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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

State Charlton & MRCSP 1 Characterization Well Report

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. This report describes the efforts to drill and complete a well in the St. Peter sandstone for characterization and evaluation of storage potential in norther Michigan. However, as presented in this report, the well was not drilled and completed up to the intended target intervals due to drilling related difficulties. While the drilling issues were being resolved, the new Class VI injection well regulations were finalized by the US EPA. MRCSP efforts focused on evaluating CO 2 storage and containment using site characterization, modeling, and monitoring in conjunction with the ongoing CO 2 -EOR operations in the same area. This report describes work associated with the initial test well, the State Chester and MRCSP Well No. 1, drilled to a depth of about 7,968 feet TVD, and subsequently plugged and designated as temporarily abandoned. A high-level overview of data collected and analyzed is provided for shallower units.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Laser-Based Means for Accelerating Nuclear Decay Rate

This project aims to replicate and build upon a reported series of results whereby nuclear decay of unstable species has been accelerated under laser irradiation in the presence of resonantly excited plasmonic nanoparticles. Most theoretical investigations of this problem do not predict the enhancement to be significant. Further, alternate explanations for the observed decrease in signals associated with the supposedly destroyed species need to be examined. There are potentially substantial benefits from exploiting this effect to reduce the long-term radiological storage requirements of certain forms of nuclear waste. These benefits merit the confirmation or disproving of the effect.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Performance and Cost Potential for Exemplar Direct Supercritical Carbon Dioxide Natural Gas Plants

This report presents the techno-economic analysis (TEA) optimization results of natural gas-fired utility-scale power plants based on the direct supercritical carbon dioxide (sCO 2 ) power cycle. To identify optimum plant configuration, the study also considered three different cases (Case A, Case B, Case C) with varying levels of thermal integration with the plant air separation unit (ASU). A fourth case (Case D), which is based on a patent from 8 Rivers, was also considered and includes thermal integration with the ASU as well as compressed recycle carbon dioxide (CO 2 ) gas. The four direct sCO 2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on an F-class gas turbine with carbon capture and storage (CCS). LCOE of the direct sCO 2 plants is 13.5–17.2 percent higher than the reference NGCC plants with CCS due to higher capital costs associated with the ASU and sCO 2 power block. Recuperators make up over 50 percent of the power cycle costs. Consequently, any research and development (R&D) efforts to reduce the recuperator capital costs will be beneficial for the technology commercialization. The study also investigated the impact of co-firing landfill gas (LFG) and natural gas on plant efficiency, LCOE, and CO 2 emissions. Increasing the LFG co-firing from 0 percent to 50 percent (mass basis), decreased the plant efficiency by 0.3 percentage points and increased the LCOE by 3 percent. Due to high inherent CO 2 capture rates, direct sCO 2 plants have strong potential to achieve net-zero CO 2 emissions with LFG and natural gas co-firing.

03 NATURAL GAS↗
DOE OSTI2021

Kimberlina 1.2 Velocity Models and Seismic Data

Kimberlina 1.2 Velocity model and synthetic seismic data, produced in collaboration of teams at the National Energy Technology Laboratory, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory through the National Risk Assessment Partnership. Data is associated with the following publication: Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019. The Kimberlina 1.2 Velocity models were produced by Zan Wang, Robert Dilmore, William Harbert, and Lianjie Huang at NETL. The following citations are directly related to the creation of the velocity models: Wang, Z. Harbert, W., Dilmore, R., Huang, L. Modeling of time-lapse seismic monitoring using CO2 leakage simulations for a model CO2 storage site with realistic geology: Application in assessment of early leak-detection capabilities. International Journal of Greenhouse Gas Control. V. 76, September 2018, Pages 39-52. https://doi.org/10.1016/j.ijggc.2018.06.011 Wang, Z., Dilmore, R., Harbert, W. Inferring CO2 saturation from synthetic surface seismic and downhole monitoring data using machine learning for leakage detection at CO2 sequestration sites. International Journal of Greenhouse Gas Control, V. 100, September 2020. https://doi.org/10.1016/j.ijggc.2020.103115 The velocity models were built based on the Kimberlina 1.2 aquifer impact data which is associated with the following publications: Buscheck, T.A., Mansoor, K., Yang, X., Wainwright, H., and Carroll, S. (2019). Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir. International Journal of Greenhouse Gas Control. 91. 102812. 10.1016/j.ijggc.2019.102812. Xianjin Yang, Thomas A. Buscheck, Kayyum Mansoor, Zan Wang, Kai Gao, Lianjie Huang, Delphine Appriou, Susan A. Carroll, Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, Volume 90, 2019, 102803, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2019.102803 The synthetic seismic data was produced by Youzuo Lin and team at LANL, and are associated with the following citations: Jordan, P. D., and J. L. Wagoner. Characterizing Construction of Existing Wells to a CO2 Storage Target: The Kimberlina Site, California. Zheng Zhou, Youzuo Lin, Zhongping Zhang, Yue Wu, Zan Wang, Robert Dilmore, and George Guthrie, "A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks," International Journal of Greenhouse Gas Control, Vol 90, 2019.

