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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The importance of independent replication of treatments in plant science

Among other foci, the Journal of Experimental Botany aims to advance understanding of plant environment interactions, including abiotic stress, mineral nutrition, and the response of plants to global change. Advancing understanding in these areas often requires manipulation of the growth environment. Experiments range in scale and include: growth chambers (Yiotis et al., 2020), glasshouses (Rho et al., 2019), whole plant chambers (Sharwood et al., 2017), and open-air field experiments (Ruiz-Vera et al., 2020). The application of environmental manipulations can be constrained by available resources, operational costs, and time. In designing experiments, researchers attempt to balance logistical challenges with the desire to address scientific questions and maximize statistical power.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Measurement of the energy-differential 35 Cl( n, p 0 ) 35 S cross section via the ratio with 6 Li( n, α ) 3 H

Knowledge of the neutron-induced 35 Cl ⁢(n, x) cross sections is vital to the design and certification of molten chloride fast reactors (MCFRs) since the 35 Cl (n, p 0 ) 35 S reaction is believed to be a significant reactor poison. However, recently published measurements are inconsistent with each other and with evaluation. Here, the goal of this work is to measure the 35 Cl (n, p 0 ) reaction cross section using a technique that is different from recent measurements. The experiment was conducted at Lawrence Berkeley National Laboratory's (LBNL) 88-Inch Cyclotron using thick target deuteron breakup from a 14 MeV deuteron beam. Energy-differential 35 Cl (n, p 0 ) 35 S cross sections were obtained via ratio with the 6 Li (n, a)⁢ 3 H reaction using an active target experiment with a Cs 2 ⁢LiYCl 6 (CLYC) scintillator. The 35 Cl (n, p 0 ) reaction cross section was measured from 2.02 to 7.46 MeV. The results are consistent with Kuvin et al., confirming a roughly 50% reduction in magnitude relative to the ENDF/B-VIII.0 evaluation. These data provide new insight into the role of natural Cl as an MCFR poison. The reduction of the 35 Cl (n, p 0 ) reaction cross section compared to evaluation suggests that MCFR criticality is less sensitive to Cl enrichment. This may in turn reduce building and operating costs since isotope separation may not be needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Small-Signal Stability Constrained Optimal Power Flow of Inverter-Dominated Power Systems with Flexible Operation Mode Selection

Given the intermittence and low inertia nature of inverter-based resources (IBRs), modern power systems with high penetration of IBRs challenge the conventional optimal power flow (OPF) analysis and the system may experience unexpected failures if stability constraints are not incorporated. This study proposes a small-signal stability-constrained OPF (SSSC-OPF) with flexible operation mode selection between grid-forming (GFM) and grid-following (GFL) modes for IBRs to address these challenges. The approach aims to maintain system stability with a sufficient stability margin while minimizing operation costs. The effectiveness of the proposed method is validated through extensive case studies on the IEEE 14-bus system. The results demonstrate that the proposed method is able to support system-level power flow analysis, reduce generation costs, and ensure stability under various disturbances.

grid-following↗
DOE OSTI2022

Optimal Coordination of Distributed Energy Resources Using Deep Deterministic Policy Gradient

Recent studies showed that reinforcement learning (RL) is a promising approach for coordination and control of distributed energy resources (DER) under uncertainties. Many existing RL approaches, including Q-learning and approximate dynamic programming, are based on lookup table methods, which become inefficient when the problem size is large and infeasible when continuous states and actions are involved. In addition, when modeling battery energy storage system (BESS), the loss of life is not reasonably considered into the decision-making process. This paper proposes an innovative deep RL method for DER coordination considering BESS degradation. The proposed deep RL is designed based on an adaptive actor-critic architecture and employs an off-policy deterministic policy gradient method for determining the dispatch operation that minimizes the operation cost and BESS life loss. Case studies were performed to validate the proposed method and demonstrate the effects of incorporating degradation models into control design.

Das, Avijit↗
DOE OSTI2024

Enhancing Power Distribution System Resilience with Fusion-GNN: A Dynamic Graph Representation Learning Approach

This paper explores the applications of Fusion Graph Neural Network (FuGNN) on power distribution systems. FuGNN effectively models dynamic networks with evolving topology and features. Applied to power system network reconfiguration, FuGNN demonstrates its feasibility in optimizing switch configurations to minimize unserved loads and operational costs during extreme events. Additionally, FuGNN supports various downstream tasks, such as node feature prediction, further enhancing its versatility and applicability in power system resilience.

