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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Operations research investigations of satellite power stations

A systems model reflecting the design concepts of Satellite Power Stations (SPS) was developed. The model is of sufficient scope to include the interrelationships of the following major design parameters: the transportation to and between orbits; assembly of the SPS; and maintenance of the SPS. The systems model is composed of a set of equations that are nonlinear with respect to the system parameters and decision variables. The model determines a figure of merit from which alternative concepts concerning transportation, assembly, and maintenance of satellite power stations are studied. A hybrid optimization model was developed to optimize the system's decision variables. The optimization model consists of a random search procedure and the optimal-steepest descent method. A FORTRAN computer program was developed to enable the user to optimize nonlinear functions using the model. Specifically, the computer program was used to optimize Satellite Power Station system components.

Cole, J. W.↗
DOE OSTI2020

Experimental Validation of Approximate Dynamic Programming Based Optimization and Convergence on Microgrid Applications

Stochastic optimization can better address uncertainties in power system problems. However, when state space and action space become large, many existing approaches become computationally expensive and even infeasible. Approximate dynamic programming (ADP) attracts researchers’ attention as a powerful tool for solving power system optimization problems with reduced computational cost. In this paper, in light of the existing literature, we investigate how the ADP approach with post-decision value function approximation converges to the nearly optimal solution with improved computational speed and experimentally validate the performance of the approach for a microgrid energy optimization problem. The approximation error versus the number of iteration is studied for convergence analysis of the post-decision ADP. A flowchart is provided to illustrate the proposed ADP algorithm for a microgrid energy optimization problem. The performance of ADP and dynamic programming (DP) is compared in terms of optimization error and computational time. It has found that the post-decision ADP approach can achieve competitive optimality with improved computational speed compared to the traditional DP.

Das, Avijit↗
DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗
DOE OSTI2021

Convex Optimization of Integrated Power-Gas Energy Flow Model With Applications to Probabilistic Energy Flow

Energy flow calculation is a fundamental problem of the integrated power and gas system (IPGS) operation and planning. However, the nonlinear gas flow model introduces major challenges to the energy flow calculation. In this paper, we propose a tractably convex optimization model to solve the energy flow problem in IPGSs. It is demonstrated that the proposed optimization model has the same optimal solution as the original nonlinear steady energy flow model. Also, piecewise linearization is adopted to tightly linearize the nonlinear objective function of the model, which transforms the formulated convex optimization into a linear program one. Thus, the computation complexity of the proposed energy flow model is significantly reduced as compared with the existing methods. In addition, the proposed model can be extended to probabilistic energy flow estimation. Extensive case studies are conducted to validate the effectiveness of the proposed energy flow model using three IPGSs.

42 ENGINEERING↗
DOE OSTI2021

Design and Metamodel-Based Optimization of a High Power Density Wound Field Traction Motor

Traction motors onboard electric vehicles (EVs) are faced with ever-increasing power density requirements and cost reduction challenges. Although permanent magnet (PM) traction motors have been favored for their potential of higher power densities, the price and supply volatilities of PM materials are the major drawbacks. Instead of a single load point, the performance of a traction motor needs to be optimized over various combinations of torque and speed to represent real-world vehicular driving scenarios, which tends to be a time-consuming task with direct optimizations. In this paper, as a PM-free solution, a wound field synchronous motor (WFSM) is designed and optimized using a metamodel-based approach to maximize its efficiency over a custom set of load points, while utilizing a fully per-unitized geometry template. The workflow has proven to be timesaving and the selected design is prototyped.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

The use of multi-objective optimization to improve the design process of nuclear power plant systems

Multi-objective optimization is proposed to address the problem of design optimality in the nuclear power plant design process. This is done via the creation/development of the Optimization and Preference Tool for the Improvement of Nuclear Systems (OPTIONS). This work details applications of multi-objective optimization methods (in Python 3) to two different nuclear system design problems: the flash-Rankine power conversion system (PCS) of the I 2 S-LWR and the Passive Endothermic Reaction Cooling System (PERCS) attached to a standard PWR primary system (in RELAP5). The PCS design problem is first solved for three configurations using a specific particle swarm optimization method, achieving a thermodynamic efficiency of 34.81%. A new method, the Mixed-Integer Non-dominated Sorting Genetic Algorithm (MI-NSGA), is created and then used, solving a PCS superstructure of 22 configurations and achieving an efficiency of 35.63%. Finally, the OPTIONS tool is used to optimize the PERCS, improving cost, core temperature control, and cooling longevity.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

National Marine Renewable Energy Center Upgrades - LUPA

The data provided is part of a power take off damping optimization study. The power take off damping coefficient was swept from 0 to approximately 7000 N/m/s during a single regular wave test with a real time control of the motor/generator. The generated power from the LUPA (Lab Upgrade Point Absorber) wave energy converter is reported by the motor drive in watts. The csv files in this submission are the corresponding raw time series outputs for each mode of operation of LUPA (one body heave only, two body heave only, and two body six degrees of freedom). Data comes from testing in the Large WaveFlume (LWF) at the O.H. Hinsdale Wave Research Laboratory in Corvallis, OR.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Modelling of a Flow-Induced Oscillation, Two-Cylinder, Hydrokinetic Energy Converter Based on Experimental Data

