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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Power over fiber development for HEP detectors

Power-over-Fiber (PoF) technology has been used extensively in settings where high voltages require isolation from ground and electromagnetic isolation is critical. In cryogenic environments, PoF offers a reliable power transmission technology, leveraging optical fibers to transfer power with minimal system degradation. PoF technology excels in maintaining low noise levels and isolation when delivering power to sensitive electronic systems operating in extreme temperature ranges and high voltage environments. Here, in a novel application of PoF for a HEP detector, power is provided to photon detector modules located on a surface at ~300 kV with respect to ground in the planned DUNE experiment. This summary paper of the PoF talk at the 16th PISA Meeting on Advanced Detectors highlights the R&D effort of PoF in extreme conditions and underscores its capacity to revolutionize power delivery and management in critical applications offering a dependable solution with low noise, optimal efficiency, and superior isolation. The DUNE (Abi et al., 2020) experiment will soon deploy large liquid argon (LAr) time projection chambers (TPC) to detect neutrino interactions and other particle physics phenomena. In addition to the particle tracking provided by the TPC, photon detectors, powered by a first ever PoF system, in the cryostat will leverage the high scintillation light yield of LAr to provide crucial timing and additional calorimetric information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Incorporating energy justice and equity objectives in power system models

Ensuring an equitable energy transition requires models and tools that can account for equity and energy justice goals. Power system models (PSMs) are widely used throughout industry, government, and academia to simulate or optimize the operations and planning of current and future electricity systems under different scenarios, parameter assumptions and policy frameworks. These models are important tools that allow users to understand how the power system may evolve under different future conditions, but importantly, they are also used to inform policy implementation and investment decisions across all aspects of the power system. However, existing models seldom include energy justice considerations and therefore energy justice priorities are not reflected in the policies and other decision-making processes that are informed by these models. The purpose of this review is to provide a framework that energy modelers can draw upon to integrate energy justice and equity goals into PSMs. To this end, 99 papers that examine the intersection of energy justice and power system models are summarized and ten core aspects of the power system that can impact energy justice outcomes, and therefore require new modeling approaches, are identified. This review then establishes key current practices, challenges, and opportunities associated with capturing energy justice considerations in power system models across these ten aspects. This review concludes by proposing four key research directions that should be pursued to improve the representation of energy justice and equity in power system modeling. Finally, this review also addresses challenges raised by United Nations Sustainable Development Goal 7, which aims to ensure affordable energy access to everyone and Sustainable Development Goal 13, which aims to take urgent action to address climate change.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Steady-state off-design modeling of the supercritical carbon dioxide recompression cycle for concentrating solar power applications with two-tank sensible-heat storage

Concentrating solar power researchers are evaluating the potential of the supercritical carbon dioxide recompression cycle to improve the thermal efficiency and decrease the capital costs of next-generation systems. This analysis investigates the steady-state off-design performance of a recompression cycle integrated with two-tank sensible-heat thermal energy storage as the ambient temperature and heat-transfer fluid (HTF) inlet conditions to the cycle change. This paper presents off-design component models and then cycle convergence and control models to maximize net cycle power output while constraining the high-side pressure and air-cooler fan power to their respective design values and fixing the cycle HTF outlet temperature to its design value. Results show that inventory control and air-cooler fan power are important control parameters that can be optimized to maximize off-design power output as the ambient temperature and HTF mass flow rate diverge from design. The high-side pressure and fan power constraints cause the cycle net power to degrade when the ambient temperature is warmer than the design value, and this study calculates a maximum mass flow rate for hot days above which the cycle cannot achieve the design HTF outlet temperature. Finally, the analysis shows that optimizing compressor shaft speeds can improve cold-day performance by around 1.5 percentage points and part-load performance by up to 2.5 percentage points versus a baseline case with no active compressor control.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimization and Techno-Economic Comparison of Regenerators and Recuperators in sCO2 Recompression Brayton Cycles for Concentrating Solar Power Applications

Supercritical CO2 power cycles might be capable of providing the cost reductions and efficiency improvements necessary to reach concentrating solar power (CSP) cost targets, but require large, highly effective recuperators. Fixed-bed regenerators are a periodic heat exchanger that could be an effective yet low-cost alternative to these recuperators. This study presents a techno-economic comparison of regenerators and recuperators for a 100 MWe supercritical CO2 power cycle for CSP applications. We quantify the levelized cost of energy (LCOE) for the power plant with both types of heat exchangers, and explore sensitivity of LCOE to parameters such as CSP-specific capital costs, power cycle component capital costs, and regenerator valve replacements costs. Results indicate that the incumbent printed circuit heat exchangers achieve the lowest LCOE, unless the upper extreme of PCHE capital cost and lower extremes of regenerator and valve capital and O&M costs are realized. Sensitivity analysis also illustrates that the primary heat exchanger cost is the most influential of power cycle components, and that CSP component capital cost is more influential than power cycle capital costs. This illustrates the importance of reducing the cost of heliostats, receivers, and thermal energy storage for achieving CSP LCOE targets.

