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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Adaptive PV Frequency Control Strategy Based on Real-time Inertia Estimation

The declining cost of solar Photovoltaics (PV) generation is driving its worldwide deployment. As conventional generation with large rotating masses is being replaced by renewable energy such as PV, the power system’s inertia will be affected. As a result, the system’s frequency may vary more dramatically in the case of a disturbance, and the frequency nadir may be low enough to trigger protection relays such as under-frequency load shedding. The existing frequency-watt function mandated in power inverters cannot provide grid frequency support in a loss-of-generation event, as PV plants usually do not have power reserves. Here, a novel adaptive PV frequency control strategy is proposed to reserve the minimum power required for grid frequency support. A machine learning model is trained to predict system frequency response under varying system conditions, and an adaptive allocation of PV headroom reserves is made based on the machine learning model as well as real-time system conditions including inertia. Case studies show the proposed control method meets the frequency nadir requirements using minimal power reserves compared to a fixed headroom control approach.

14 SOLAR ENERGY↗
DOE OSTI2023

Effect of fiber reinforcement on the process parameters of injection molding manufacturing process

Use of fillers with the resin is common in injection molding manufacturing technique to enhance the stiffness and strength of the manufactured components. However, the addition of fillers changes the rheological and mechanical properties of the material, required adjustments in the process parameters used for the manufacturing. The present study aims to investigate this impact of fillers on the process parameters of the injection molding manufacturing process. Neat Acrylonitrile Butadiene Styrene (ABS) and ABS reinforced with short glass fibers are used as the materials to perform the comprehensive numerical analysis including complete process sequence, namely filling, packing, cooling and warpage. Parametric studies are conducted to optimize the manufacturing process for minimal warpage (or shrinkage) in the produced part. The selected process parameters for the parametric study include cooling time, packing pressure, and mold temperature, all of which directly impact the quality of the manufactured parts. The optimal values obtained for these parameters are compared to examine the effect fiber reinforcement has on the manufacturing process.

Garg, Nikhil↗
DOE OSTI2020

Influence of Nanoconfinement and Elevated Temperatures on Geocolloid-Facilitated Transport of Energy related Contaminants

Fossil fuels obtained using hydraulic fracturing and nuclear energy are deemed crucial for meeting the current and future energy demands of the United States. The utilization of these resources, especially fossil fuels from hydraulic fracturing, has increased rapidly within the last decade. However, with the increasing levels of hydraulic fracturing activities, the occurrence and fate of chemicals used in these processes in the geosystem, as well as the potential consequences of their interactions with environment and geosystems (e.g. mineral-based geocolloids and geosurfaces), have been increasingly recognized as issues warranting consideration owing to the toxic nature of these chemicals. Scientific results originated from this project has brought better understandings on how such contaminants can preferentially or adversely interact with geosystems and how geocolloids can facilitate their transport by realistically mimicking geological conditions and environments in the laboratory level for the first time. Based on such knowledge, it may eventually lead to the development of mitigation strategies for reducing and minimizing the transport and wide distribution of contaminants and pollutants. This aspect is important for the society to ensure that energy-related contaminants do not find their way to waterbodies that are used as drinking water or irrigation. The project is also anticipated to increase public awareness about potential adverse effects of energy systems on the geosphere and environment and may motivate society to consider better mitigation strategies to prevent such occurrences.

(Nano)Confinement↗
DOE OSTI2021

Mitigation of Gaseous Emissions from Stored Swine Manure with Biochar: Effect of Dose and Reapplication on a Pilot-Scale

Rural communities are affected by gaseous emissions from intensive livestock production. Practical mitigation technologies are needed to minimize emissions from stored manure and improve air quality inside barns. In our previous research, the one-time surficial application of biochar to swine manure significantly reduced emissions of NH3 and phenol. We observed that the mitigation effect decreased with time during the 30-day trials. In this research, we hypothesized that bi-weekly reapplication of biochar could improve the mitigation effect on a wider range of odorous compounds using a larger scale and longer trials. The objective was to evaluate the effectiveness of biochar dose and reapplication on mitigation of targeted gases (NH3, odorous, volatile organic compounds VOCs, odor, greenhouse gases (GHG)) from stored swine manure on a pilot-scale setup over 8-weeks. The bi-weekly reapplication of the lower biochar dose (2 kg/m2) showed much higher significant percentage reductions in emissions for NH3 (33% without and 53% with reapplication) and skatole (42% without and 80% with reapplication), respectively. In addition, the reapplication resulted in the emergence of a statistical significance to the mitigation effect for all other targeted VOCs. Specifically, for indole, the percentage reduction improved from 38% (p = 0.47, without reapplication) to 78% (p = 0.018, with reapplication). For phenol, the percentage reduction improved from 28% (p = 0.71, without reapplication) to 89% (p = 0.005, with reapplication). For p-cresol, the percentage reduction improved from 31% (p = 0.86, without reapplication) to 74% (p = 0.028, with reapplication). For 4-ethyl phenol, the percentage emissions reduction improved from 66% (p = 0.44, without reapplication) to 87% (p = 0.007, with reapplication). The one-time 2 kg/m2 and 4 kg/m2 treatments showed similar effectiveness in mitigating all targeted gases, and no statistical difference was found between the dosages. The one-time treatments showed significant percentage reductions of 33% and 42% and 25% and 48% for NH3 and skatole, respectively. The practical significance is that the higher (one-time) biochar dose may not necessarily result in improved performance over the 8-week manure storage, but the bi-weekly reapplication showed significant improvement in mitigating NH3 and odorous VOCs. The lower dosages and the frequency of reapplication on the larger-scale should be explored to optimize biochar treatment and bring it closer to on-farm trials.

