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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting fatigue from heart rate signatures using functional logistic regression

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Identification of Life Models for Li-Ion Batteries Using Penalized Regression and Bilevel Optimization

Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control and optimal sizing

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. Finally, this research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Using electronic health record metadata to predict housing instability amongst veterans

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction

The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element- (FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Environmentally Assisted Fatigue in Light Water Reactor Environment

This report summarizes the Environmentally Assisted Fatigue (EAF) research conducted at ANL under the US DOE Light Water Reactor Sustainability (LWRS) program. Starting from a rich background in theoretical and experimental EAF, ANL previously developed an approach to evaluate fatigue performance of reactor materials in light water reactor environments with the correction factor F en . The approach was based on a large body of experimental work performed at ANL and elsewhere, and was consistent with American Society of Mechanical Engineers (ASME)’s methodology governing the design and construction of reactor components. In recent years, the program was focused on component fatigue prediction and made several major and fundamental contributions in this area. These accomplishments help meet the needs identified by the industry concerning component level fatigue predictions in complex, transient conditions. The main contribution of the ANL program involved the development of a system-level model for estimating residual strain and life of nuclear reactor coolant system components under connected-system-thermal-mechanical boundary conditions. The goal was to predict the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the finite element (FE) model results, the strain residuals, strain amplitudes and resulting fatigue lives of reactor coolant system (RCS) components were predicted. The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. In addition, the simulated component-level strain profile can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests. Building upon the system-level model, ANL developed a digital twin (DT) framework to predict the structural states and associated fatigue life of components in real-time. This framework is a comprehensive system designed to predict the structural states and fatigue lives of reactor components. It includes multiple models and integrates artificial intelligence (AI), machine learning (ML), and FE based modeling tools to evaluate the structural states and fatigue lives.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Design principles for intrinsically circular polymers with tunable properties

This perspective discusses a set of design principles for next-generation kinetically trapped, intrinsically circular polymers (iCPs) that are inherently, selectively, and expediently depolymerizable to their monomer state once their kinetic barriers of deconstruction are overcome, thereby enabling not only the ideal shortest chemical circularity but also tunable performance properties. After describing four elements of the design principles - thermodynamics and kinetics, strategies to overcome trade-offs and unify conflicting properties, predictive modeling, and supply-chain life-cycle assessment and techno-economic analysis, which are illustrated with state-of-the-art examples - it concludes with presenting key challenges and opportunities for sustainable development of iCPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Revealing causes of macroscale heterogeneity in lithium ion pouch cells via synchrotron X-ray diffraction

Heterogeneous battery performance is a critical issue for maximization of cell lifetime capacity and safety. Using high energy synchrotron X-ray diffraction, the influence of charge rate, voltage limit, uneven stack pressure, and gas generation on the lithium transport properties was quantified in single-layer graphite/LiNi 0.5 Mn 0.3 Co 0.2 O 2 pouch cells. A freshly formatted cell tracked in operando during initial fast charge cycles indicated variable position-dependent performances, while lateral mapping showed a significant fast charge (6C) heterogeneity compared to slow charge (C/2). Pressure effects were non-dominant compared to charge rate. Maps of previously aged and rested cells indicate that lateral heterogeneity slowly equilibrates at rest, but regenerates upon further cycling at fast charge rate. Furthermore, an unformatted cell was mapped at charge and discharge during its first formation cycle to analyze the effect of byproduct gases on the heterogeneous lithium transport. Gas was observed as randomly interspersed “bubbles” which locally hindered lithium intercalation and caused significant heterogeneity. Electrode architectures and charging protocols that promote homogeneous intercalation are critical for predictable high-performance and long-life batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Design of a Compact Dry Cooler With an Aluminum Heat Exchanger Core for a Supercritical CO2 Power Cycle Is Evaluated for a Concentrating Solar Power Application

