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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A grid-scale study of demand bidding by large industrial users

A demand bidding mechanism for engaging large industrial electricity users in the operation of the power grid is presented. Demand bidding is formulated as an optimization problem based on a modified version of the alternating current optimal power flow problem, and can be interpreted as a tâtonnement process between the grid operator and electricity users. Here, the work provides the first – to the authors’ knowledge – grid-scale case study of demand bidding, using a synthetic grid structure in the footprint of the grid of Texas. Results reveal that the demand bidding lowers overall power generation costs, but economic benefits plateau as the number of participants increases. Transmission line and transformer capacity constraints become the limiting factors, revealing that expanding and fortifying the transmission infrastructure is key to expanding demand-side participation. Demand bidding does not substantially alter the optimal operation of existing bidding entities when the number of bidders increases, thereby supporting existing bidders to stay in the system and encouraging new ones to join.

Chlor-alkali plant↗
DOE OSTI2020

Local Communication in Populations of Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) have shown great potential for demand response (DR) in electric grid operations. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of a small number of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a linearized model of the aggregate system and the results of that analysis compared to simulation results. The proposed modification of thermostat behavior results in fourfold reduction in the post-DR peak while suppressing ensuing oscillations at the expense of a modest increase in compressor cycling. Further, the linearized model also provides insight into the aggregate behavior of the population.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Building and grid system benefits of demand flexibility and energy efficiency

The global electricity system is undergoing a dramatic transformation. Technology advancements in clean energy generation (e.g., solar photovoltaics and wind) and electrified demand (e.g., transportation and building heating) are providing a cost-competitive pathway to mitigate the urgent and severe consequences associated with a changing climate. In addition, as extreme events continue to increase in frequency and magnitude, solutions are needed for adapting to the current climate. Through energy efficiency and demand flexibility, buildings play a prominent role in meeting these challenges; recently in Joule, a paper by Langevin et al. provides clear insight into the magnitude of buildings’ technical potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Modeling the U.S. Western Electric Interconnection to Understand the Consequences of Hydrometeorological Extremes and Options for Risk Mitigation

Electricity grid operators around the world face a dual challenge; withstanding increasingly severe weather and the longer term impacts of climate change, while simultaneously decarbonizing. Extreme weather events such as heat waves and droughts are rising in both severity and frequency, which is threatening the reliability of electricity grids through increased demand, generation capacity losses, and equipment failures. Consequently, incorporating hydrometeorological stressors into computational power systems analysis is becoming an even more critical tool in long term planning and short term operations. However, there is a general lack of open-source customizable grid simulation software capable of exhaustively stress testing the grid under hydrometeorological uncertainty, and/or examining potential risk mitigation pathways. A related, persistent challenge for power system modelers is striking an appropriate balance between model fidelity (e.g. spatial scale and time resolution) and computational tractability (wall clock run-time). In this study, we are proposing a solution to this problem with open-source software that allows users to seamlessly customize the scale and track the accuracy of grid operations models. Our approach allows users to search over numerous model parameters (network topology, mathematical formulation, economic hurdle rates, and transmission line scaling) to identify model instantiations that accommodate experimental design. Further, we use this approach to demonstrate the importance of including extreme weather events in model validation and model selection. Focusing on the occurrence of heatwaves and droughts in the U.S. Western Interconnection, we examine role of extreme events in balancing tradeoffs between model fidelity and run-time at the model design stage.

