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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 181 records · Page 10

All sources NASA NTRS DOE OSTI

DOE OSTI2021

A Data-Driven Multi-Period Importance Sampling Strategy for Stochastic Economic Dispatch

Power systems with high penetrations of renewable energy (e.g., wind power) require sophisticated approaches to optimize system performance due to uncertainty in short-term system generation capacity. In this paper, we combine a data-driven analog scenario selection method with importance sampling to create a novel scenario construction approach for two-stage stochastic economic dispatch problems with a large number of wind farms on a network. The proposed method produces scenarios with realistic physics by finding high-fidelity analogs that can describe future states of the system. We show how to extend this method to multi-period operations and demonstrate the effectiveness of this technique by simulating economic dispatch operations on a synthetic test system over the course of a week.

data-driven forecasting↗

DOE OSTI2022

Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment

Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using neural networks. These neural networks can be exactly transformed into mixed integer linear programs and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear approximations. Such approaches, though, suffer from an explosion of the number of binary variables needed to represent the neural network. Accordingly, this paper develops a technique for training an "optimally compact'' neural network, i.e., one that can represent the power flow equations with a sufficiently high degree of accuracy while still maintaining a tractable number of binary variables. We demonstrate the use of this neural network as an approximator of the nonlinear power flow equations by embedding it in the AC unit commitment problem, transforming the problem from a mixed integer nonlinear program into a more manageable mixed integer linear program. We use the 14-, 57-, and 89-bus networks as test cases and compare the AC-feasibility of commitment decisions resulting from the neural network, DC, and linearized power flow approximations. Our results show that the neural network model outperforms both the DC and linearized power flow approximations when embedded in the unit commitment problem. The neural network formulation most often selects a feasible unit commitment schedule, and furthermore, it only s

AC power flow↗

DOE OSTI2020

Design screening and analysis of gas-fired ammonia-based chemisorption heat pumps for space heating in cold climate

Thermally-driven ammonia-based chemisorption heat pumps (CSHP) have the potential to provide high-efficiency space heating in cold climates. Using the reversible chemical bond between sorbent salt and ammonia, CSHP thermochemically pumps heat from the cold ambient to the end-uses of space heating at 50 °C. The heating coefficient of performance (COP) of a CSHP largely depends on the selection of the sorbent salts, cycle configuration, and the system operation. This study uses a thermodynamic model to investigate the performance of six CSHP system configurations, including four single-effect and two double-effect cycles. The feasibility and performance of 121 available NH 3 /salt reactions are studied for each configuration. The thermal COP of the cycles and the primary energy COP of the gas-fired CSHP systems are evaluated assuming 50 °C supply temperature for building space heating and the optimal system designs are identified. The highest thermal COP for single-effect and double-effect cycles under -25 °C ambient temperatures are predicted to be 1.22 and 1.57, respectively. The corresponding primary energy COPs are above 1.0 and 1.15, which are 30% higher than condensing furnaces and is sustained into the same cold temperatures.

42 ENGINEERING↗

DOE OSTI2025

Direct Air Capture Case Studies: Limestone Looping Screening-Level Analysis

A screening-level techno-economic analysis of a passive limestone looping direct air capture system is presented. The base case examined in this report does not represent an optimized system and makes many simplifying assumptions due to the nascence of the technology and lack of publicly available information. The base case serves as a reference for detailed and informative sensitivity analyses. This initial study (1) reveals that improving reaction rate and tray configuration can potentially significantly reduce cost, and (2) indicates that LL has the potential to be cost competitive with other DAC technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Optimizing Bifacial PV Systems (Q3 FY2020 Project Report)

This project has four main technical objectives. Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. Evaluate optimal bifacial system designs using simulations leveraging high performance computing, and also using full sized and miniaturized experimental field deployments. Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

14 SOLAR ENERGY↗

DOE OSTI2025

Optimizing a detection system for fissile material in nuclear disarmament verification

In arms control treaties, verification plays a crucial role in detecting non-compliance, deterring future violations, and building trust between state parties. Neutron interrogation that induces fission reactions in fissile isotopes and measures the resulting fission neutrons, could be employed for this purpose. This study aims to develop a system which can determine the presence of fissile material while intrinsically protecting information. In this paper, we focus on optimizing the system for discriminating between an enriched uranium block from a depleted uranium (DU) block. The system was built and we report on benchmark measurements with DU and 16% enriched uranium blocks. Furthermore, the Excalibur (Experiment for Calibration with Uranium) neutron source, a neutron spectrometer (redBubble Technology Industries (BTI) N-Probe), and superheated droplet detectors were used for these measurements. MCNP simulations provided insights into detector responses to fissile materials with varying isotopic compositions, confirming that the system functioned as designed.

Active neutron interrogation↗

DOE OSTI2021

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. Here, we also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.