Liu, Boming↗
DOE OSTI2025

A New Design Consideration for Bidirectional Resonant Converters

This paper introduces innovative design considerations for a bidirectional resonant converter, optimized for Electric Vehicle (EV) charging and vehicle-togrid (V2G) applications. By eliminating the need for complex voltage or current gain functions, the proposed design greatly simplifies the development process, making it more accessible for manufacturers and system integrators. This approach not only reduces engineering time and resource investment but also accelerates time-to-market, giving stakeholders a competitive edge in rapidly expanding EV infrastructure markets. In addition, the streamlined design ensures soft switching at maximum output power, leading to higher efficiency and improved durability, which translates to lower operating costs and a longer service life for EV charging equipment. These benefits provide a clear pathway to more cost-effective, scalable, and sustainable EV charging and V2G solutions, enhancing value for stakeholders by facilitating smoother integration into the grid and supporting energy resilience. To validate the benefits, a 60 kW bidirectional charger with a flexible input range of 700−900VDC and an output range of 400−1250VDC was developed, underscoring the design's adaptability and potential for large-scale implementation.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2021

Peak Reduction in a Residential Community Through Bayesian Optimization of Transactive Control Signals

Daily residential power consumption in aggregate tends to have a large peak that places stress on the distribution system and induces high operational costs. This work shows that coordinated transactive control of noncritical loads within a residential community or microgrid can help to alleviate peak-time stress on the distribution system by flattening the aggregate load curve. Treating the load forecaster as a high-fidelity, expensive black-box function, a new algorithm utilizing Bayesian optimization (BO) is proposed to achieve the best solution under uncertainty with minimal computing effort. The proposed BO algorithm manipulates the shape of the load based on transactive signals sent to each home. The thermostatically controlled loads (TCLs) act out of self-interest in response to the given price while maintaining comfort, and the optimizer exploits the thermal energy retention of the homes for the benefit of the community. Simulations confirm consistent neighborhood-level peak power reduction and energy cost savings.

Schomer, Ian↗
DOE OSTI2020

A Model-Free Frequency Control Approach for Diesel-Wind Powered Microgrids

Islanded microgrids usually consist of diesel generators and renewable energy sources (RES) to reduce the operating cost. Such microgrids have shown values for powering remote locations, but introduced unique challenges for frequency control of the grid due to the variability of renewable energy as well as the decoupled design of converter interface in RES. So, supportive control in RES becomes mandatory in such networks. This paper proposes a new dynamic control strategy, based on model-free control (MFC) approach, to support the primary frequency control of such islanded microgrids. The practical values of MFC have been discussed in various domains due to its control capability without modeling procedure and its efficient computation. In particular, we consider the diesel-wind system and the proposed MFC strategy is utilized as an online controller, which computes supplementary control signals for the rotor-side converter of the available wind turbine generators (WTGs). The calculated input signals allow WTGs to respond to frequency variations and improve the frequency response of the system. The controller is implemented and verified using the modified IEEE 33-bus full nonlinear model of the three-phase diesel-wind system in Simulink. Simulation results show the enhanced frequency response from the MFC strategy.

Park, Byungkwon↗
DOE OSTI2025

Economic Storage Size Optimization for Electric Vehicle Extreme-Fast Charging Stations

En-route charging infrastructure for electric vehicles is critical to support transportation needs. These charging stations are likely to have high loads and especially sharp peak loads given fast charging capabilities needed to meet transportation schedules. In order to reduce both strain on distribution grid infrastructure and charging station operational costs, many stations are likely to employ behind the meter storage. This paper demonstrates a behind the meter storage sizing optimization that employs an open-source agent-based vehicle behavior model (BEAM) to determine the best sizing across many scenarios. This optimization and analysis is novel in that it examines how storage size impacts not only charging station cost and peak load, but also vehicle queue times. The optimization is also applied across a wide analysis region with sufficient diversity and numbers to provide novel statistical analysis of optimal sizes.