Chen, Baitong (ORCID:0000000266071253)↗
DOE OSTI2022

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ActiveBAS: A Low-cost, Scalable Control Solution for Grid-Interactive Small and Medium Sized Commercial Buildings

This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development of a vadose zone advanced monitoring system: Tools to assess groundwater vulnerability

Performing repeat pore-fluid sampling over long time-scales can provide valuable information on unsaturated zone contaminants and their potential flux to ground water. This information can be used to manage groundwater remedies and identify contaminants that need to be sequestered in the vadose zone to minimize flux to ground water. Pore-water samples are commonly used to obtain contaminant concentrations within the vadose zone, but existing methods are limited as they only provide a single sample at one location and time. The vadose zone advanced monitoring system (VZAMS) has been designed to integrate multiple technologies into a single down-borehole system that allows for sampling of pore fluids (liquid and gas) to provide information about contamination and hydraulic conditions at multiple depths (~0.3-m intervals) within a cased borehole. Testing has been completed at the laboratory scale to verify the sampling elements of VZAMS, including geochemical testing for representative contaminants known to exist at the Hanford Site, located in southeastern Washington State. Physical tests focused on the ability of the sampler to draw fluid under unsaturated conditions. Initial geochemical testing showed that the stainless steel material used with the porous cuff may affect the sampled concentrations of redox-sensitive contaminants under very dry conditions. Additional laboratory testing demonstrated that the VZAMS components are able to collect representative samples for substances of interest under expected field conditions. In this paper, the design and functionality of a novel instrument are demonstrated in support of subsequent testing in the field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Timepix4 analog front-end design: Lessons learnt on fundamental limits to noise and time resolution in highly segmented hybrid pixel detectors

This manuscript describes the optimization of the front-end readout electronics for high granularity hybrid pixel detectors. The theoretical study aims at minimizing the noise and jitter. The model presented here is validated with both circuit post layout simulations and measurements on the Timepix4 Application Specific Integrated Circuit (ASIC). The analog front-end circuit and the procedure to optimize the dimensions of the main transistors are described with detail. The Timepix4 is the most recent ASIC designed in the framework of the Medipix4 Collaboration. It was manufactured in 65nm CMOS process, and consists of a four side buttable matrix of 448 × 512 pixels with 55µm pitch. The analog front-end has a gain of ~36mV/ke - when configured in High Gain Mode, and ~20mV/ke - when configured in Low Gain Mode. The Equivalent Noise Charge (ENC) is ~68e - rms and ~80e - rms in High Gain Mode and in Low Gain Mode respectively. In event driven mode the incoming hits can be time stamped within a ~ 200ps time bin and the chip can deal with a maximum flux of ~ 3.6MHzmm –2 s –1 . In photon counting mode, the chip can deal with up to ~ 5GHzmm –2 s –1 . The routine designed to optimize the Timepix4 front-end is then used to analyze the performance limits in terms of jitter and noise for Charge Sensitive Amplifiers in pixel detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Insights into determining pore size properties of ultrafiltration membranes