42 ENGINEERING↗

DOE OSTI2024

A Perspective on the Impact of Group Delay Dispersion in Future Terahertz Wireless Systems

This article discusses the challenges and opportunities of managing group delay dispersion (GDD), and its relation to the performance standards of future sixth-generation (6G) wireless communication systems utilizing terahertz frequency waves. The unique susceptibilities of 6G systems to GDD are described, along with a quantitative description of the sources of GDD, including multipath, rough surface scattering, intelligent reflecting surfaces, and propagation through the atmosphere. An experimental case-study is presented that confirms previous models quantifying the impact of atmospheric GDD. Several GDD manipulation strategies are presented, illustrating their hindered effectiveness in the 6G context. Conversely, some benefits of leveraging GDD to enhance 6G systems, such as improved security and simplified hardware, are also discussed. Finally, a perspective on using photonic GDD control devices is provided, revealing quantitative benefits that may unburden existing equalization schemes. Here, the article argues that GDD will uniquely and significantly impact some 6G systems, but that its careful consideration along with new mitigation strategies, including photonic devices, will help optimize system performance. The conclusion provides a perspective to guide future research in this area.

Strecker, Karl↗

DOE OSTI2021

2021 Q1 Project Report: Optimized Bifacial PV Systems (Q1 FY2021 Project Report)

This project has four main technical objectives: 1) Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. 2) Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. 3) Evaluate optimal bifacial system designs using simulations leveraging high-performance computing, and also using full sized and miniaturized experimental field deployments. 4) Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

42 ENGINEERING↗

DOE OSTI2023

Electrolyzers in the System Advisor Model (SAM): A Techno-Economic Potential Study

A technoeconomic analysis of grid to low temperature electrolysis (Grid-LTE), photovoltaic to low temperature electrolysis (PV-LTE), concentrating solar power to high temperature electrolysis (CSP-HTSE) and a concentrating solar power with PV to high temperature electrolysis (CSP-PV-HTSE) centralized hydrogen production systems are analyzed to assess the economics of system and to provide a baseline for comparing these technologies against hydrogen production cost targets. A framework integrating the system advisor model (SAM) and US Department of Energy hydrogen production models (H2A) is developed to assess these systems. The hydrogen levelized cost given current and future assumptions for technology cost and performance is evaluated at optimal system configurations. The framework described in this report integrates SAM with H2A electrolyzer technologies and provides analysts a detailed technoeconomic method to analyze concentrating and photovoltaic solar technologies to produce energy that are directly coupled to LTE and HTSEs that use that energy to split water into hydrogen and oxygen. The baseline hydrogen levelized cost (HLCs) for the GRID-LTE, PV-LTE, CSP-HTSE, and CSP-PV-HTSE systems in Daggett, CA are 2.82, 3.86, 3.68, and 2.90 $\$$USD 2016/kg H2 and 2.50, 2.13, 2.84, 2.15 $\$$USD 2016/kg H2 in the 2020 and 2050 scenarios respectively. To achieve the $\$$2/kg H2 target in locations with excellent solar resources, cost parameters values aligned with aggressive R&D targets will need to be achieved for all the systems configurations. In Daggett, PV costs of $\$$0.68 /Wac or moderate ATB PV CAPEX projections result in HLCs of $\$$2 /kg H2. Similarly for PV-MSALT-HTSE systems, $\$$0.60 /Wac result in $\$$2/kg H2. For the MSALT-HTSE systems, better than aggressive 2050 ATB salt tower CAPEX projections would be needed to reach $\$$2/kg H2. Molten salt tower capital costs of $\$$2400/kW would enable $\$$2/kg H2 in 2050.

08 HYDROGEN↗

DOE OSTI1978

Plant Engineers Solar Energy Handbook: Southern California Region

Discussed in order after the introduction are solar components and systems (collectors, storage, service hot water systems, space heating with liquid and air systems, space cooling, heat pumps and controls); computer programs for system optimization; local solar and weather data; a description of buildings and plants in Southern California applying solar technology; current Federal and California solar legislation; standards, codes and performance testing information; a listing of manufacturers, distributors, and professional services available in Southern California region; and information access. Finally, solar design check lists for those engineers who wish to design their own systems. The program for the Solar Workshop for the Plant Engineer, March 30, 1978, Los Angeles, California is included.