Aka, Julius↗
DOE OSTI2025

Suppressing Screening-Current-Induced Strain in a 72-mm-Bore REBCO Insert for a 20-T Magnet: A Numerical Study

Rare-earth barium copper oxide (REBCO) high-temperature superconducting (HTS) magnets are considered a game changer for the capability of generating magnetic fields exceeding 20 T at or above liquid helium temperature, combined with the potential for substantially reduced manufacturing and operational costs. Princeton Plasma Physics Laboratory is dedicated to developing large-bore high-field superconducting magnet systems to support forefront physics research. Here, our current project focuses on a φ72-mm cold-bore REBCO insert comprising 21 dry-wound double-pancake coils, designed to generate at least 8 T when nested within a 12-T outsert magnet. A significant challenge for high-field REBCO magnets is the time-varying-magnetic-field-induced screening currents (SC) in the REBCO conductors, which can cause localized strain and stress concentrations. This paper presents a numerical study of the SC-induced strain in the REBCO insert magnet, confirming that the SC-induced strain can be substantially suppressed by energizing the REBCO insert before the outsert magnets. We discuss and reveal the mechanism behind this reduction. By applying this strategy, we expect to unleash the potential of the REBCO insert magnet to generate up to 12 T in a 12-T background field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

High-Efficiency Inductive Output Tubes Using a Third Harmonic Drive on the Grid

In this article, we discuss the use of a third harmonic component to the drive voltage on the grid of an inductive output tube (IOT). High-efficiency IOTs are typically characterized by efficiencies up to 70%–75%. However, the achievement of efficiencies greater than 80% would substantially reduce the operating costs of next-generation accelerators. In order to achieve this goal, we consider the addition of a third harmonic component to the drive signal on the grid of an IOT. The use of a third harmonic drive component in IOT guns has been considered in order to apply such a gun as the injector of radio frequency linear accelerators (RF linacs). Furthermore, we consider that the IOT will be used to provide the rf power to drive RF linacs and apply the third harmonic with the intention of increasing the efficiency of the RF output of the IOT. We consider a model IOT with a 700-MHz resonant cavity and using an annular beam with a voltage of 30 kV, an average current of 6.67 A yielding a perveance of about 1.3 μP. We simulate this IOT using the NEMESIS simulation code which has been successfully validated by comparison with the K5H90W-2 IOT developed by Communications and Power Industries. It is found that the effect of the third harmonic on the efficiency is greatest when the phase of the third harmonic is shifted by radians with respect to the fundamental drive signal and with third harmonic powers greater than about 50% that of the fundamental drive power. For the present example, we show that efficiencies approaching 86% are possible by this means.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Resilience-Oriented DG Siting and Sizing Considering Stochastic Scenario Reduction

In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. Here, the case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.

42 ENGINEERING↗
DOE OSTI2021

Optimal Demand Response Incorporating Distribution LMP with PV Generation Uncertainty

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. Here, the objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the Karush-Kuhn-Tucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

To take unit commitment (UC) decisions under uncertain load, most existing stochastic optimization (SO) frameworks adopt a generic representation of uncertainty. While load levels that materialize on a particular day are influenced by various covariates (such as the day of the week or temperature), SO frameworks typically disregard such side observations, wasting actionable information that could significantly enhance decision quality. Here, this article proposes a contextual SO (CSO) framework for UC under uncertain load, which can effectively exploit covariate observations in conjunction with a class of machine learning (ML) algorithms to improve the out-of-sample performance of UC decisions. It shows how three ML algorithms, adaptive boosting, gradient boosted trees, and extreme gradient boosting, can be used to this end, constituting the first application of these algorithms in any CSO framework. Using real-world data harvested from the New York ISO grid, we measure the out-of-sample performance of the framework in terms of total operation cost, shed load values, locational marginal prices, and total payments by the loads, against several benchmark methods proposed in the literature. The article has an online companion (Yurdakul et al.), wherein we present additional results and lay out further mathematical formulations used in this work.