Economics↗
DOE OSTI2024

Valuing EV Managed Charging for Bulk Power Systems

When and where electric vehicle (EV) charging occurs has significant implications for power systems supporting widespread EV adoption, especially with high shares of wind and solar generation. This study extends previous works by leveraging detailed simulation models for EV adoption, EV use, EV charging, and bulk power system operations, and by linking them with methods for describing charging flexibility at both the individual vehicle and aggregate levels. This technical potential study focuses on how the value of EV managed charging (EVMC) changes depending on charging flexibility type (within-charging session or within-week scheduling), dispatch mechanism (direct load control or one of several price-based mechanisms), and managed charging participation rate. We show that naively aggregating EV charging flexibility from individual vehicles into megawatt-scale resources grossly overestimates the flexibility of the fleet, because such aggregate models can unrealistically pair, e.g., one already-fully-charged vehicle's ability to increase load with another already-charging vehicle's ability to accept more charge, effectively requesting a charging rate that is infeasible for the latter vehicle. We find per-vehicle bulk system value is highest at low participation rates for all dispatch mechanisms. Factoring in production cost savings, avoided firm capacity savings, and combustion-related power sector emissions savings, we estimate the value of EVMC at low participation rates (5%) to be $33/vehicle-year to $69/vehicle-yr for within-session charging flexibility and $40/vehicle-yr to $120/vehicle-yr for within-week charging flexibility in an envisioned 2038 New England power system and monetary value reported in 2016 U.S. dollars. At 100% participation, per-vehicle value declines to $25/vehicle-yr to $31/vehicle-yr for within-session charging flexibility and to $29/vehicle-yr to $36/vehicle-yr for within-week charging flexibility; however, 100% participation yields the highest total system savings.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Additively manufactured and cast high-temperature aluminum alloys for electric vehicle brake rotor application

Electric vehicle brake rotors demand lightweight, high thermal conductivity materials with good resistance to wear, creep, salt corrosion, and thermal fade, all of which present challenges to the traditionally used cast iron. In this work, we investigated the braking performance of recently developed high-temperature aluminum alloys in both cast and 3D printed forms, that possess excellent microstructural and mechanical stability at elevated temperatures. Three aluminum alloys, Al-6Cu-Mn-Zr, Al-9Cu-Mn-Zr, and Al-Ce-Ni-Mn-Zr and a reference cast iron were tested on a sub-scale brake tester against a commercial brake pad material over a range of sliding speeds between 2 and 15 m/s. The performance of these alloys was evaluated for wear resistance, friction behavior, temperature elevation, and surface morphological change. Although all three candidate alloys had lower wear-resistance than cast iron, Al-Ce-Ni-Mn-Zr showed a significantly reduced wear rate in comparison to the Al-Cu-Mn-Zr alloys. Moreover, Al-Ce-Ni-Mn-Zr alloy had the most consistent friction behavior at all sliding speeds and good fade resistance, as the coefficient of friction did not dramatically decrease with temperature rise but stayed within a desirable range of 0.35–0.50 instead. The superior wear resistance and braking performance of the Al-Ce-Ni-Mn-Zr alloy were attributed to its higher hardness, and high temperature yield strength and creep resistance compared with the Al-Cu-Mn-Zr alloys. In conclusion, the results suggest that the braking performance of these aluminum alloys could be further enhanced by increasing the hardness and forming a more stable transfer layer on the sliding surface.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Forecasting EV Charging Demand on the Distribution System

The U.S. transportation and electricity sectors have historically operated independently, but the growth of electric vehicles (EVs) is driving their convergence. After decades of stagnant demand, utilities must prepare for rising load growth, driven in part by transportation electrification. Utilities must anticipate when and where these new loads will materialize to effectively manage EV growth and maintain grid reliability. This presentation outlines NREL's approach to developing high-resolution EV load datasets for distribution planning, with insights from the Multi-State Transportation Electrification Impact Study on EV and load forecasting, infrastructure requirements, and managed charging strategies.

