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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Evolutionary and functional perspectives on signaling from neuronal surface to nucleus

Reliance on Ca{sup 2+} signaling has been well-preserved through the course of evolution. While the complexity of Ca{sup 2+} signaling pathways has increased, activation of transcription factors including CREB by Ca{sup 2+}/CaM-dependent kinases (CaMKs) has remained critical for long-term plasticity. In C. elegans, the CaMK family is made up of only three members, and CREB phosphorylation is mediated by CMK-1, the homologue of CaMKI. CMK-1 nuclear translocation directly regulates adaptation of thermotaxis behavior in response to changes in the environment. In mammals, the CaMK family has been expanded from three to ten members, enabling specialization of individual elements of a signal transduction pathway and increased reliance on the CaMKII subfamily. This increased complexity enables private line communication between Ca{sup 2+} sources at the cell surface and specific cellular targets. Using both new and previously published data, we review the mechanism of a γCaMKII-CaM nuclear translocation. This intricate pathway depends on a specific role for multiple Ca{sup 2+}/CaM-dependent kinases and phosphatases: α/βCaMKII phosphorylates γCaMKII to trap CaM; CaN dephosphorylates γCaMKII to dispatch it to the nucleus; and PP2A induces CaM release from γCaMKII so that CaMKK and CaMKIV can trigger CREB phosphorylation. Thus, while certain basic elements have been conserved from C. elegans, evolutionary modifications offer opportunities for targeted communication, regulation of key nodes and checkpoints, and greater specificity and flexibility in signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Reducing the computational burden of a microgrid energy management system

As renewable technology advances and decreases in cost, microgrids are becoming an appealing means of distributed generation both for isolated communities and integrated with existing electrical grid systems. Due to their small size, however, microgrids may have financial limitations which preclude them from using commercial software to optimize control of their assets. Open-source optimization solvers are a viable alternative, but increase computation time. This work expands on a rolling horizon optimization framework for economic dispatch within an existing residential microgrid located in Hoover, Alabama. The microgrid has an open-source solver requirement and a need for quick solution time on a rolling horizon as opposed to a day-ahead commitment. We present a method of reducing integer variables by relaxation which completes two goals: reduction in computation time for real-time operations, and reduction in daily operational cost for the microgrid. Seasonal data for load and photovoltaic (PV) power was also collected from the microgrid to facilitate simulation testing. Computation time was successfully reduced using multiple variations of the relaxation method, while obtaining solution quality with operational cost similar to or better than the original model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Methods identifying cost reduction potential for water electrolysis systems

The electrochemical reduction of water to form hydrogen is an emerging alternative for energy storage, where hydrogen can be used as a transportation fuel, combusted to generate electricity, utilized in a fuel cell, or used as a feedstock for chemical synthesis. Techno-economic analysis (TEA) is a valuable tool for understanding how to inform research directions that could make hydrogen from electrolysis cost competitive with that produced by conventional means like steam methane reforming. The current state of knowledge on TEA of electrolysis systems suggests cost reductions are likely to result from advances in system design and materials, scale-up of manufacturing processes, and learning by doing effects as electrolysis deployment increases. Future directions and opportunities for TEA of electrolysis systems include dispatchable operation in wholesale power markets, optimization of capital cost and system durability, and analysis of pathways for hydrogen to support economy-wide decarbonization.