CO2 Leakage↗
DOE OSTI2023

Control of the Structural Charge Distribution and Hydration State upon Intercalation of CO 2 into Expansive Clay Interlayers

Numerous experimental investigations indicated that expansive clays such as montmorillonite can intercalate CO 2 preferentially into their interlayers and therefore potentially act as a material for CO 2 separation, capture, and storage. However, an understanding of the energy–structure relationship during the intercalation of CO 2 into clay interlayers remains elusive. Here, in this study, we use metadynamics molecular dynamics simulations to elucidate the energy landscape associated with CO 2 intercalation. Our free energy calculations indicate that CO 2 favorably partitions into nanoconfined water in clay interlayers from a gas phase, leading to an increase in the CO 2 /H 2 O ratio in clay interlayers as compared to that in bulk water. CO 2 molecules prefer to be located at the centers of charge-neutral hydrophobic siloxane rings, whereas interlayer spaces close to structural charges tend to avoid CO 2 intercalation. The structural charge distribution significantly affects the amount of CO 2 intercalated in the interlayers. These results provide a mechanistic understanding of CO 2 intercalation in clays for CO 2 separation, capture, and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Potential for Transactive Energy to Improve the Provisioning of Grid Services from Batteries

This study assessed the degree to which transactive energy systems could help reduce or remove barriers to the deployment of battery energy storage, and realize the full potential of battery resources to supply needed services to the grid and fairly compensate various types of battery owners. To enable this assessment, typical battery deployments were characterized, along with energy markets, Federal Energy Regulatory Commission Order rulings and implementations, grid services, and current deployment barriers. Finally, this study analyzed the value that accrues to batteries supplying today’s grid services as a function of the participation models associated with three primary types of battery ownership: merchant-owned transmission-connected batteries; utility-owned distribution-connected batteries; and customer-owned behind-the-meter batteries. This provided both quantitative and qualitative assessments comparing opportunities for battery storage in business-as-usual and transactive energy scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Disposition of Newly Generated Transuranic Waste at ORNL - 20107

ORNL operates the High Flux Isotope Reactor, which provides a high neutron source for production of isotopes for medical, industrial, and nuclear research programs and is the US DoE's center for production, storage, and distribution for TRU isotopes (plutonium through californium). Operation of these facilities results in the generation of CH-TRU and RH-TRU waste for disposal at the WIPP. This paper describes the program ORNL is implementing in collaboration with the Carlsbad Field Office National TRU Program Difficult Waste Team to disposition newly generated TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Zigzag flow reactor for weekly thermochemical energy storage

This paper describes theoretical models and experimental performance of a novel Zigzag Flow Reactor (ZFR) for weekly thermochemical energy storage. The ZFR reduces redox-active metal oxide (MO x ) particles at high temperature (up to ~1100 °C) under inert gas sweep. A physical model demonstrates the approach to process equilibrium by minimizing the associated exergy destruction in a finite number of reaction steps, establishing the thermodynamic requirements for a practical reactor. The model results show several cost-relevant parameter tradeoffs, and the tradeoff analysis implies a cost-optimized set of boundary conditions. Numerical models and prototypes show that the ZFR enables significant gas phase homogenization while simultaneously enabling a customizable MO x residence time in the reactor, both key requirements for approaching an equilibrium process. A scaling model demonstrates the simplicity and affordability of sizing the ZFR to grid-scale levels, with fabrication costs at least five times lower than previously proposed scalable reactor concepts. As a result, a laboratory ZFR prototype achieved an energy storage density of ~90 Wh/kg with CaAl 0.2 Mn 0.8 O 3-δ as the MO x , at temperatures of ~850 °C in >10 h of total runtime.