OTHER INSTRUMENTATION,SOLAR ENERGY↗
DOE OSTI2022

Nighttime electric power generation at a density of 50 mW/m 2 via radiative cooling of a photovoltaic cell

A large fraction of the world's population lacks access to the electric grid. Standard photovoltaic (PV) cells can provide a renewable off-grid source of electricity but only produce power from daytime solar irradiance and do not produce power at night. While there have been several theoretical proposals and experimental demonstrations of energy harvesting from the radiative cooling of a PV cell at night, the achieved power density is very low. Here, we construct a device, which incorporates a thermoelectric generator that harvests electricity from the temperature difference between the PV cell and the ambient surrounding. We achieve 50 mW/m 2 nighttime power generation with a clear night sky, with an open-circuit voltage of 100 mV, which is orders of magnitude higher as compared with previous demonstrations. During the daytime, the thermoelectric generator also provides additional power on top of the electric power generated directly from the PV cells. Our system can be used as a continuous renewable power source for both day- and nighttime in off-grid locations.

14 SOLAR ENERGY↗
DOE OSTI2026

ML-Based Pebble Power Reconstruction for Pebble Bed Reactor Analysis

Pebble power reconstruction has been explored to complement the conventional homogenized modeling approach in pebble bed reactor (PBR) analysis, as detailed heterogeneous geometry calculations are computationally expensive. The random distribution of pebble fuels within the core challenges the application of conventional pin power reconstruction methods. To address this, we introduce a machine learning approach based on the transformer model, composed of encoder and decoder layers, to estimate the flux and power form functions for reconstructing individual pebble neutron fluxes and powers. The homogeneous neutron flux distribution within each spectral zone (SZ) is obtained from finite element solutions of global diffusion or transport calculations. Verification tests demonstrate that the trained transformer model accurately predicts power form functions over a range of conditions, including variations in pebble enrichment, location, type, SZ size, and burnup. In particular, verification using a three-dimensional PBR benchmark with burned pebbles shows good agreement in heterogeneous pebble power distributions between Griffin and Serpent. These results highlight the potential of applying conventional pin power reconstruction approaches to PBR cores with randomly distributed pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Power balance analysis at the L-H transition in JET-ILW NBI-heated deuterium plasmas

The understanding of the physics underlying the L-H transition has strong implications for ITER experimental reactor and demonstration power plant (DEMO). In many tokamaks, including JET, it has been observed that, at a particular plasma density, n e,min , the power necessary to access H-mode P L-H is minimum. In the present work, L-H transitions of JET deuterium plasmas heated by neutral beam injection (NBI) are studied for the first time by means of a power balance analysis to characterize the main contributions in the transition, through integrated transport modelling. In the pulses analysed, we do observe a minimum of the L-H power threshold in density, indicating the presence of density branches and of n e,min . Electron and ion heat fluxes at the transition are estimated separately. The electron/ion equipartition power results in favour of the ions, as shown by QuaLiKiz quasilinear gyrokinetic simulations, which predict a larger ion transport that causes T e > T i . The resulting edge ion heat flux also shows a clear change of slope below n e,min , similarly to ASDEX-Upgrade (AUG) NBI pulses (Ryter et al 2014 Nucl. Fusion 54 083003). JET NBI data are compared to radio-frequency heated AUG and Alcator C-mod pulses (Schmidtmayr et al 2018 Nucl. Fusion 58 056003), showing a different trend of the power, coupled to ions at the L-H transition with respect to the linearity observed in the radio-frequency heated plasmas. Finally, the presence of n e,min and the role of the ion heat flux is discussed in the paper, although it seems it is not possible to explain the presence of a P L-H minimum in density by a critical ion heat flux and by the equipartition power for the JET NBI-heated plasmas analysed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Time-dependent scenario modeling for the ST-E1 fusion power plant