08 HYDROGEN↗
DOE OSTI2025

Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Development of a concentrating solar power system using fluidized-bed technology for thermal energy conversion and solid particles for thermal energy storage

Concentrating solar power (CSP) is an effective way to convert solar energy into electricity with an economic energy-storage capability for grid-scale, dispatchable renewable power generation. However, CSP plants need to reduce costs to be competitive with other power generation methods. Two ways to reduce CSP cost are to increase solar-to-electric efficiency by supporting a high-efficiency power conversion system, and to use low-cost materials in the system. The current nitrate-based molten-salt systems have limited potential for cost reduction and improved power-conversion efficiency with high operating temperatures. Even with significant improvements in operating performance, these systems face challenges in satisfying the cost and performance targets. This paper introduces a novel CSP system with high-temperature capability that can be integrated into a high-efficiency CSP plant and that meets the low-cost, high-performance CSP targets. Unlike a conventional salt-based CSP plant, this design uses gas/solid, two-phase flow as the heat-transfer fluid (HTF); separated solid particles as storage media; and stable, inexpensive materials for the high-temperature receiver and energy storage containment. We highlight the economic and performance benefits of this innovative CSP system design, which has thermal energy storage capability for base-load power generation.

14 SOLAR ENERGY↗
DOE OSTI2021

Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An attack-tolerant trustable distributed intelligence paradigm

In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of "centralized dispatching and hierarchical management", this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Intelligent industrial demand response to increase grid flexibility and reliability: A review

The rapid transition toward renewable energy has introduced challenges in grid stability due to the intermittency of non-dispatchable sources like solar and wind. Industrial Demand Response (IDR) offers a promising, cost-effective solution that adjusts energy consumption patterns to align with supply, increases renewable utilization, and reduces costs. This review provides an updated analysis of IDR, sorting technologies into five categories: energy storage, scheduled energy usage, operational flexibility, on-site generation, and intelligent operations. Energy storage solutions, while requiring little flexibility, often have the longest payback periods. While slightly better, on-site generation also has longer payback periods, ranging from 5 to 20 years or more. Scheduled energy usage, operational flexibility, and intelligent operations allow significant peak reduction at lower capital costs but require greater flexibility. While 15–20 % peak reduction is within the range of all five categories, scheduled energy use and on-site energy generation are shown to have reductions of up to 70–80 % in select scenarios. Combining multiple IDR strategies from these five categories maximizes both financial and operational benefits. Synergistic approaches are shown to enhance grid stability while reducing costs. As the grid evolves, IDR will enable a more flexible, renewable-powered future that will benefit industrial facilities and the broader energy system.

Demand flexibility↗
DOE OSTI2019

Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters

To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Accuracy of hourly energy predictions for demand flexibility applications

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A novel means to flexibly operate a hybrid concentrated solar power plant and improve operation during non-ideal direct normal irradiation conditions

Hybridization of concentrated solar power (CSP) with other energy sources can drastically increase how much solar energy is harnessed and potentially expand the current geographical deployment of CSP installlations. In this study, a novel operation strategy referred to as flexible heat integration (FHI), is proposed and applied to a hybrid CSP-natural gas power plant. The proposed plant, modeled in Simulink, employs a solar central receiver (SCR) and dual-source gas boiler to drive a steam Rankine power cycle. FHI involves collecting and dispatching solar energy at modified temperatures during off-design direct normal irradiance (DNI). During low-grade (<100 W/m 2 ) and medium-grade (100–500 W/m 2 ) levels of DNI, molten salt is used for steam boiling and feedwater preheating, respectively, rather than superheating usually seen in SCR steam plants. Hybrid gas-firing supplies energy to bypassed systems. FHI is applied to a hybrid plant over the course of a day from each season for a plant located in Salt Lake City, Utah, an unconventional location due to off-design DNI variability. A spring day exhibiting variable DNI is used as the main case study to demonstrate the dynamics and robust operation under the FHI framework. The operation of the plant over the winter day shows the largest improvement in solar-to-electric (STE) efficiency whereas the summer day exhibits the smallest improvement. Monthly results follow a similar trend with FHI improving STE from 15.3% and 16.3% to 17.0% and 16.7% in January and July, respectively. Yearly results show FHI improving STE efficiency from 16.1% to 17.1% relative to standard hybrid operation. Altogether, the proposed concept expands the current understanding of energy management in hybrid SCR-natural power plants under non-ideal DNI conditions and locations that have lower DNI by applying FHI.