25 ENERGY STORAGE↗
DOE OSTI2022

Reinforcement Learning for Intelligent Building Energy Management System Control *

A building energy management system (BEMS) is a computer-based system designed to monitor and control a building's energy needs. Modern BEMS rely on the sensing and connectivity capabilities of Internet of Things (IoT) technology to intelligently adjust the energy consumption to reduce cost while respecting the consumers' preferences. Increasingly, control decisions are made based on predictions by models trained using supervised machine learning methods, which still requires control policies to be formulated in a rule-based fashion. When using reinforcement learning (RL) instead, control policies are learned by observing the utility in terms of cost and comfort associated with actions such as a change in the heating system's setpoint. The resulting RL-based controllers can capture not only the dynamics of the building and the associated electrical devices, but also fluctuations in electricity prices and user demand, avoiding the need to combine multiple predictive models with tailored control policies. This chapter will provide an overview of RL-based approaches for BEMS. After sketching the taxonomy of general RL methods, we discuss the implications of relying on the individual methods in a BEMS context. Existing work applying RL is presented along the key devices controlled by BEMS systems. Finally, we summarize the state-of-the-art and sketch limitations and open research directions.

Kotevska, Olivera↗
DOE OSTI2025

Valuation of Anode Materials for High-Performance Lithium Batteries: From Graphite to Lithium Metal and Beyond

Lithium-ion batteries have revolutionized energy storage, yet advanced technologies such as electric vehicles and eVTOLs demand even higher performance and safety. Anodes, the negative electrodes, are crucial in enhancing batteries’ safety, lifespan, and fast-charging capabilities. This review paper comprehensively evaluates the progression of anode materials from traditional graphite to advanced anodes like lithium metal. Graphite anodes, with a capacity of 372 mAh g −1 , enabled the first commercial lithium-ion batteries, but future applications require higher energy densities and fast-charging capabilities. Emerging anode materials, including alloying, and conversion types, as well as lithium metal, offer significantly higher capacities, with lithium metal offering a theoretical capacity of 3 860 mAh g −1 . However, these advanced anodes face challenges such as volume expansion, high surface reactivity, sluggish Li+ kinetics, and unstable lithium deposition morphologies. Here, this review critically examines the electrochemical performance, interfacial properties, mechanical attributes, and stability issues of various anode materials. It further discusses solid electrolyte interphase (SEI) formation, strategies for enhancing interface stability, and the requirements of anodes for solid-state batteries. Additionally, the review explores potential solutions for limitations with each anode type, highlights innovative anode-free architectures, and evaluates the current and future trends of battery anode industries. Ultimately, this paper aims to guide the development of high-performance anode materials, paving the way for the next generation of efficient, reliable lithium batteries.

Alloying anodes↗
DOE OSTI2024

Assessment of the impacts of renewable energy variability in long-term decarbonization strategies

To meet the nationally determined contributions proposed by the countries that signed the Paris Agreement, investments must be made in renewable generation technologies such as solar and wind. However, due to their high variability, these technologies pose challenges in terms of meeting demand or generating excess electricity. For this reason, energy system models are designed to capture this variability by considering flexibility technologies. Nevertheless, it is important to note that some energy system models lack integration with other sectors. Therefore, integrated assessment models have been employed to evaluate mitigation strategies, as they endogenously consider the linkages between energy and non-energy sectors. In addition, due to their complexity, these models do not account for the variability of renewable resources. Hence, this research aims to address this issue. Here, this work represents the first attempt to evaluate how the introduction of hourly resolution affects the outcomes of integrated assessment models, specifically focusing on the Global Change Analysis Model (GCAM). We employ a soft-linking approach between the GCAM and the Highway to Renewable Energy Systems model (H2RES, an hourly level energy system model) to accomplish this. The proposed approach is tested using Chile’s Nationally Determined Contributions under different hydrological profiles in the power sector. The results show that it is possible to use the capacity obtained from the Global Change Analysis Model and implement it on an hourly scale. However, the feasibility of implementation depends on high levels of flexibility technologies, such as battery energy storage. When given the choice of investments in renewable sources and flexible technologies, the optimal dispatch of the H2RES model show small differences than those obtained by GCAM-Chile. H2RES differs from GCAM-Chile in approximately 5% for wind and 3% for solar electricity generation in the year 2050. However, feasible integration of significant renewable sources is obtained with relatively high Critical Excess Electricity Production levels, reaching 20% in 2050. This excess electricity is attributed to the necessity for flexible technologies to manage the intermittency of renewables sources when hourly profiles of such sources are considered.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Carbon decorated Li 3 V 2 (PO 4 ) 3 for high-rate lithium-ion batteries: Electrochemical performance and charge compensation mechanism