ST-E1 is a low aspect ratio fusion power plant being designed by Tokamak Energy targeting 1.5 GW of fusion power. Characterization of the ST-E1 flat-top scenario is described elsewhere McNamara et al (2026 Nucl. Fusion 66 086008); here we focus on addressing the question of how to ramp-up the ST-E1 plasma from an initial state following breakdown and flux-surface formation to the target flat-top state. Being low-aspect ratio, the available solenoid flux of ST-E1 is limited. Therefore, particular consideration is placed on developing ramp-up scenarios that predominantly use inductive flux provided by external vertical field coils. Through time-dependent modeling with METIS, we show that this is possible when the ramp-up is performed at relatively high plasma density: although auxiliary current drive efficiency is reduced, this is significantly outweighed by (1) higher electron-ion collisional equilibration, (2) higher fusion power once ions become sufficiently hot, (3) higher poloidal beta for increased vertical-field flux, and (4) potentially favorable exhaust compatibilities. Ultimately, we show the target ST-E1 flat-top performance can be reached after a ramp-up period lasting 150 s using less than 40 Vs of solenoid flux (with vertical field providing ∼ 90 Vs of flux). The sensitivity to model assumptions are presented, with the general observation that deleterious effects can be mitigated through minor alterations of the auxiliary power temporal waveform and/or total auxiliary power level. The impact of a solenoid and the auxiliary power mix (electron cyclotron heating only versus electron and ion cyclotron heating) on the ST-E1 ramp-up success are also discussed in appendices. On this latter topic, we show that the effect of direct-ion heating during ramp-up is obscured by the uncertainty in the pedestal dynamics, identifying a clear line of future work required to make a definite decision on the ST-E1 auxiliary power mix.

current ramp-up↗
DOE OSTI2024

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗
DOE OSTI2020

Power Factor revisited

Power factor is a term whose value can be found by dividing the power in a load to the product of the voltage and the current. It has been used as a rule of thumb to estimate the cost of the losses incurred in delivering reactive power. But it has been extremely troublesome. The same load might be penalized by some definitions, and might not by others. That is still the situation today. There have been two efforts at solution. In 1919 a committee of experts was formed to solve the problem of its definition. The effort did not succeed at producing a definition; two alternatives were recommended in the committee’s final report, but both were adopted. In 2010, IEEE issued Standard 1459 to give definitions. In this standard, definitions of power factor appear under no less than seven different headings. The problem demands solution. The matter is of considerable economic importance to power producers, consumers, and instrument makers. This paper examines the history of the topic, and explores reasons for the difficulty of definition in terms of measurement theory. It is shown that power factor is not suited to purpose. In fact, no definition can exist that will define power factor usefully for its intended economic purpose. Two alternatives are proposed.

power factror, definitions, measurement theory, IE↗
DOE OSTI2023

AutoTG: Reinforcement Learning-Based Symbolic Optimization for AI-Assisted Power Converter Design

Power converters are pervasive in modern electronic component design. They can be found in all electronic devices from household appliances and cellphone chargers to vehicles. Currently, designing new circuit topologies is hard because it requires human expertise based on experience and is difficult to automate. However, artificial-intelligence-assisted design can significantly facilitate the development of new power converters and/or improve the final result. Intelligently designed highly efficient power converters can have a significant effect on many important attributes, such as power efficiency, layout size, cost, heat dissemination, energy requirements, etc. We propose Autonomous Topology Generator (AutoTG), a reinforcement-learning-based framework that generates power converter topology candidates based on user specifications, optimized for user preferences. By modeling power converter design as a symbolic optimization problem, we sequentially sample components in an autoregressive manner until new topologies are formed, providing both the topology specification and the sizing (magnitude of each component parameter) of the proposed power converter. Here, we provide an empirical evaluation and show that AutoTG is able to generate varied high-efficiency topologies within component restrictions based on user input and show that previously unknown topologies can be found for further evaluation.

(AI)-based design↗
DOE OSTI2020

Reconfigurable Real-Time Power Grid Emulator for Systems With High Penetration of Renewables

Novel power system control and new utility devices need to be tested before their actual deployment to the power grid. To assist with such a testing need, real-time digital emulators such as RTDS and Opal-RT can be used to connect to the physical world and form a hardware in the loop (HIL) emulation. However, due to the limitations of today's computational resources, the accuracy and fidelity suffer from different levels of model reductions in purely digital simulations. CURENT has developed a reconfigurable electric grid hardware testbed (HTB) to overcome the limitations of digital emulators. The HTB has been used to develop measurement, control, modeling, and actuation techniques for a national grid with a high penetration of renewables. The power electronic-based system includes emulators for synchronous generators; photovoltaics with grid-interfacing inverter; wind turbines; induction motor loads, ZIP loads, power electronic loads; batteries; ac and dc transmission lines; short circuit faults and grid relay protection; and a multiterminal HVDC overlay including power electronics interfaces. The system contains real elements of power flow, measurement, communication, protection, and control that mimic what would be seen in an actual electric grid. This paper presents an overview of the HTB and several scenarios that have been run to determine control and actions needed for the future power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Review of Machine Learning Applications in Power System Resilience