14 SOLAR ENERGY↗
DOE OSTI2017

The impact of ancillary services in optimal DER investment decisions

Microgrid resource sizing problems typically include the analysis of a combination of value streams such as peak shaving, load shifting, or load scheduling, which support the economic feasibility of the microgrid deployment. However, microgrid benefits can go beyond these, and the ability to provide ancillary grid services such as frequency regulation or spinning and non-spinning reserves is well known, despite typically not being considered in resource sizing problems. This paper proposes the expansion of the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state-of-the-art microgrid resource sizing model, to include revenue streams resulting from the participation in ancillary service markets. Results suggest that participation in such markets may not only influence the optimum resource sizing, but also the operational dispatch, with results being strongly influenced by the exact market requirements and clearing prices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A systematic solution to quantify economic values of vehicle grid integration

We report Vehicle-Grid-Integration (VGI) supplies one of the potential benefit extensions for electric vehicles (EVs) to make use of their parking time, which enables the EVs to provide grid services while still meeting consumer driving needs. However, the costs, benefits and risks of VGI still remain unclear, which limits the development of the VGI to promote the interaction between the EV and grid. In this study, we propose an integrated framework to quantify and utilize the aggregate flexibility of the EVs to supply the grid services in electricity markets. The integrated solution includes five sub-modules that cover end-to-end functionalities from individual EV energy consumption estimation to final monetary values calculation of providing grid services. Both wholesale market and local level charging management are formulated in the optimization module. A predictive control algorithm is proposed to allocate power to individual vehicles in real time, considering uncertainties from dispatch signal and travel behavior. Simulation results from 10,000 EVs indicate that the proposed optimization methods can significantly reduce the system cost in both wholesale market and retail market. Local tariff optimization reduces the electricity cost by 24.4% compared to uncontrolled charging. Wholesale market optimization results show that $\$$691 and $\$$255 revenues can be captured by each EV in ERCOT and CAISO markets per year, although with a conservative assumption on battery throughput cost at 0.16$\$$/kWh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

High-temperature Pumping of Silicon for Thermal Energy Grid Storage

As the cost of renewable energy falls below fossil fuels, the key barrier to widespread sustainable electricity has become availability on demand. Energy storage can enable dispatchable renewables, but only with drastic cost reductions compared to current batteries. One electricity storage concept that could enable these cost reductions stores electricity as sensible heat in an extremely hot liquid (>2000 °C) and uses multi-junction photovoltaics (MPV) as a heat engine to convert it back to electricity on demand, hours or days later. This paper follows previously reported technoeconomics and liquid containment, examining equipment that would be needed to exchange heat between resistive heaters, a molten silicon storage tank above 2000 °C, and a heat engine. Herein, we report on a pump that was designed and tested to circulate the liquid silicon between these three regions and the effect of spatial thermal cycling was simulated in models and experiments. While the pump successfully circulated silicon between 1800-2080 °C for ten hours, circulation with a temperature gradient caused it and other non-isothermal experiments to dissolve significantly due to the temperature dependent solubility of not only carbon, but also silicon carbide which otherwise protected the graphite infrastructure. Furthermore, methods to reduce dissolution and an alternative embodiment are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

25 ENERGY STORAGE↗
DOE OSTI2011

Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power.

This paper uses a new unit commitment model which can simulate the interactions among plug-in hybrid electric vehicles (PHEVs), wind power, and demand response (DR). Four PHEV charging scenarios are simulated for the Illinois power system: (1) unconstrained charging, (2) 3-hour delayed constrained charging, (3) smart charging, and (4) smart charging with DR. The PHEV charging is assumed to be optimally controlled by the system operator in the latter two scenarios, along with load shifting and shaving enabled by DR programs. The simulation results show that optimally dispatching the PHEV charging load can significantly reduce the total operating cost of the system. With DR programs in place, the operating cost can be further reduced.

17 WIND ENERGY↗