Fast charging and high-power delivering batteries are highly demanded in mobile electronics, electric vehicles and grid energy storage, but there are full of challenges. The star-material Li 3 V 2 (PO 4 ) 3 is demonstrated as a promising high-rate cathode material meeting the above requirements. Herein, we report the carbon decorated Li 3 V 2 (PO 4 ) 3 (LVP/C) cathode prepared via a facile method, which displays a remarkable high-rate capability and long-term cycling performance. Briefly, the prepared LVP/C delivers a high discharge capacity of 122 mAh g -1 (~93% of the theoretical capacity) at a high rate up to 20 C and a superior capacity retention of 87.1% after 1000 cycles. Importantly, by applying a combination of X-ray absorption spectroscopy and full-range mapping of resonant inelastic X-ray scattering, we clearly elucidate the structural and chemical evolutions of LVP upon various potentials and cycle numbers. Furthermore, we show unambiguous spectroscopic evidences that the evolution of the hybridization strength between V and O in LVP/C as a consequence of lithiation/delithiation is highly reversible both in the bulk and on the surface during the discharge-charge processes even over extended cycles, which should be responsible for the remarkable electrochemical performance of LVP/C. Our present study provides not only an effective synthesis strategy but also deeper insights into the surface and bulk electrochemical reaction mechanism of LVP, which should be beneficial for the further design of high-performance LVP electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2021

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

The pursuit of scalable and manufacturable all-solid-state batteries continues to intensify, motivated by the rapidly increasing demand for safe, dense electrical energy storage. Here, we describe the numerous, often conflicting implications of materials choices that have been made in the search for effective mitigations to the interfacial instabilities plaguing solid-state batteries. Specifically, we show that the manufacturing scalability of solid-state batteries can be governed by at least three principal consequences of materials selection: (1) the availability, scaling capacity, and price volatility of the chosen materials’ constituents, (2) the manufacturing processes needed to integrate the chosen materials into full cells, and (3) the cell performance that may be practically achieved with the chosen materials and processes. While each of these factors is, in isolation, a pivotal determinant of manufacturing scalability, we show that consideration and optimization of their collective effects and trade-offs is necessary to more completely chart a scalable pathway to manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2024

Ligand-channel-enabled ultrafast Li-ion conduction

Li-ion batteries (LIBs) for electric vehicles and aviation demand high energy density, fast charging and a wide operating temperature range, which are virtually impossible because they require electrolytes to simultaneously have high ionic conductivity, low solvation energy and low melting point and form an anion-derived inorganic interphase. We report guidelines for designing such electrolytes by using small-sized solvents with low solvation energy. The tiny solvent in the secondary solvation sheath pulls out the Li + in the primary solvation sheath to form a fast ion-conduction ligand channel to enhance Li + transport, while the small-sized solvent with low solvation energy also allows the anion to enter the first Li + solvation shell to form an inorganic-rich interphase. The electrolyte-design concept is demonstrated by using fluoroacetonitrile (FAN) solvent. The electrolyte of 1.3 M lithium bis(fluorosulfonyl)imide (LiFSI) in FAN exhibits ultrahigh ionic conductivity of 40.3 mS cm -1 at 25 °C and 11.9 mS cm -1 even at -70 °C, thus enabling 4.5-V graphite||LiNi 0.8 Mn 0.1 Co 0.1 O 2 pouch cells (1.2 Ah, 2.85 mAh cm -2 ) to achieve high reversibility (0.62 Ah) when the cells are charged and discharged even at -65 °C. The electrolyte with small-sized solvents enables LIBs to simultaneously achieve high energy density, fast charging and a wide operating temperature range, which is unattainable for the current electrolyte design but is highly desired for extreme LIBs. This mechanism is generalizable and can be expanded to other metal-ion battery electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2025