42 ENGINEERING↗
DOE OSTI2023

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Sustainable Energy Solutions for Thermal Load in Buildings—Role of Heat Pumps, Solar Thermal, and Hydrogen-Based Cogeneration Systems

Economic and population growth is leading to increased energy demand across all sectors—buildings, transportation, and industry. Adoption of new energy consumers such as electric vehicles could further increase this growth. Sensible utilization of clean renewable energy resources is necessary to sustain this growth. Thermal needs in a building pose a significant challenge to the energy infrastructure. Potential technological solutions to address growing energy demand while simultaneously lowering the carbon footprint and enhancing the grid flexibility are presented in this study. Performance assessment of heat pumps, solar thermal collectors, nonfossil fuel-based cogeneration systems, and their hybrid configurations is reported in this study. The impact of design configuration, coefficient of performance (COP), electric grid’s primary energy efficiency on the key attributes of total carbon footprint, life cycle costs, operational energy savings, and site-specific primary energy efficiency are analyzed and discussed in detail. In conclusion, heat pumps and hydrogen-fueled solid oxide fuel cells (SOFCs) are highly effective building energy resources compared to traditional approaches; however, the carbon intensity of electrical energy and hydrogen production are keys to the overall environmental benefit.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Design of a Compact Dry Cooler With an Aluminum Heat Exchanger Core for a Supercritical CO2 Power Cycle Is Evaluated for a Concentrating Solar Power Application

Abstract As the supercritical CO2 power cycle develops and the component technologies mature, there is still a need to reduce the associated capital and operating costs to maintain a competitive levelized cost of electricity (LCOE) in order to enter the market. When considering concentrating solar power (CSP) coupled with an sCO2 power block and sensible thermal storage, the technology presents a clean source for utility-scale power generation to support baseload or peak-load electrical demand. However, the LCOE of the technology is still considered higher than the competing technologies and should be reduced to better compete in the market; 2030 targets for dispatchable solar plants are 5¢/kWh for baseload CSP and 10¢/kWh for peaker plants, as set by the United States Department of Energy. In response to this need, this study is targeting improvements in the power cycle precooler to reduce power block contribution to LCOE. This study considers a dry cooler, as CSP plants are sensitive to water consumption because many installations are slated for remote or arid locations where solar irradiance is very high, but water is scarce. Furthermore, the power block footprint for an sCO2 system is quite compact, especially as compared to a steam cycle. Therefore, there is interest in installing a more compact dry cooler that is proportional to the reduced footprint sCO2 power block, while conventional dry coolers are an order of magnitude larger. The competing goals of size, performance, and cost were considered in this study to develop a compact dry cooler that can easily be packaged with the power block, significantly reducing the installation and transport cost compared to the current state of the art, while maintaining or improving upon the heat transfer performance and impact on plant LCOE. This paper details the high-level findings of a large dry cooler sensitivity study for design point selection, design of the compact dry cooler, expected year-round performance for the dry cooler and the power cycle, and the predicted LCOE for a 30-year plant life. It was found that an aluminum heat exchanger core can be suitably designed to meet the pressure and temperature requirements for a precooler in an sCO2 recompression Brayton cycle. The dry cooler assembly was found to have improved heat transfer performance, allowing for increased cycle efficiencies and a reduced plant LCOE. When coupled with a centrifugal blower and compact transition duct, the dry cooler assembly was able to reduce the installation footprint by over 50%.

14 SOLAR ENERGY↗
DOE OSTI2022

Modeling and Analysis of a Novel Offshore Binary Species Free-Floating Longline Macroalgal Farming System

The investigation of innovative macroalgal cultivation is important and needed to optimize farming operations, increase biomass production, reduce the impact on the ecosystem, and lower system and operational costs. However, most macroalgal farming systems (MFSs) are stationary, which need to occupy a substantial coastal area, require extensive investment in farm infrastructure, and cost high fertilizer and anchoring expenses. This study aims to model, analyze, and support a novel binary species free-floating longline macroalgal cultivation concept. The expected outcomes could provide a basis for the design and application of the novel MFS to improve biomass production, decrease costs, and reduce the impact on the local ecosystem. In this paper, Saccharina latissima and Nereocystis luetkeana were modeled and validated, and coupled with longline to simulate the binary species MFS free float in various growth periods and associated locations along the US west coast. Further, the numerical predictions indicated the possibility of failure on the longline and breakage at the kelp holdfasts is low. However, the large forces due to an instantaneous change in dynamic loads caused by loss of hydrostatic buoyancy when the longline stretches out of the water would damage the kelps. Buoy-longline contact interactions could damage the buoy, resulting in the loss of the system by sinking. Furthermore, the kelp-longline and kelp-kelp entanglements could potentially cause kelp damage.

59 BASIC BIOLOGICAL SCIENCES↗