Thermochemical energy storage↗
DOE OSTI2024

Adsorption, Orientation, and Speciation of Amino Acids at Air–Aqueous Interfaces for the Direct Air Capture of CO 2

Amino acids make up a promising family of molecules capable of direct air capture (DAC) of CO 2 from the atmosphere. Under alkaline conditions, CO 2 reacts with the anionic form of an amino acid to produce carbamates and deactivated zwitterionic amino acids. The presence of the various species of amino acids and reactive intermediates can have a significant effect on DAC chemistry, the role of which is poorly understood. In this study, all-atom molecular dynamics (MD) based computational simulations and vibrational sum frequency generation (vSFG) spectroscopy studies were conducted to understand the role of competitive interactions at the air–aqueous interface in the context of DAC. We find that the presence of potassium bicarbonate ions, in combination with the anionic and zwitterionic forms of amino acids, induces concentration and charge gradients at the interface, generating a layered molecular arrangement that changes under pre- and post-DAC conditions. In parallel, an enhancement in the surface activity of both anionic and zwitterionic forms of amino acids is observed, which is attributed to enhanced interfacial stability and favorable intermolecular interactions between the adsorbed amino acids in their anionic and zwitterionic forms. The collective influence of these competitive interactions, along with the resulting interfacial heterogeneity, may in turn affect subsequent capture reactions and associated rates. Finally, these effects underscore the need to consider dynamic changes in interfacial chemical makeup to enhance DAC efficiency and to develop successful negative emission and storage technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Soil carbon formation is promoted by saturation deficit and existing mineral-associated carbon, not by microbial carbon-use efficiency

Mineral-associated organic carbon (MAOC) is the largest terrestrial pool of organic carbon, yet controls on its formation remain unresolved. Existing MAOC is thought to preclude additional C storage on minerals, but this perspective is difficult to reconcile with observations that MAOC stacks in multilayers, suggesting that existing MAOC could promote greater C retention. Here, in a manipulative experiment using 118 soils from 15 agricultural sites across the United States, we show that MAOC formation is promoted by both existing MAOC and its counterpart—MAOC saturation deficit. The positive effect of existing MAOC on the formation of new MAOC persists after accounting for soil physicochemical properties that covary with MAOC. In contrast with current theory, we found that MAOC formation was not clearly influenced by microbial carbon-use efficiency (CUE). Our findings demonstrate that existing MAOC and saturation deficit, not microbial CUE, are key to determining new MAOC formation in agricultural soils.

King, Alison E. [Colorado State Univ., Fort Collin↗
DOE OSTI2022

The calculation of light element impurity (α, n) yield curves in a PuO 2 matrix and associated specific yield coefficients: Influence of the reaction cross sections

Most of the Pu separated from irradiated commercial nuclear fuel is stored as PuO 2 . The primary quantitative nondestructive measurement technique used to verify the amount of Pu in storage containers is passive neutron correlation counting. An important physical property of the oxide material is the ratio, α, of the rate of (α, n) neutrons produced inside the item to the rate of neutrons produced by spontaneous fission. This ratio influences the precision of the correlated counting method and affects the interpretation of the data because of how it changes both the primary total neutron production rate and the rate of induced fission events taking place inside the item. In addition to the main O(α, n) contribution, additional contributions come from α-particle interactions with light element impurities that are inevitably present. In this work, we calculate specific (α, n) yield coefficients, expressed in units of neutrons per second per gram of α-emitting nuclide per part per million by mass of the specified impurity element distributed in a pure PuO 2 matrix, for some key α-emitting actinides commonly present in reprocessed Pu ( 238–242 Pu+ 241 Am). These coefficients are directly applicable to nuclear safeguards verification work in which the α ratio is often calculated from the Pu-isotopic composition and chemical information obtained by other means. Further, they also provide a convenient up-to-date reference set against which values generated by other methods can be compared. Results are presented for impurities with atomic number from 3 to 17 inclusive, plus K and Fe. In most cases, these coefficients are not expected to change by more than 5%–10% at any time in the future. However, as new data become available, changes as large as 20% may be needed for some targets (e.g., F). The present yield calculations are limited by the general shortage of quality experimental total (α, n) reaction cross section data, which, together with unexplained variation between determinations, means that an objective and coherent evaluation is not possible. The situation is even less satisfactory for the partial differential cross section needed to calculate neutron spectra.