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

Deep Learning↗
DOE OSTI2024

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↗
DOE OSTI2025

A Two-Stage Approach for PV Inverter Engagement in Power Factor Correction and Voltage Regulation

The rapid integration of distributed energy resources, like solar photovoltaics (PVs), can lead to overvolt-age challenges due to reverse power flow and a noticeable decrease in power factor at the substation interface. While existing literature extensively explores utilizing smart inverter capabilities for reactive power flexibility using a volt-var curve (VVC), obtaining time-varying operating points of such curves in real-time is challenging due to computational demands and communication requirements. Similarly, employing optimization-based approaches for reactive power control and active voltage regulation in large-scale distribution feeders is difficult due to the complexity of the problem and the challenges in effectively engaging customer-owned resources. This paper proposes a two-stage strategy to harness smart inverters for reactive power support. The first stage formulates short-term planning by optimally designing VVCs (on a daily or hourly basis) for large-scale solar PVs based on projected system needs and communicating optimal curves to smart inverters in advance. Subsequently, the second stage employs a transactive-based method to involve customer-owned PVs for reactive power support, effectively enhancing overall system performance and addressing real-time demands. In conclusion, the efficacy of this approach will be demonstrated using real-world distribution circuits provided by Vermont Electric Power Company (VELCO) and Vermont Electric Cooperative (VEC).

Poudel, Shiva [Pacific Northwest National Laborato↗
DOE OSTI2021

3-D Prismatic Packaging Methodologies for Wide Band Gap Power Electronics Modules

In a power module the parasitic inductance limits the dynamic high-frequency performance, and the area of the cooling surfaces limits the power capability. This paper presents a new 3D power electronic design methodology based on the concept of mutual inductance cancellation and multi-sided heat transfer. New 3D prismatic packaging concepts are proposed for Wide Band Gap (WBG) power devices, where the devices can be mounted at acute angles to adjacent interconnects or other devices. Discussion is given on electrical and thermal path optimization in a 3D space. To validate the 3D prismatic packaging methodology, a 1200V/50A SiC half-bridge power module is fabricated and tested for electrical and thermal performance and results are compared with simulations. The power density calculated for the module is 12 kW/in3 (including heatsink) and shows a 31.3% inductance reduction compared to a 2D planar module. Finally, design guidance suggested for utilizing prismatic structures is provided, together with suggested future work in the area. This paper presents the first reported true 3D power module.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Virtual Critical Coupling in High-Power Resonant Systems

Power reflections are a fundamental challenge in high-power resonant systems, causing energy loss, limiting per- formance, and potentially damaging critical components. This problem is particularly acute in applications such as lower hybrid current drive (LHCD) for tokamak-based nuclear fusion, where efficient, stable power transfer to the plasma is paramount. In this work, we introduce and experimentally demonstrate the virtual critical coupling (VCC) mechanism, a nonmechanical method to achieve reflection-free excitation in high-power S-band resonators. VCC utilizes a temporally shaped excitation signal with a precise complex frequency, tailored to the resonator’s characteristics to enable complete energy absorption. Using a custom low-level RF system and a 5-MW S-band klystron, we conducted experiments from low power (32 mW) to high power (600 kW). Furthermore, the results demonstrate a greater than ninefold reduction in the reflection coefficient compared to conventional monochromatic excitation. This signal processing-based approach offers a robust path toward enhancing the efficiency and stability of high-power resonant systems, with significant implications for particle accelerators and the advancement of nuclear fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Probabilistic Zonal Reserve Requirements for Improved Energy Deliverability with Wind Power

In power systems with high penetration of renewable energy resources, uncertainty and variability of these stochastic resources introduce additional challenges for the operation of power systems. To improve the power system's reliability in the face of uncertainty, reserves are required as additional generation capacity to rebalance the power system following random disturbances. However, reserve deliverability is not guaranteed, because it may encounter potential transmission line congestion. Zonal reserve requirements can address this issue, but operators lack efficient ways to allocate reserves to zones while accounting for wind power forecast uncertainty. Herein we propose a methodology for probabilistic zonal reserve requirements to address wind power forecast uncertainties. This method estimates the probability distribution of line flows based on the system generation margin and injection shift factor. This estimate is then used to construct pre-defined and post-zonal reserve requirements. Case studies demonstrate that the proposed method efficiently schedules energy and reserves to balance energy and manage deliverability with wind power forecast uncertainty.We also discuss operational implications of the proposed method.

17 WIND ENERGY↗