The VIVACE Converter consists of cylindrical oscillators in tandem subjected to transverse flow-induced oscillations (FIOs) that can be improved by varying the system parameters for a given in-flow velocity: damping, stiffness, and in-flow center-to-center spacing. Compared to a single isolated cylinder, tandem cylinders can harness more hydrokinetic energy due to synergy in FIO. Experimental and numerical methods have been utilized to analyze the FIO and energy harnessing of VIVACE. A surrogate-based model of two tandem cylinders is developed to predict the power harvesting and corresponding efficiency by introducing a backpropagation neural network. It is then utilized to reduce excessive experimental or computational testing. The effects of spacing, damping, and stiffness on harvested power and efficiency of the established prediction-model are analyzed. At each selected flow velocity, optimization results of power harvesting using the prediction-model are calculated under different combinations of damping and stiffness. The main conclusions are: (1) The surrogate model, built on extensive experimental data for tandem cylinders, can predict the cylinder oscillatory response accurately. (2) Increasing the damping ratio range from 0–0.24 to 0–0.30 is beneficial for improving power efficiency, but has no significant effect on power harvesting. (3) In galloping, a spacing ratio of 1.57 has the highest optimal harnessed power and efficiency compared with other spacing values. (4) Two tandem cylinders can harness 2.01–4.67 times the optimal power of an isolated cylinder. In addition, the former can achieve 1.46–4.01 times the efficiency of the latter. (5) The surrogate model is an efficient predictive tool defining parameters of the Converter for improved energy acquisition.

backpropagation neural network↗
DOE OSTI2014

Simultaneous recovery of vanadium and nickel from power plant fly-ash: Optimization of parameters using response surface methodology

Highlights: • Leaching of vanadium and nickel from fly ash (14.43% V and 5.19% Ni) in sulfuric acid was performed. • Optimization of leaching parameters was carried out using a response surface methodology. • Using optimum conditions, 94.28% V and 81.01% Ni “actual recovery” was obtained. - Abstract: Simultaneous recovery of vanadium (V) and nickel (Ni), which are classified as two of the most hazardous metal species from power plant heavy fuel fly-ash, was studied using a hydrometallurgical process consisting of acid leaching using sulfuric acid. Leaching parameters were investigated and optimized in order to maximize the recovery of both vanadium and nickel. The independent leaching parameters investigated were liquid to solid ratio (S/L) (5–12.5 wt.%), temperature (45–80 °C), sulfuric acid concentration (5–25 v/v%) and leaching time (1–5 h). Response surface methodology (RSM) was used to optimize the process parameters. The most effective parameter on the recovery of both elements was found to be temperature and the least effective was time for V and acid concentration for Ni. Based on the results, optimum condition for metals recovery (actual recovery of ca.94% for V and 81% for Ni) was determined to be solid to liquid ratio of 9.15 wt.%, temperature of 80 °C, sulfuric acid concentration of 19.47 v/v% and leaching time of 2 h. The maximum V and Ni predicted recovery of 91.34% and 80.26% was achieved.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Nuclear design of small-sized high temperature gas-cooled reactor for developing countries

Japan Atomic Energy Agency (JAEA) has started a conceptual design of a small-sized HTGR with 50 MW thermal power (HTR50S), which is a first-of-a-kind commercial or demonstration plant of a small-sized HTGR to be deployed in developing countries such as Kazakhstan in the 2020's. The nuclear design of the HTR50S is performed by upgrading the proven technology of the High Temperature Engineering Test Reactor (HTTR) to reduce the cost for the construction. In the HTTR design, twelve kinds of fuel enrichment was used to optimize the power distribution, which is required to make the maximum fuel temperature below the thermal limitation during the burn-up period. However, manufacture of many kinds of fuel enrichment causes increase of the construction cost. To solve this problem, the present study challenges the nuclear design by reducing the number of fuel enrichment to as few as possible. The nuclear calculations were performed with SRAC code system whose validity was proven by the HTTR burn-up data. The calculation results suggested that the optimization of the power distribution was reasonably achieved and the maximum fuel temperature was kept below the limitation by using three kinds of fuel enrichment. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV

High penetration of grid-edge, inverter-based photovoltaic (PV) can cause significant voltage fluctuations not only at the distribution but also at the sub-transmission levels due to PV output intermittency. Traditional reactive power planning approaches do not consider intermittency, nor the possibility of coordinating the control of existing and future volt-ampere reactive resources. This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high PV penetration and intermittency with a minimum investment cost. The planning tool coordinates with an optimization-based volt-var operational tool for: a) modeling the coordination of all existing var assets in both sub-transmission and distribution systems to reduce the need of new equipment, and b)selecting a set of scenarios with voltage violations, derived from PV intermittency c) testing the final investment decision. The tool obtains an investment need for each intermittency scenario with a proposed optimal power-flow framework with efficient techniques to handle a high number of discrete variables. Two options are provided for final planning decision: i) a conservative direct combination of investment need solutions and ii) a machine learning-based selection of representative investment needs at most time steps. The final investment decision options are verified using a realistic large-scale sub-transmission system and 5-minute PV and load data. The results show a significant voltage performance improvement with a lower investment cost for additional var equipment compared to conventional approaches.