(𝛼, n) reactions↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Assessing Nonlinear Polarization in Electrochemical Cells using AC Impedance Spectroscopy

AC impedance spectroscopy is widely used to evaluate performance limitations in energy storage and conversion devices (e.g., batteries, supercapacitors, and fuel cells). This work shows that integrating the resistive elements in an equivalent circuit as functions of steady-state current enables one to recover overpotentials associated with different processes (e.g., ion migration, charge transfer, and diffusion) in nonlinear electrochemical power supplies. Closed form expressions for diffusion overpotentials are derived using this method for transmissive and reflective boundary conditions and three electrode symmetries (planar, cylindrical, and spherical). Discussion is also extended to macroscopically homogenous porous electrodes which are relevant for most real-world devices. Overall, the approach described herein is a powerful tool to identify rate-limiting steps and guide material/component design.

25 ENERGY STORAGE↗
DOE OSTI2023

Considerations for Fire Service Response to Residential Battery Energy Storage System Incidents

Renewable energy sources (e.g., rooftop photovoltaics, wind turbines) are capable of addressing our grid-level energy challenges by reducing environmental impacts, increasing resiliency, and increasing the supply of input energy. However, renewable energy sources alone are not a complete solution. The generation of power from major renewable energy sources fluctuates with the weather, creating significant challenges in matching electrical power generation to electrical power consumption. The value of renewable power generation sources is multiplied when paired with battery energy storage systems (ESS), which can store excess power generated from renewable energy sources when it is not consumed and deliver that power later when demand exceeds base power generation. Residential ESS units are frequently installed to support renewable energy initiatives by storing energy from intermittent sources such as photovoltaic panels. Residential ESS units also offer an alternative to gas-powered generators or other backup power means. Lithium-ion batteries are the most common residential ESS technology due to their affordability and energy density. Though lithium-ion systems come with benefits, there is also a risk of thermal runaway within this technology which can result in flammable gas release, fire, and explosion. As the installations of these products increase, the frequency of response to fire incidents involving these products will increase. In response to this new and evolving hazard, UL Solutions (ULS) and UL Fire Safety Research Institute (FSRI) have partnered with the International Association of Fire Fighters (IAFF) to conduct a series of large-scale tests sponsored by the US Department of Energy to characterize the challenges for fire fighters responding to fires involving residential energy storage systems. The project focuses on developing size-up and tactical considerations to support the fire service in navigating the evolving modern fireground.

25 ENERGY STORAGE↗
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 OSTI2023

The Role of Atmospheric Rivers on Groundwater: Lessons Learned From an Extreme Wet Year

Abstract In the coastal regions of the western United States, atmospheric rivers (ARs) are associated with the largest precipitation generating storms and contribute up to half of annual precipitation, but the impact of ARs on the integrated hydrologic cycle, specifically on groundwater storage and hydrodynamics, is largely unknown. To better explore the hydrologic behavior of AR versus non‐AR event precipitation, we present a novel combination of two water tracking methods (one in the atmosphere and one in the subsurface) to explicitly track the full lifecycle of water parcels generated by ARs. Simulations of northern California's Cosumnes River watershed during the record wet 2017 water year are performed via the coupling of a high‐resolution regional climate model and a land surface‐groundwater model accounting for lateral groundwater flow. Despite ARs contributing more precipitation than non‐AR storms, we find less AR water is preferentially stored in aquifers by year end. Fractionally, ARs result in 300% less snow derived groundwater‐recharged compared to non‐AR precipitation. Rain‐on‐snow (RoS) plays an important role in AR‐driven discharge, where over 50% of total discharge from ARs snow is from RoS events. Finally, despite record‐breaking annual precipitation, simulated groundwater depletion occurs by year end due to estimates of groundwater pumping activities. The results from these simulations serve as a partial analogue of future hydrologic conditions where ARs are expected to intensify and provide a greater fraction of annual precipitation due to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimizing Grid-interactive Efficient Building Designs with Stacked Value Streams

Grid-interactive efficient buildings (GEBs) are those characterized by the combination of energy efficiency and demand flexibility with smart technologies and communications to not only deliver greater affordability and comfort to buildings, but also help utilities manage grid operations and lower system costs. This paper presents an innovative techno-economic assessment framework to effectively examine different GEB design options, explore various use cases, define technically achievable benefits, and thereby assist in informed decision-making. In particular, building load flexibility, thermal storage, and battery energy storage are considered. Advanced optimal dispatch problem is formulated to maximize the stacked value streams from multiple, competing use cases, subject to the physical capabilities and operational flexibility associated with different designs and configurations. Comprehensive case studies were performed for a real-world building to evaluate the cost-effectiveness of different GEB designs and offer in-depth insights. It was found that the proposed assessment method could effectively capture the costs and benefits linked to each GEB design option. Furthermore, the study revealed that outage mitigation and demand response are the two most significant sources of benefits for GEBs.

Ma, Xu↗