14 SOLAR ENERGY↗

DOE OSTI2022

Analysis of Multi-Output Hybrid Energy Systems Interacting with the Grid: Application of Improved Price-Taker and Price-Maker Approaches to Nuclear-Hydrogen Systems

The growing recognition of the value of hydrogen as an energy intermediate in supporting future power systems with high shares of variable renewable energy has prompted many studies to quantify the economic potential of multi-output hybrid systems, which are one type of integrated energy systems (IES). Because of the complexity of modeling multiple sectors, these studies typically use simplified modeling approaches to capture the interactions between sectors. In this study, we explore the implications of alternative modeling approaches for nuclear-hydrogen IES focusing on a power system in the Midwest United States. We combine highly resolved capacity expansion and production cost modeling tools of the power system with a detailed hydrogen system optimization tool to determine the optimal electrolyzer and storage sizing and optimal operations of the nuclear-hydrogen hybrid resource across three future study years. We compare economic and operational outcomes across a spectrum of modeling approaches, including a non-hybridized base approach; a traditional price-taker approach that does not include the impact of hydrogen production on the electricity system; a power-system-focused price-maker approach that does not account for temporal hydrogen constraints; and two improved price-taker and price-maker approaches that each address the impact of revenue-optimal levels of electricity production on the resulting power system and temporal hydrogen constraints on the overall feasible solution. Results show how a traditional price-taker approach can overestimate the economic benefits of multi-output nuclear-hydrogen IES compared to our two improved approaches that estimate both hydrogen system constraints and power system interaction. We find that hydrogen output requirements and storage size limits are key drivers to overall operations and some economic outcomes. Under our assumed constant hydrogen output requirement, storage costs, test system, and modeling approaches, our results indicate that hybridization can provide a net benefit, but results are sensitive to the treatment of hydrogen revenues and electricity prices as impacted by the power system evolution.

capacity expansion modeling↗

DOE OSTI2020

Resiliency-based restoration optimization for dependent network systems against cascading failures

Due to the increasing importance of large-scale and complex network systems and the potential for massive cascading failures in these real-world systems, modeling of system resiliency and optimization of restoration strategies to mitigate system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. Although society has experienced many incidents that demonstrate the influence of cascading failures aggravated by dependencies inside network systems, existing resiliency-based restoration optimization research rarely if ever jointly considers the impact of system dependencies on cascading failures. In this paper, different restoration prioritization strategies are applied to network systems subject to cascading failures that take into account system dependencies. By conducting case studies on synthetic networks and the U.S. airport network system, the effects of restoration strategies are evaluated using a system resiliency metric and two system performance measurements. Furthermore, the influence of system dependency characteristics and the interplay between them and restoration strategies on system resiliency regarding cascading failures are also investigated. This work demonstrates the distinct effects of restoration prioritization actions against cascading failures to mitigate the performance loss in network systems with different properties. It also provides insights about restoration improvement by considering dependency impacts to effectively reduce the intensity and extent of cascading failures.

42 ENGINEERING↗

DOE OSTI2022

An analytical method for identifying synergies between behind-the-meter battery and thermal energy storage

Electric utilities build generation capacity to meet the highest demand period, and they often pass on the costs associated with these peaking generators to building owners through demand charges. Building owners can minimize these demand charges by shifting energy use away from peak periods with behind-the-meter storage. This storage can include batteries, which can directly shift the metered load, or thermal energy storage, which can shift thermal-driven electric loads like air conditioning. However, there is a lack of research on how best to combine battery and thermal energy storage. In this study, we develop an analytical sizing method to calculate the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. We show that adding batteries to a thermal energy storage system can increase the total system's load shaving potential. This is particularly true when the building has onsite photovoltaic generation or electric vehicle charging, which add significant variability to the load shape. We also show that for a given total storage size, selecting a higher fraction of thermal energy storage can significantly lower the cycling of the battery, and therefore extend the battery life. This, combined with the expected lower first cost of thermal energy storage materials compared to batteries, shows that hybrid energy storage systems can outperform a standalone battery or standalone thermal storage system. Assuming the thermal storage has a capital cost 6x lower than the battery, our analysis shows that the optimal system is 71% thermal energy storage and 29% battery energy storage for a scenario with electric vehicle charging. The annualized cost savings for this system are $48.6 k/yr, whereas an equivalently sized standalone thermal energy storage system would provide annualized cost savings of $28.5 k/yr and a standalone battery would lead to savings of $8.72 k/yr. The hybrid system also reduces battery cycling by 52% compared to a standalone battery, extending battery lifetime.

25 ENERGY STORAGE↗

DOE OSTI2020

Phase I Final Technical Report on Energy-Efficient Reconfigurable Universal Accelerator Interconnect

It is well known that application specific computing systems, optimally designed and configured for a given workload, offer much higher energy-efficiency and throughput than general purpose systems. In modern computing systems, heterogeneous computing systems have emerged that exploit the energy and performance benefits of combining various different domain-specific processor architectures. Application domains such as high-performance computing and machine learning now process terabyte-sized data sets, requiring enormous processing and memory resources. These applications have very high-power consumption due to bottlenecks in the electrical interconnection between processing units. This project aims to reduce both communication energy and latency by integrating universally available accelerators with silicon photonics. It also aims to increase system throughput by exploiting emerging technologies in silicon photonic reconfigurable interconnects; this will allow the system to balance itself in real time to accommodate changes in workloads and data flows.

2.5D/3D integration↗

DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

42 - ENGINEERING↗