The selectivity of porous membranes is often characterized using solute rejection tests, where membranes are challenged with dilute aqueous solutions of neutral solutes at operating conditions that minimize concentration polarization and fouling. In single solute tests, a membrane is challenged with one molecular weight (MW) solute at a time from low to high MW. Since single solute methods are time-intensive, mixed solute tests have become more common, where a mixture of several MW solutes challenges a membrane at once. However, the presence of large solutes in a mixture increases the rejection of smaller solutes. Furthermore, there are no universally accepted operating conditions or standard methods used by membrane manufacturers or researchers for the experiments, leading to difficulty in pore size and pore characteristic comparisons. In this paper, commercial ultrafiltration membranes were challenged with single and mixed solute polyethylene glycol (PEG) and dextran aqueous solutions. First, rejection values determined using total organic carbon (TOC) and high-performance liquid chromatography (HPLC) from single solute filtration experiments are compared. Differences in rejection curves obtained by the two techniques are attributed to solute polydispersity. Mixed solute filtration experiments with binary mixtures of solutes showcased co-solute interactions, which increase with both the size and weight percent of large solute in the mixture. Mixed solute filtration experiments at varying operating conditions (i.e., stir speed and flux) were conducted to determine operating conditions that mitigate co-solute interactions. Stir speed had a minimal effect on co-solute interactions. In contrast, low flux conditions can help minimize co-solute interactions, leading to pore size distributions that closely resemble results observed in single solute filtration using narrowly dispersed solutes. Additionally, at low flux conditions, the predicted membrane pore size distributions utilizing mixed solute experiments with PEG and dextran were similar.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Segregated Approach for Modeling the Electrochemistry in the 3-D Microstructure of Li-Ion Batteries and Its Acceleration Using Block Preconditioners

Abstract Battery performance is strongly correlated with electrode microstructure. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the electrochemical system at the microstructure scale. A fast-iterative solver with an appropriate preconditioner is then required to simulate large representative volume in a reasonable time. In this work, a finite element electrochemical model is developed to resolve the concentration and potential within the electrode active materials and the electrolyte domains at the microstructure scale, with an emphasis on numerical stability and scaling performances. The block Gauss-Seidel (BGS) numerical method is implemented because the system of equations within the electrodes is coupled only through the nonlinear Butler–Volmer equation, which governs the electrochemical reaction at the interface between the domains. The best solution strategy found in this work consists of splitting the system into two blocks—one for the concentration and one for the potential field—and then performing block generalized minimal residual preconditioned with algebraic multigrid, using the FEniCS and the Portable, Extensible Toolkit for Scientific Computation libraries. Significant improvements in terms of time to solution (six times faster) and memory usage (halving) are achieved compared with the MUltifrontal Massively Parallel sparse direct Solver. Additionally, BGS experiences decent strong parallel scaling within the electrode domains. Last, the system of equations is modified to specifically address numerical instability induced by electrolyte depletion, which is particularly valuable for simulating fast-charge scenarios relevant for automotive application.

25 ENERGY STORAGE↗
DOE OSTI2025

Stream bed topography in 2016 at the Lower Montane site in the East River Watershed, Colorado

This dataset contains stream bed topography data from the Lower Montane site in the East River Watershed, Colorado. It is intended to support hydro-biogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). The stream bed was surveyed using a RTK-GPS (Real-Time Kinematic Global Positioning System) and a total station at hundreds of locations during two field campaigns, conducted in October 2015 and October 2016. The two surveys were merged into a single product under the assumption of minimal changes in stream bed topography between the years. A LiDAR (Light Detection and Ranging) derived ground surface elevation product from 2015 (see reference below) was used to interpolate the stream bed topography and estimate water depth at the time of the LiDAR survey. The dataset includes three GeoTIFF products, two *.csv data files, and three *.csv metadata files. Feel free to contact the author with any questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Combining model-based and model-free methods for stochastic control of distributed energy resources

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. Here, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Macro-level mechanical interlocking: A rapid joining approach for additively manufactured compression molded composite panels

Composite joining typically involves multiple steps, such as drilling and surface treatment, as part of the manufacturing process, which leads to low throughput and long cycle times. In the present study, we demonstrated a macro-level mechanical interlocking (MI) based, rapid joining technique to assemble additively manufactured compression molded (AMCM) panels, enabling the production of parts larger than the mold dimensions. Composite panels made of 20 wt% short carbon fiber reinforced acrylonitrile butadiene styrene (CF/ABS) were joined using MI features of various geometries, namely tree (TR), dovetail (Dov), rectangle 2 (Rect2), and rectangle 1 (Rect1), and their in-plane strength was evaluated. The resultant strength of the tested MI joints reached up to 74 % of the baseline tensile strength (i.e., the ‘no joint’ case). Observations from optical and scanning electron microscopy revealed inadequate polymer diffusion between the adherends, indicating that the joint strength was primarily derived from mechanical interlocking. Additionally, the fracture surfaces exhibited stress-whitening marks, which were characterized using differential scanning calorimetry (DSC). The increase in melting enthalpy suggested local stretching of polymer chains due to MI. Finite element analysis (FEA) indicated that the Rect1 MI feature, which generated the lowest stress concentration, outperformed the others in terms of joint strength, achieving 42 MPa. As a demonstration of the MI joining method, a battery box tray measuring 108 cm × 34 cm using a mold with an effective dimension of 36 cm × 34 cm successfully manufactured, resulting in a part with an area three times larger than the mold. In conclusion, this study presents a promising approach to improving composite joining techniques while minimizing production complexities.

