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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The impacts of convex piecewise linear cost formulations on AC optimal power flow

Despite strong connections through shared application areas, research efforts on power market optimization (e.g., unit commitment) and power network optimization (e.g., optimal power flow) remain largely independent. A notable illustration of this is the treatment of power generation cost functions, where nonlinear network optimization has largely used polynomial representations and market optimization has adopted piecewise linear encodings. This work combines state-of-the-art results from both lines of research to understand the best mathematical formulations of the nonlinear AC optimal power flow problem with piecewise linear generation cost functions. An extensive numerical analysis of non-convex models, linear approximations, and convex relaxations across fifty-four realistic test cases illustrates that nonlinear optimization methods are surprisingly sensitive to the mathematical formulation of piecewise linear functions. The results indicate that a poor formulation choice can slow down algorithm performance by a factor of ten, increasing the runtime from seconds to minutes. Furthermore, these results provide valuable insights into the best formulations of nonlinear optimal power flow problems with piecewise linear cost functions, an important step towards building a new generation of energy markets that incorporate the nonlinear AC power flow model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Determining System Parameters for Optimal Performance of Hybrid DS/FFH Spread-Spectrum

In recent years there has been great interest in using hybrid spread-spectrum (HSS) techniques for commercial applications, particularly in the Smart Grid, in addition to their use in military communications because they accommodate high data rates with high link integrity, even in the presence of significant multipath effects and interfering signals. A highly useful form of this transmission technique for many types of command, control, and sensing applications is the specific code-related combination of standard direct sequence (DS) modulation with "fast" frequency hopping (FFH), denoted hybrid DS/FFH, wherein multiple frequency hops occur within a single data-bit time. In this paper, an optimization problem is formulated that maximizes the DS/FFH communication system performance in terms of probability of bit error and solves for the system design parameters. The objective function is non-convex and can be solved by applying the Karush-Kuhn-Tucker conditions. System design parameters of interest are the length of the DS code sequence, number of frequency hopping channels, number of channels corrupted by wide-band jamming, and number of hops per bit. The proposed formulation takes into account the effects from wide-band and partial-band jamming, multi-user interference and/or varying degrees of Rayleigh and Rician multipath fading. Numerical results are presented to demonstrate the method s viability.

direct sequence↗
NASA NTRSDate not supplied

Improving Kinetic Consistency Across Elastic Model Transitions With Quadratic Inequality Constrained Weighted Least Squares (WLSQI)

Flexible body modeling presents a large challenge in the development of simulations to aid in design of flight control systems for launch and landing vehicles. Typically, the flexible body model is not a single continuous model but are rather discrete sets of Linear Time Invariant (LTI) Finite Element Models (FEM) incremented by propellant levels. This introduces the problem of smoothly transitioning modal and physical states of the vehicle when switching from one FEM to the next. This paper introduces a new approach to optimally transition flexible body states with weighted least squares, building off previous methods.

GN&C↗
DOE OSTI2023

Parallel Memory-Independent Communication Bounds for SYRK

In this paper, we focus on the parallel communication cost of multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK). SYRK requires half the computation of general matrix multiplication because of the symmetry of the output matrix. Recent work (Beaumont et al., SPAA '22) has demonstrated that the sequential I/O complexity of SYRK is also a constant factor smaller than that of general matrix multiplication. Inspired by this progress, we establish memory-independent parallel communication lower bounds for SYRK with smaller constants than general matrix multiplication, and we show that these constants are tight by presenting communication-optimal algorithms. The crux of the lower bound proof relies on extending a key geometric inequality to symmetric computations and analytically solving a constrained nonlinear optimization problem. Here, the optimal algorithms use a triangular blocking scheme for parallel distribution of the symmetric output matrix and corresponding computation.

Communication costs↗
DOE OSTI2023

On relaxations of the max k -cut problem formulations

Here, a tight continuous relaxation is a crucial factor in solving mixed integer formulations of many NP-hard combinatorial optimization problems. The (weighted) max k-cut problem is a fundamental combinatorial optimization problem with multiple notorious mixed integer optimization formulations. In this paper, we explore four existing mixed integer optimization formulations of the max k-cut problem. Specifically, we show that the continuous relaxation of a binary quadratic optimization formulation of the problem is: (i) stronger than the continuous relaxation of two mixed integer linear optimization formulations and (ii) at least as strong as the continuous relaxation of a mixed integer semidefinite optimization formulation. We also conduct a set of experiments on multiple sets of instances of the max k-cut problem using state-of-the-art solvers that empirically confirm the theoretical results in item (i). Furthermore, these numerical results illustrate the advances in the efficiency of global non-convex quadratic optimization solvers and more general mixed integer nonlinear optimization solvers. As a result, these solvers provide a promising option to solve combinatorial optimization problems. Our codes and data are available on GitHub.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-C-204-04: Patient Specific Proton Stopping Powers Estimation by Combining Proton Radiography and Prior-Knowledge X-Ray CT Information