Abstract As the supercritical CO2 power cycle develops and the component technologies mature, there is still a need to reduce the associated capital and operating costs to maintain a competitive levelized cost of electricity (LCOE) in order to enter the market. When considering concentrating solar power (CSP) coupled with an sCO2 power block and sensible thermal storage, the technology presents a clean source for utility-scale power generation to support baseload or peak-load electrical demand. However, the LCOE of the technology is still considered higher than the competing technologies and should be reduced to better compete in the market; 2030 targets for dispatchable solar plants are 5¢/kWh for baseload CSP and 10¢/kWh for peaker plants, as set by the United States Department of Energy. In response to this need, this study is targeting improvements in the power cycle precooler to reduce power block contribution to LCOE. This study considers a dry cooler, as CSP plants are sensitive to water consumption because many installations are slated for remote or arid locations where solar irradiance is very high, but water is scarce. Furthermore, the power block footprint for an sCO2 system is quite compact, especially as compared to a steam cycle. Therefore, there is interest in installing a more compact dry cooler that is proportional to the reduced footprint sCO2 power block, while conventional dry coolers are an order of magnitude larger. The competing goals of size, performance, and cost were considered in this study to develop a compact dry cooler that can easily be packaged with the power block, significantly reducing the installation and transport cost compared to the current state of the art, while maintaining or improving upon the heat transfer performance and impact on plant LCOE. This paper details the high-level findings of a large dry cooler sensitivity study for design point selection, design of the compact dry cooler, expected year-round performance for the dry cooler and the power cycle, and the predicted LCOE for a 30-year plant life. It was found that an aluminum heat exchanger core can be suitably designed to meet the pressure and temperature requirements for a precooler in an sCO2 recompression Brayton cycle. The dry cooler assembly was found to have improved heat transfer performance, allowing for increased cycle efficiencies and a reduced plant LCOE. When coupled with a centrifugal blower and compact transition duct, the dry cooler assembly was able to reduce the installation footprint by over 50%.

14 SOLAR ENERGY↗
DOE OSTI2022

Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based

Abstract Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Meta-Analysis of Stressors from the Total Environment Associated with Children’s General Cognitive Ability

General cognitive ability, often referred to as ‘general intelligence’, comprises a variety of correlated abilities. Childhood general cognitive ability is a well-studied area of research and can be used to predict social outcomes and perceived success. Early life stage (e.g., prenatal, postnatal, toddler) exposures to stressors (i.e., chemical and non-chemical stressors from the total (built, natural, social) environment) can impact the development of childhood cognitive ability. Building from our systematic scoping review (Ruiz et al., 2016), we conducted a meta-analysis to evaluate more than 100 stressors related to cognitive development. Our meta-analysis identified 23 stressors with a significant increase in their likelihood to influence childhood cognitive ability by 10% or more, and 80 stressors were observed to have a statistically significant effect on cognitive ability. Stressors most impactful to cognition during the prenatal period were related to maternal health and the mother’s ability to access information relevant to a healthy pregnancy (e.g., diet, lifestyle). Stressors most impactful to cognition during the early childhood period were dietary nutrients (infancy), quality of social interaction (toddler), and exposure to toxic substances (throughout early childhood). In conducting this analysis, we examined the relative impact of real-world exposures on cognitive development to attempt to understand the inter-relationships between exposures to both chemical and non-chemical stressors and early developmental life stages. Our findings suggest that the stressors observed to be the most influential to childhood cognitive ability are not permanent and can be broadly categorized as activities/behaviors which can be modified to improve childhood cognition. This meta-analysis supports the idea that there are complex relationships between a child’s total environment and early cognitive development.

Nilsen, Frances M. (ORCID:0000000241936951)↗
DOE OSTI2022

Characterization and Accelerated Life Testing of a New Solid Oxide Electrolysis Cell

The project aims to develop mechanistic degradation models that realistically predict long-term Solid Oxide Electrolysis Cell (SOEC) durability, using input data from accelerated electrochemical life testing combined with quantitative microstructural and microchemical evaluation. A promising SOEC type will be further developed. The understanding achieved by combining experimental results and theory will be used to guide improvements in long-term SOEC durability, and validate that this technology can reach DOE H 2 production cost and durability targets. Planned outcomes include: (1) new experimental accelerated testing data on SOEC degradation at the oxygen electrode, fuel electrode, and electrolyte; (2) predictive theory of degradation developed based on the accelerated test data; (3) new electrode materials and processing methods that minimize degradation rate; (4) determination of the impact of SOEC electrolyte thickness and electrode composition/microstructure on performance and degradation; (5) determination of the operating conditions where degradation is minimized; and (6) validation of degradation models for extrapolating accelerated test data to predict long-term durability.

