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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 OSTI2023

Electrochemically Grown Ultrathin Platinum Nanosheet Electrodes with Ultralow Loadings for Energy-Saving and Industrial-Level Hydrogen Evolution

Nanostructured catalyst-integrated electrodes with remarkably reduced catalyst loadings, high catalyst utilization and facile fabrication are urgently needed to enable cost-effective, green hydrogen production via proton exchange membrane electrolyzer cells (PEMECs). Herein, benefitting from a thin seeding layer, bottom-up grown ultrathin Pt nanosheets (Pt-NSs) were first deposited on thin Ti substrates for PEMECs via a fast, template- and surfactant-free electrochemical growth process at room temperature, showing highly uniform Pt surface coverage with ultralow loadings and vertically well-aligned nanosheet morphologies. Combined with an anode-only Nafion 117 catalyst-coated membrane (CCM), the Pt-NS electrode with an ultralow loading of 0.015 mg Pt cm -2 demonstrates superior cell performance to the commercial CCM (3.0 mg Pt cm -2 ), achieving 99.5% catalyst savings and more than 237-fold higher catalyst utilization. The remarkable performance with high catalyst utilization is mainly due to the vertically well-aligned ultrathin nanosheets with good surface coverage exposing abundant active sites for the electrochemical reaction. Overall, this study not only paves a new way for optimizing the catalyst uniformity and surface coverage with ultralow loadings but also provides new insights into nanostructured electrode design and facile fabrication for highly efficient and low-cost PEMECs and other energy storage/conversion devices.

42 ENGINEERING↗
DOE OSTI2023

Load balancing for multi-beam additive manufacturing systems

As powder bed fusion (PBF) additive manufacturing (AM) becomes a more mature field, system configurations are gradually moving away from the classic single heat source, layer-by-layer system configurations towards unconventional system configurations that offer higher throughput. Higher throughput systems allow PBF systems to be considered for a larger variety of industrial applications. However, the inclusion of multiple heat sources, or beams, also increases the complexity of the control schemes needed. For multi-beam systems with overlapping fields of view, the distribution of workload, or load balancing, across these beams directly affects the total print time for a build. Additionally, the probability of any beam failing in a multi-beam system increases with the number of beams. While manual methods of load balancing and dealing with beam failures are reasonable for current generation multi-beam systems, as system configurations become more complex, manual methods will become prohibitively inefficient. Here, this paper introduces two different ways to load balance multi-beam systems of various configuration types, regardless of their complexity, which are highly performant. A consequence of this performance is the enablement of on-the-fly load balancing in the event a beam fails, thus improving system robustness.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Low temperature water-gas shift: Optimization of K loading on Pt/m-ZrO 2 for enhancing CO conversion

Previous work showed that alkali doping of Pt/ZrO 2 significantly improved the LT-WGS rate. This improvement was caused by an acceleration in the rate of formate decomposition, the rate limiting step of an associative mechanism, through electronically weakening the CH bond. In this study, the effects that potassium loading on Pt/ZrO 2 have on the rate of LT-WGS and the strength of the CH bond in intermediate formate species are explored. Catalysts were investigated using in-situ DRIFTS, TPR-MS, TPD, EXAFS, and catalyst testing. Here, loadings of 1.7 wt.% potassium or higher shifted the formate ν(CH) band to significantly lower wavenumbers, indicating weakening of the bond. For the optimal loading, 2.6 wt.% K, the formate CH bond was significantly weakened, and the surface of Pt nanoparticles remained largely uncovered. However, loadings of 3.4 wt. % and above blocked the surface of the platinum nanoparticles. This, in addition to an increasing average Pt0 cluster size, hindered platinum from assisting in dehydrogenation of formate during steam promoted formate decomposition occurring in LT-WGS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks

The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposed in this paper. Furthermore, by regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. In order to tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings

Batteries and load control devices can increase the value of distributed solar photovoltaics (PV) from multiple perspectives - end-user, utility, and social. This review paper summarizes the end-user economics of battery and load control technologies that increase the value of PV by controlling and temporally shifting PV output, an approach referred to as 'solar plus.' Solar plus can increase on-site PV use. The literature shows that these values justify the incremental costs of solar plus devices for a wide variety of technologies, geographies, and customer load profiles, especially for customers in three rate structure contexts: where PV is sold to the grid at a lower value than the customer's retail rate, in time-of-use rates where peak periods do not coincide with PV output, and in demand charge rates where load peaks do not coincide with PV output. Rate structure and policy reform may be necessary to ensure that increasing solar plus deployment provides both end-user and system-level benefits.