Purpose: The material relative stopping power (RSP) uncertainty is the highest contributor to the range uncertainty in proton therapy. The purpose of this work is to develop a robust and systematic method that yields accurate, patient specific, RSP by combining 1) pre-treatment x-ray CT and 2) daily proton radiograph of the patient. Methods: The method is formulated as a linear least-square optimization problem (min||Ax-B||2). The parameter A represents the pathlength crossed by the proton in each material. The RSPs for the materials (water equivalent thickness (WET)/physical thickness) are denoted by x. B is the proton radiograph expressed as WET crossed. The problem is minimized using a convex-conic optimization algorithm with xi<xi+1 constraint. The Gammex RMI-465 phantom, with 13 material inserts, is used to investigate the optimal RSPs. The proton radiograph (B) is produced using Geant4 simulation. The matrix A is computed by calculating proton trajectories through the CT with the cubic-spline approach. Geant4 RSPs are used as reference and the clinical HU-RSP calibration curve is used as comparison. The convergence is investigated as a function of the number of angles and protons used. The radiographs angle which yield the most accurate RSPs are investigated. Results: Optimization with 9 angles and 104 protons/angle yields precise RSP (<0.75%) for all materials, except plastic lung (1.5%). Average deviation relative to Geant4 RSP is 0.5% and 3.5% using HU-RSP. Using more than two angles or more than 4×103 proton does not further increase the RSP estimate accuracy. The insert distance to the beam initial plane is related to the RSP error for this insert material. Specific filters are applied on the proton angle and energy loss to remove nuclear interaction artefacts. Conclusion: The proposed formulation of the problem with prior knowledge x-ray CT demonstrates serious potential to increase the accuracy of present RSP estimates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-FG-BRB-04: A New Optimization Method for Pre-Treatment Patient-Specific Stopping-Power by Combining Proton Radiography and X-Ray CT

Purpose: The relative stopping power (RSP) uncertainty is the largest contributor to the range uncertainty in proton therapy. The purpose of this work is to develop a robust and systematic method that yields accurate patient specific RSPs by combining pre-treatment X-ray CT and daily proton radiography. Methods: The method is formulated as a penalized least squares optimization (PLSO) problem min(|Ax-B|). The matrix A represents the cumulative path-length crossed in each material computed by calculating proton trajectories through the X-ray CT. The material RSPs are denoted by x and B is the pRad, expressed as water equivalent thickness. The equation is solved using a convex-conic optimizer. Geant4 simulations were made to assess the feasibility of the method. RSP extracted from the Geant4 materials were used as a reference and the clinical HU-RSP curve as a comparison. The PLSO was first tested on a Gammex RMI-467 phantom. Then, anthropomorphic phantoms of the head, pelvis and lung were studied and resulting RSPs were evaluated. A pencil beam was generated in each phantom to evaluate the proton range accuracy achievable by using the optimized RSPs. Finally, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Results: Numerical simulations showed precise RSP (<0.75%) for Gammex materials except low-density lung (LN-300) (1.2%). Accurate RSP have been obtained for the head (µ=−0.10%, 1.5σ=1.12%), lung (µ=−0.33, 1.5σ=1.02%) and pelvis anthropomorphic phantoms (µ=0.12, 1.5σ=0,99%). The range precision has been improved with an average R80 difference to the reference (µ±1.5σ) of −0.20±0.35%, −0.47±0.92% and −0.06±0.17% in the head, lung and pelvis phantoms respectively, compared to the 3.5% clinical margin. Experimental HU-RSP curve have been produced on the CIRS pediatric head. Conclusion: The proposed PLSO with prior knowledge X-ray CT shows promising potential (R80 σ<1.0% over all sites) to decrease the range uncertainty.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Pseudo-spectral control of a novel oscillating surge wave energy converter in regular waves for power optimization including load reduction

The aim of this study is to describe a procedure to maximize the power-to-load ratio of a novel wave energy converter (WEC) that combines an oscillating surge wave energy converter with variable structural components. The control of the power-take-off torque will be on a wave-to-wave timescale, whereas the structure will be controlled statically such that the geometry remains the same throughout the wave period. Linear hydrodynamic theory is used to calculate the upper and lower bounds for the time-averaged absorbed power and surge foundation loads while assuming that the WEC motion remains sinusoidal. Previous work using pseudo-spectral techniques to solve the optimal control problem focused solely on maximizing absorbed energy. This work extends the optimal control problem to include a measure of the surge foundation force in the optimization. The objective function includes two competing terms that force the optimizer to maximize power capture while minimizing structural loads. A penalty weight was included with the surge foundation force that allows control of the optimizer performance based on whether emphasis should be placed on power absorption or load shedding. Results from pseudo-spectral optimal control indicate that a unit reduction in time-averaged power can be accompanied by a greater reduction in surge-foundation force.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Robust and Simple ADMM Penalty Parameter Selection

We present a new method for online selection of the penalty parameter for the alternating direction method of multipliers (ADMM) algorithm. ADMM is a widely used method for solving a range of optimization problems, including those that arise in signal and image processing. In its standard form, ADMM includes a scalar hyperparameter, known as the penalty parameter, which usually has to be tuned to achieve satisfactory empirical convergence. In this work, we develop a framework for analyzing the ADMM algorithm applied to a quadratic problem as an affine fixed point iteration. Using this framework, we develop a new method for automatically tuning the penalty parameter by detecting when it has become too large or small. We analyze this and several other methods with respect to their theoretical properties, i.e., robustness to problem transformations, and empirical performance on several optimization problems. Our proposed algorithm is based on a theoretical framework with clear, explicit assumptions and approximations, is theoretically covariant/invariant to problem transformations, is simple to implement, and exhibits competitive empirical performance.