08 HYDROGEN↗
DOE OSTI2023

Stability study of aqueous foams under high-temperature and high-pressure conditions relevant to Enhanced Geothermal Systems (EGS)

In Enhanced Geothermal System (EGS) hydraulic fracturing is carried out by injecting cold water into deep Hot Dry Rocks (HDR) under carefully controlled conditions to create new or reopen existing fractures. Water-based fracturing fluids demonstrate some challenges including immense quantity of water usage, water sensitivity of the formations, water blocking, and lack of proppant carrying capacity and transportation. Thus, an alternative is to use foam-based fracturing fluid which offers potential advantage over conventional water-based fracturing fluid such as minimum water usage, reduced wellbore damage, high proppant carrying capacity, and less environmental damage. However, foams are complex mixture of gaseous phase and liquid phase which are thermodynamically unstable at downhole conditions, and their stability can decrease over time due to liquid drainage, bubble coarsening, and coalescence. This paper shows laboratory experiments executed to study foam stability at high temperature (200°C) and high pressure (6.9MPa) conditions which simulates the geothermal environment. Foam stability was characterized by half-life of foam, which is defined as the time taken by the foam to decreases by 50% of its original height due to drainage. In this paper, two types of gaseous phases, nitrogen (N 2 ) and carbon dioxide (CO 2 ) were investigated. Also, based on successful practice of foam-based fracturing fluid in oil and gas industries, four surfactants, including Alpha olefin sulfonate (AOS), Sodium dodecyl sulfonate (SDS), Tergitol™ (NP – 40), and Cetyltrimethylammonium chloride (CTAC) at optimum concentration of 1wt.% were tested for best stability performance. In addition, different stabilizing agents including guar gum, bentonite clay, crosslinker, silicon dioxide nanoparticles (SiO 2 ), graphene oxide (GO) were also studied. Experimental results showed that N 2 foams were more stable than CO 2 foams. It was observed that foam half-life decreased with the increase in temperature. Among all the surfactants, AOS foams showed the most promising thermal stability at high temperatures. Moreover, with the addition of stabilizing agents, foam's half-life was enhanced. Stabilizing agents such as crosslinker and GO dispersion showed the most stable foams with half-life recorded at 20min and 17min, respectively, at 200°C and 6.9MPa. Finally, pressure also showed a positive effect on foam stability; with increased pressure, foam half-life was increased. Based on the experimental data, analytical models for the effect of temperature and pressure were developed, considering foam degradation is a first-order kinetic reaction that linearly depends on the foam drainage mechanism. The effect of temperature on foam half-life was studied as an exponential decay model. In this model, foam half-life is a function of drainage rate constant (D A ) and activation energy (E a ) of the foam system. The effect of pressure on foam half-life was found to obey a power-law model where an increase in pressure showed an increase in foam half-life. Furthermore, a linear relation was studied for the effect of pressure on foam activation energy and drainage rate. Then the combined effects of temperature and pressure were studied, which yielded an analytical model to predict the foam stabilities in terms of half-life for different foam compositions. In conclusion, this research indicates that with an appropriate selection of surfactants and stabilizing agents, it is possible to obtain stable foams, which could replace conventional water fracturing fluid under EGS conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Limited surface impacts of the January 2021 sudden stratospheric warming

Subseasonal weather prediction can reduce economic disruption and loss of life, especially during “windows of opportunity” when noteworthy events in the Earth system are followed by characteristic weather patterns. Sudden stratospheric warmings (SSWs), breakdowns of the winter stratospheric polar vortex, are one such event. They often precede warm temperatures in Northern Canada and cold, stormy weather throughout Europe and the United States - including the most recent SSW on January 5th, 2021. Here we assess the drivers of surface weather in the weeks following the SSW through initial condition “scrambling” experiments using the real-time CESM2(WACCM6) Earth system prediction framework. We find that the SSW itself had a limited impact, and that stratospheric polar vortex stretching and wave reflection had no discernible contribution to the record cold in North America in February. Instead, the tropospheric circulation and bidirectional coupling between the troposphere and stratosphere were dominant contributors to variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A dendritic strontium river isoscape for fisheries applications in the Sacramento River basin, California, USA