14 SOLAR ENERGY↗
DOE OSTI2012

Optimal control system design for IGCC power plants with CO2 capture

Designing an optimal control system for an integrated gasification combined cycle (IGCC) power plant with CO2 capture addresses the challenge of efficiently operating and controlling a coal-fed IGCC plant with the desired extent of CO2 capture in the face of disturbances without violating operational and environmental constraints. The control system design needs to optimize a desired scalar objective function while satisfying all the operational and environmental constraints in the presence of measured and unmeasured disturbances. Various objective functions can be considered for the control system design such as maximization of profit, maximization of the power produced, or minimization of the auxiliary power. The design of such a control system makes the plant suitable to play an active role in the smart grid era as the plant will have the required agility. In addition, other penalty function(s) such as emission penalties for CO2 or other criteria pollutants can be considered in the framework as well as losses associated with any hydrogen or carbon monoxide loses. The proposed control system design is performed in two stages. In the first stage, a top-down analysis is performed to generate a list of controlled, manipulated, and disturbance variables considering a scalar operational objective and other process constraints. In the second stage, a bottom-up approach for simultaneous design of the control structure and the controllers is used. In this paper, the first stage of the two-stage approach is applied to the IGCC’s acid gas removal (AGR) process which removes both H2S and CO2 from the shifted synthesis gas. While these results are still preliminary, they demonstrate the application of the proposed approach for a commercial-scale plant and show some interesting results related to controlled variable selection. Such an approach can be followed not only to design control systems for new power plants, but also to retrofit control systems for existing plants with suitable modifications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Robust Distribution Network Reconfiguration

We propose a two-stage robust optimization model for the distribution network reconfiguration problem with load uncertainty. The first-stage decision is to configure the radial distribution network and the second-stage decision is to find the optimal a/c power flow of the reconfigured network for given demand realization. We solve the two-stage robust model by using a column-and-constraint generation algorithm, where the master problem and subproblem are formulated as mixed-integer second-order cone programs. Computational results for 16, 33, 70, and 94-bus test cases are reported. We find that the configuration from the robust model does not compromise much the power loss under the nominal load scenario compared to the configuration from the deterministic model, yet it provides the reliability of the distribution system for all scenarios in the uncertainty set.

distribution network↗
NASA NTRS1975

Development of a solar-powered residential air conditioner: System optimization preliminary specification

Investigations aimed at the optimization of a baseline Rankine cycle solar powered air conditioner and the development of a preliminary system specification were conducted. Efforts encompassed the following: (1) investigations of the use of recuperators/regenerators to enhance the performance of the baseline system, (2) development of an off-design computer program for system performance prediction, (3) optimization of the turbocompressor design to cover a broad range of conditions and permit operation at low heat source water temperatures, (4) generation of parametric data describing system performance (COP and capacity), (5) development and evaluation of candidate system augmentation concepts and selection of the optimum approach, (6) generation of auxiliary power requirement data, (7) development of a complete solar collector-thermal storage-air conditioner computer program, (8) evaluation of the baseline Rankine air conditioner over a five day period simulating the NASA solar house operation, and (9) evaluation of the air conditioner as a heat pump.

Rousseau, J.↗
NASA NTRSDate not supplied

Supervisory On-line Optimal Control of an Electric Power Microgrid Design for Lunar Habitation

The following article describes an optimal control algorithm for the operation and study of an electric microgrid designed to power a lunar habitat. A photovoltaic (PV) generator powers the habitat and the presence of predictable lunar eclipses necessitates a system to prioritize and control loads within the microgrid. The algorithm consists of a reduced order model (ROM) that describes the microgrid, a discretization of the equations that result from the ROM, and an optimization formulation that controls the microgrid’s behavior. In order to validate this approach, the paper presents results from simulation based on lunar eclipse information and a schedule of intended loads.

Joseph Young↗
DOE OSTI2024

FIDVR Capability of Hybrid Grid-Forming PV Power Plants During Feeder Restoration

Grid-forming inverters must optimally transfer power from dc-coupled photovoltaic arrays and batteries into an ac grid. Further, they must be able to restore single-phase induction motors (SPIMs) and withstand fault-induced delayed voltage-recovery (FIDVR) events. These resilience and reliability challenges are addressed here by: (i) engineering a controller to optimally operate dc-coupled hybrid resources; (ii) modeling residential air-conditioning compressors for restoration/FIDVR studies; and (iii) analyzing SPIM thermal-relay performance under limited inverter currents and designing an electronic protection for stalled SPIMs. Furthermore, these contributions are demonstrated via electromagnetic-transient simulations and can be helpful to understand recommendations by the North American Electric Reliability Corporation.

14 SOLAR ENERGY↗