In-plane joining↗
DOE OSTI2024

Massively parallel axisymmetric fluid model for streamer discharges

A highly parallelizable fluid plasma simulation tool based upon the first-order drift-diffusion equations is discussed. Atmospheric pressure plasmas have densities and gradients that require small element sizes in order to accurately simulate the plasm resulting in computational meshes on the order of millions to tens of millions of elements for realistic size plasma reactors. To enable simulations of this nature, parallel computing is required and must be optimized for the particular problem. Here, a finite-volume, electrostatic drift-diffusion implementation for low-temperature plasma is discussed. The implementation is built upon the Message Passing Interface (MPI) library in C++ using Object Oriented Programming. The underlying numerical method is outlined in detail and benchmarked against simple streamer formation from other streamer codes. Electron densities, electric field, and propagation speeds are compared with the reference case and show good agreement. Convergence studies are also performed showing a minimal space step of approximately 4 μm required to reduce relative error to below 1% during early streamer simulation times and even finer space steps are required for longer times. Additionally, strong and weak scaling of the implementation are studied and demonstrate the excellent performance behavior of the implementation up to 100 million elements on 1024 processors. Lastly, different advection schemes are compared for the simple streamer problem to analyze the influence of numerical diffusion on the resulting quantities of interest.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Economic viability of using thermal energy storage for flexible carbon capture on natural gas power plants

Fossil fuel-based power plants generate 80% of the electricity in the United States and provide a reliable generation source for both base and peak power demands. These plants are expected to adapt to changes in environmental policies that will require carbon management with carbon capture and storage (CCS) representing a possible solution. Current solvent-based CCS has a detrimental impact on a power plant's performance due to large heat loads required for carbon capture solvent regeneration. This parasitic load restricts the power plant's output and operation flexibility. Therefore, this study evaluates the feasibility of using thermal storage technologies for natural gas combined cycle (NGCC) power plants coupled with CCS to minimize the impact of solvent regeneration and enable the plant to operate at peak power output. Thermal storage can minimize the impact of CCS on the power plant by providing the heat load required for solvent regeneration during times of peak demand which will allow the plant to operate unrestricted and at full power. In total, fifteen unique thermal storage configurations were evaluated from three thermal storage categories: Brayton cycle heat pump, vapor compression heat pump, and heat recovery steam generator steam extraction for storage. The viability of these systems was determined by evaluating each configuration on thousands of real-world Locational Marginal Pricing (LMP) profiles from the New York Independent System Operator and California Independent System Operator electricity markets using a techno-economic analysis. Afterwards, results were compared to the performance of a base power plant (NGCC with CCS and no thermal storage) to determine the impact of thermal storage on power plant economics. Overall, six of the thermal storage configurations performed better than base CCS enabled power plant on between 11.5% and 38.7% of the LMP signals evaluated. The best performing configuration was a vapor compression heat pump that used flue gas as the working fluid and had both hot and cold thermal storage units. This configuration performed better than the base CCS power plant on 38.7% of the LMP profiles. The results of this study show thermal storage can mitigate the economic impact of carbon capture solvent regeneration on NGCC power plants. Discussion focuses on the impact of electricity pricing on the optimal thermal storage system, the advantages and disadvantages of the systems evaluated, and identifies limitations with the study.

25 ENERGY STORAGE↗
DOE OSTI2025

Insights Into Nature-Based Climate Solutions: Managing Forests for Climate Resilience and Carbon Stability

Successful implementation of forest management as a nature-based climate solution is dependent on the durability of management-induced changes in forest carbon storage and sequestration. As forests face unprecedented stability risks in the face of ongoing climate change, much remains unknown regarding how management will impact forest stability, or how interactions with climate might shift the response of forests to management across spatiotemporal scales. Here, we used a process-based model to simulate multidecadal projections of forest dynamics in response to changes in management and climate. Simulations were conducted across gradients in forest type, edaphic factors, and management intensity under two alternate radiative forcing scenarios (RCP4.5 and RCP8.5). This allowed for the quantification of forest stability shifts in response to climate change, and the role of management in modulating that response, where ecosystem stability is characterized as the resilience and temporal stability of net primary production, aboveground biomass, and soil carbon. Our results indicate that forest structure is primarily shaped by management, but the same management strategy often produced divergent structures over time, due to interactions with regional climate change. We found that management can be used to increase stability and minimize the release of stored carbon by reducing mortality, but also highlight the regional dependency of management-induced changes in resilience to climate change.

54 ENVIRONMENTAL SCIENCES↗