14 SOLAR ENERGY↗
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Experimental investigation of advanced turbine control strategies and load-mitigation measures with a model-scale floating offshore wind turbine system

To advance the control co-design of offshore wind energy systems, the authors perform basin-scale experiments with a fully instrumented and actuated floating offshore wind turbine model. The model consists of a 1:70 scale performance-matched model of the International Energy Agency Wind Technology Collaboration Programme 15-MW reference turbine atop the VolturnUS-S semisubmersible platform. The Reference OpenSource Controller provides real-time blade pitch and generator torque control. Here we aim to develop an open data set for the validation of numerical models in predicting the influence of turbine control and load-mitigation measures. For this purpose, we measure the effects of advanced turbine control features, including peak shaving and floating feedback as well as hull-based control using tuned mass dampers on the system. Overall, the results demonstrate measurable and consistent influences from the control and load-mitigation measures, thus confirming the usefulness as a validation data set. Peak shaving attenuates the response to wind turbulence at near-rated wind speed. Floating feedback reduces the load and platform pitch motion associated with the negative damping induced by blade pitch control. The tuned mass dampers also attenuate the system response near the targeted frequencies under suitable conditions. We also identify detrimental side effects of each load-mitigation measure.

17 WIND ENERGY↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2015

Optimizing the performance of Ice-storage Systems in Electricity Load Management through a credit mechanism. An analytical work for Jiangsu, China

Ice-storage air-conditioning is a technique that uses ice for thermal energy storage. Replacing existing air conditioning systems with ice storage has the advantage of shifting the load from on-peak times to off-peak times that often have excess generation. However, increasing the use of ice-storage faces significant challenges in China. One major barrier is the inefficiency in the current electricity tariff structure. There is a lack of effective incentive mechanism that induces ice-storage systems from achieving optimal load-shifting results. This study presents an analysis that compares the potential impacts of ice-storage systems on load-shifting under a new credit-based incentive scheme and the existing incentive arrangement in Jiangsu, China. The study indicates that by changing how ice-storage systems are incentivized in Jiangsu, load-shifting results can be improved.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. Here, this study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

14 SOLAR ENERGY↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Mitigation of flow-induced pressure fluctuations in a Francis turbine operating at the design and partial load regimes-LES simulations

High-fidelity large eddy simulations (LES) were conducted to characterize the spatial and temporal structure of turbulent flows in an industrial-sized Francis turbine while the unit operated at the design point and partial load. A pressure surge with an amplitude of 8% of the turbine head was observed at partial load while the amplitude was<1% at the design point. The vortex rope precession observed in the draft tube correlated to the amplitude and frequency of the pressure surge. Central and peripheral water injections at various volumetric rates were considered to control the flow-induced pressure fluctuations. Central injection at the 4% and 6% flow rates attenuated high amplitude pressure fluctuations by 40% and 75% respectively at partial load. At the same operating conditions, peripheral injections did not have the same desired effect. Although power generation was not changed with water injection at the design point, it was reduced by about 2.5% by central injection and 0.5% by peripheral injection at partial load, showing a water injection mitigation strategy could be applied without any penalty.

Design and partial load regime↗
DOE OSTI2024

In situ measurement of three-dimensional intergranular stress localizations and grain yielding under elastoplastic axial-torsional loading