Spatial Impacts of Electric Vehicle Charging on Power Grid Stability: A Downtown Atlanta Case Study

The rapid increase in electric vehicle (EV) charging demand poses a potential risk to power grid stability, particularly as the spatial distribution of this demand remains underexplored. Existing research often focuses on technical optimization models while overlooking the geographic and human dynamics that affect energy consumption. This study addresses this gap by incorporating mobility data to estimate both building energy use and EV charging demand while also considering geographic factors for a better understanding of grid load. Using agent-based simulations and the Open-Source Distribution System Simulator, the study evaluates the effect of various EV penetration scenarios on grid voltage and unbalance. The results show that, although voltage remains within acceptable limits at lower EV penetration rates, significant voltage drop and unbalance occur as EV penetration exceeds 40%, particularly in residential areas with high charging demand. This study offers a framework for integrating spatial analysis and mobility data in power network simulations, providing insights for future EV infrastructure planning.

Pan, Melrose [ORNL] (ORCID:000000031627448X)↗
DOE OSTI2023

Design, modeling and simulation of nuclear-powered integrated energy systems with cascaded heating applications

Nuclear-renewable integrated energy systems (IES) consist of a variety of energy generation and conversion technologies and can be used to meet heterogeneous end uses (e.g., electricity, heat, and cooling demands). In addition to supply-demand balance, end-use heat demands usually require heat supply of certain temperature ranges. The effective and efficient utilization of heat produced within an IES is, therefore, a critical challenge. Here, this paper examines design options of an IES that includes heating processes of multiple temperature grades. We investigate a cascaded design configuration, where the remaining residual heat after high-grade heating processes [e.g., hydrogen production through high-temperature steam electrolysis (HTSE)] is recovered to meet the low-grade heating needs [e.g., district heating (DH)]. Additionally, a thermal energy storage system is integrated into the DH system to address the imbalance between heat supply and demand. This paper primarily focuses on the design and modeling of the proposed system and evaluates its operation with a 24-h transient process simulation using a DH demand profile with hourly resolution. The results indicate that the residual heat from the HTSE exhaust is insufficient for the DH demand, and additional topping heat directly from the reactor process steam is needed. Furthermore, the inclusion of thermal energy storage within the DH system provides the necessary balance between thermal generation and demand, thereby ensuring a consistent rated temperature of the DH supply water. This approach helps minimize the control actions needed on the reactor side.

08 HYDROGEN↗
DOE OSTI2024

Balancing Charging Station Utilization and Throughput in Electric Vehicle Charging Stations with Queuing Theory

The rapid increase in electric vehicle (EV) adoption demands enhancements in the efficiency and adaptability of EV supply equipment (EVSE). Traditional EVSE systems often fail to optimize power delivery to meet the variable acceptance rates of EV batteries, resulting in significant energy wastage and reduced operational efficiency. This research addresses these challenges by integrating queuing theory with modular EVSE architectures, offering a dual strategy to optimize the operation of EV charging stations. A simulation model was developed to assess various configurations of charger capacities and outlet numbers. This model aimed to identify the optimal setup that maximizes station utilization while minimizing charging times and maximizing throughput. The model focused on charger capacities ranging from 50 to 250 kW and analyzed the different capacities’ effects on charging times and the number of vehicles served. The results indicate that a charger capacity of 125 kW is optimal, striking a balance between the charging time and the number of EVs served per hour, thus achieving the highest station utilization rate. This capacity allows for servicing a significant number of EVs with moderate increases in charging times. Lower capacities, although capable of serving more vehicles, lead to longer charging times and decreased throughput efficiency. The study underscores the effectiveness of combining queuing theory with flexible, modular charging systems that can dynamically adjust to EV charging demands.

Kumar, Praveen↗