42 ENGINEERING↗
DOE OSTI2022

Multi-service battery energy storage system optimization and control

Battery energy storage systems (BESS) have become fundamental part of modern power systems due to their capability to provide multiple grid services. As the renewable penetration increases, BESS procurement is also expected to increase where it is envisioned to play a systematic and strategical role in power systems planning and operation. Hence, in this paper we present a multiple grid service procurement and operation for BESS - ranging from energy arbitrage, reserve/regulation services, power factor correction, and demand management. The proposed framework considers an optimal multi-temporal dimension, designed to be operable for both planning and real-time operation. Moreover, non-linearity inherent to BESS services and uncertainty associated to market forecasts variables are addressed using techniques such as polyhedral norms and robust optimization approaches. Here, the developed model is tested using a utility-scaled BESS and the obtained results show the effectiveness of the systematic BESS multi-service planning and operation approach.

25 ENERGY STORAGE↗
DOE OSTI2019

Optimal Operation of Soft Open Points in Active Distribution Networks Under Three-Phase Unbalanced Conditions

The asymmetric integration of distributed generators (DGs) exacerbates the three-phase unbalanced condition in distribution systems. The serious unbalanced operation causes inefficient utilization of network assets and security risks in the system. Soft open point (SOP) is a flexible power electronic device which can achieve accurate active and reactive power flow control to balance the power flow among phases. This paper proposes an SOPs-based operation strategy for unbalanced active distribution networks (ADNs). By regulating the operation of SOPs, the strategy can reduce power losses and simultaneously mitigate the three-phase unbalance of the upper-level grid. Semidefinite programming (SDP) relaxation is advocated to convert the original non-convex, nonlinear optimization model into an SDP formulation, which can be efficiently solved to meet the requirement of rapid adjustment. Case studies are conducted on the modified IEEE 33-node and IEEE 123-node distribution system to verify the effectiveness and efficiency of the proposed strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Computing the Feasible Spaces of Optimal Power Flow Problems

The solution to an optimal power flow (OPF) problem provides a minimum cost operating point for an electric power system. The performance of OPF solution techniques strongly depends on the problem’s feasible space. This paper presents an algorithm that is guaranteed to compute the entire feasible spaces of small OPF problems to within a specified discretization tolerance. Specifically, the feasible space is computed by discretizing certain of the OPF problem’s inequality constraints to obtain a set of power flow equations. All solutions to the power flow equations at each discretization point are obtained using the Numerical Polynomial Homotopy Continuation (NPHC) algorithm. To improve computational tractability, “bound tightening” and “grid pruning” algorithms use convex relaxations to preclude consideration of many discretization points that are infeasible for the OPF problem. Here, the proposed algorithm is used to generate the feasible spaces of two small test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimizing Desalination Operations for Energy Flexibility

Despite the value of energy optimization in desalination processes, modeling dynamic operations for monthly billing periods has remained a computational challenge. This work proposes a framework for energy flexibility optimization, which includes new modeling features for independent operation of parallel skids, start-up delays associated with chemical stabilization, the consideration of industrial energy tariff structures, and inclusion of hourly electrical carbon intensities. This is done using a modular and computationally efficient formulation that guarantees a globally optimal solution with standard optimization solvers. In this study, the approach is demonstrated in two distinct case studies: a seawater desalination plant in Santa Barbara, CA, and an indirect potable reuse facility in San Jose, CA. Trends predicted from the model are validated against operational facility measurements from a demand response shutdown event. Preliminary results show that optimizing energy flexibility can result in 18.51% monthly cost savings over energy efficiency-optimized operation. The value extracted from a facility-wide shutdown during peak electricity price hours is hampered by start-up delays in post-treatment chemical stabilization. In cases in which a facility does not have much excess capacity, using a flow equalization tank or operating over a wide recovery range may be cost-effective.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or functions with block matrix components. ADMM is widely used for solving constrained optimization problems in a variety of fields, including signal and image processing. Implementations of ADMM often utilize a single hyperparameter, referred to as the penalty parameter, which needs to be tuned to control the rate of convergence. However, in problems with multiple constraints, ADMM may demonstrate slow convergence regardless of penalty parameter selection due to scale differences between constraints. Accounting for scale differences between constraints to improve convergence in these cases requires introducing a penalty parameter for each constraint. The proposed method is able to adaptively account for differences in scale between constraints, providing robustness with respect to problem transformations and initial selection of penalty parameters. It is also simple to understand and implement. Our numerical experiments demonstrate that the proposed method performs favorably compared to a variety of existing penalty parameter selection methods.

97 MATHEMATICS AND COMPUTING↗