The three-dimensional grain-averaged response of solid bar samples under non-proportional (NP) elastoplastic axial-torsional loading was investigated using in situ high energy diffraction microscopy (HEDM) and companion crystal plasticity finite element (CPFE) modeling. Important stress metrics including applied shear (σ θZ ) and axial (σ ZZ ) stress tensor components, stress and stress deviator tensor invariants (I 1 , J 2 , and J 3 ), von Mises equivalent stress (σ$^{grain}_{VM}$), maximum resolved shear stress (mRSS), stress triaxiality (η), and lode angle parameter ($\barθ$) values were tracked for ~300 grains under two different loading conditions: (1) Torsion-dominated loading (low NP) and (2) Tension-torsion loading (high NP) in equiatomic NiCoCr, a representative multicomponent face-centered cubic (FCC) superalloy. Overall, significant stress localizations existed within both samples as evidenced by the radial dependence of grain-resolved σ θZ , σ$^{grain}_{VM}$, and J 2 ; by comparison, I 1 , J 3 , η, and $\barθ$ metrics did not show discernible trends within the volume. These stress localizations reveal a complex interplay between axial and shear stress components (e.g., stress coupling) resulting in grain yielding near the sample surface largely driven by shear stress, whereas internal grain yielding was largely accommodated by axial stress. Grain-resolved stress localization trends were described well by the CPFE model, although some discrepancies in magnitude occurred, particularly for volumetric stress metrics (I 1 and η) due to initial type II residual stress distributions. The superposition of initial residual stress states onto CPFE grain-resolved data significantly improved model accuracy for η. This suggests that residual stresses more strongly influence the simulation of volumetric rather than deviatoric (yield) stress metrics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Characterizing electrical panel capacity, breaker space, and loads in U.S. single-family homes

Electrical panels distribute electricity to appliances, equipment, and other building loads and maintain safety by preventing overloads of branch circuits, feeders, and utility service wires connecting homes to the grid. Load calculations in the National Electrical Code (NEC) determine the capacity of installed electrical panels in the United States, which are rated in amps (A). Circuit breakers within electrical panels affect the magnitude and number of loads that panels can serve. In home retrofits and renovations, existing electrical panels may not accommodate new electrical loads, either due to insufficient electrical capacity or a lack of available breaker spaces. Homes with these panel constraints may need to replace existing panels with new panels that have greater capacity and breaker space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Efficient 3D Printed Pseudocapacitive Electrodes with Ultrahigh MnO 2 Loading

Retaining sound electrochemical performance of electrodes at high mass loading holds critical importance to energy storage. Pseudocapacitive materials such as manganese oxide (MnO 2 ) deposited on current collectors have achieved outstanding gravimetric capacitances, sometimes even close to their theoretical values. However, this is only achievable with very small mass loading of active material typically less than 1 mg cm -2 . Increasing mass loading often leads to drastic decay of capacitive performance due to sluggish ion diffusion in bulk material. In this work, we demonstrate a 3D printed graphene aerogel electrode with MnO 2 loading of 182.2 mg cm -2 , which achieves a record-high areal capacitance of 44.13 F cm -2 . Most importantly, this 3D printed graphene aerogel/MnO 2 electrode can simultaneously achieve excellent capacitance normalized to area, gravimetry, and volume, which is the trade-off for most electrodes. This work successfully validates the feasibility of printing practical pseudocapacitive electrodes, which might revolutionize pseudocapacitor fabrication.

36 MATERIALS SCIENCE↗
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 OSTI2017

Power-to-load balancing for asymmetric heave wave energy converters with nonideal power take-off

The aim of this study is to maximize the power-to-load ratio for asymmetric heave wave energy converters. Linear hydrodynamic theory was used to calculate bounds of the expected time-averaged power (TAP) and corresponding surge-restraining force, pitch-restraining torque, and power take-off (PTO) control force with the assumption of sinusoidal displacement. This paper formulates an optimal control problem to handle an objective function with competing terms in an attempt to maximize power capture while minimizing structural and actuator loads in regular and irregular waves. Penalty weights are placed on the surge-restraining force, pitch-restraining torque, and PTO actuation force, thereby allowing the control focus to concentrate on either power absorption or load mitigation. The penalty weights are used to control peak structural and actuator loads that were found to curb the additional losses in power absorption associated with a nonideal PTO. Thus, in achieving these goals, a per-unit gain in TAP would not lead to a greater per-unit demand in structural strength, hence yielding a favorable benefit-to-cost ratio. Demonstrative results for 'The Berkeley Wedge' in the form of output TAP, reactive TAP needed to drive WEC motion, and the amplitudes of the surge-restraining force, pitch-restraining torque, and PTO control force are shown.

16 TIDAL AND WAVE POWER↗