-scale numerical computations in a message-passing environment. First, we present an efficient parallel implementation of a tetrahedral mesh adaption scheme. Extremely promising parallel performance is achieved for various refinement and coarsening strategies on a realistic-sized domain. Next we describe PLUM, a novel method for dynamically balancing the processor workloads in adaptive grid computations. This research includes interfacing the parallel mesh adaption procedure based on actual flow solutions to a data remapping module, and incorporating an efficient parallel mesh repartitioner. A significant runtime improvement is achieved by observing that data movement for a refinement step should be performed after the edge-marking phase but before the actual subdivision. We also present optimal and heuristic remapping cost metrics that can accurately predict the total overhead for data redistribution. Several experiments are performed to verify the effectiveness of PLUM on sequences of dynamically adapted unstructured grids. Portability is demonstrated by presenting results on the two vastly different architectures of the SP2 and the Origin2OOO. Additionally, we evaluate the performance of five state-of-the-art partitioning algorithms that can be used within PLUM. It is shown that for certain classes of unsteady adaption, globally repartitioning the computational mesh produces higher quality results than diffusive repartitioning schemes. We also demonstrate that a coarse starting mesh produces high quality load balancing, at a fraction of the cost required a fine initial mesh. Results indicate that our parallel load balancing strategy will remain viable on large numbers of processors.

Oliker, Leonid↗

Foresee: A user-centric home energy management system for energy efficiency and demand response

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