Objective Understanding the origins and movements of fish is fundamental to effective conservation and fisheries management. Strontium isotope ratios ( 87 Sr/ 86 Sr) in otoliths provide a powerful tracer of natal origin and migratory pathways. However, existing 87 Sr/ 86 Sr isoscapes for the Sacramento River basin, an ecosystem that supports ecologically and economically important salmon populations, rely on discrete classification approaches that overlook unsampled habitats and do not incorporate spatial uncertainty. Our objective was to develop a continuous, network-explicit 87 Sr/ 86 Sr isoscape with quantified uncertainty to fill in data gaps and enable probabilistic assignments of fish origin and movement. Methods We used river water 87 Sr/ 86 Sr data from 106 sites (1997–2021) to develop spatial stream network models that use dendritic connectivity and watershed characteristics (lithology, bedrock age, and land cover) to predict river water 87 Sr/ 86 Sr throughout the basin. Models were fitted using maximum and restricted likelihood and were evaluated via Akaike’s information criterion and leave-one-out cross validation. We produced both historical (pre-dam) and present-day (below-dam) isoscapes, delineated uncertainty-informed isotopic ranges using k -means clustering, and applied a proof-of-concept Bayesian assignment to estimate natal origins and early rearing habitats for two endangered winter-run Chinook Salmon Oncorhynchus tshawytscha. Results Cross validation indicated strong performance of the 87 Sr/ 86 Sr model (leave-one-out cross validation: R 2 = 0.91; root mean square error = 0.0005). Uncertainty-informed clustering identified 19 isotopic “suites” (reaches with indistinguishable 87 Sr/ 86 Sr values) in present-day anadromous habitats and 25 suites in the historical network. Example natal and early rearing assignments included predictions that challenged expectations for juvenile salmon migration based on predicted river 87 Sr/ 86 Sr compositions. Conclusions This study developed a continuous, network-explicit 87 Sr/ 86 Sr isoscape that integrates existing river data to predict 87 Sr/ 86 Sr in unsampled reaches and the likely achievable range and resolution of otolith-based origin and life history inference. The resulting river isoscape provides a valuable tool to predict salmon movements and identify habitats supporting their survival and growth that otherwise might remain undetected. Coupling these predictions with complementary approaches that ground-truth juvenile presence (e.g., targeted fish surveys) represents an important step toward science-informed restoration and management of critical habitats throughout the Sacramento River basin.

Environmental sciences↗
DOE OSTI2021

Exposure to ionizing radiation disrupts normal epigenetic aging in Japanese medaka

Alterations to the epigenome are a hallmark of biological aging and age-dependent patterning of the DNA methylome (“epigenetic aging”) can be modeled to produce epigenetic age predictors. Rates of epigenetic aging vary amongst individuals and correlate to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not empirically resolved. Here, we investigate the relationship between aging, DNA methylation, and environmental exposures in Japanese medaka (Oryzias latipes). We find age-associated DNA methylation patterning enriched in genomic regions of low CpG density and that, similar to mammals, most age-related changes occur during early life. We construct an epigenetic clock capable of predicting chronological age with a mean error of 61.1 days (~8.4% of average lifespan). To test the role of environmental factors in driving epigenetic age variation, we exposed medaka to chronic, environmentally relevant doses of ionizing radiation. Because most organisms share an evolutionary history with ionizing radiation, we hypothesized that exposure would reveal fundamental insights into environment-by-epigenetic aging interactions. Radiation exposure disrupted epigenetic aging by accelerating and decelerating normal age-associated patterning and was most pronounced in cytosines that were moderately associated with age. These findings empirically demonstrate the role of DNA methylation in integrating environmental factors into aging trajectories.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Seasonal mixing from intermittent flow drives concentration-discharge behaviour in a stream affected by coal mine drainage

Abandoned mining operations continue to severely degrade many ecosystems worldwide by releasing acidic water and/or heavy metals into surface and groundwater. Contaminant concentrations in affected streams vary with discharge in patterns that reflect both geochemical reactions and variable mixing of contaminated and non-contaminated waters. However, controls on concentration-discharge (C-Q) patterns remain unclear, particularly for constituents that experience changing solubility across redox and pH gradients. Understanding the C-Q behaviour of contaminants aids in predicting both downstream transport and effects on aquatic life under variable flow. Here, we examined the C-Q behaviours of non-reactive (Na, K, Ca, Mg, Cl - ) and reactive (Fe, Mn, Al, H + , SO 4 2- ) solutes in a stream contaminated with acid mine drainage in northeastern Ohio, USA. Concentration-discharge patterns at the watershed outlet primarily reflected mixing of contaminated baseflow with intermittent inputs of high pH water draining from a passive limestone treatment system into the stream. The treatment system acted as an ephemeral tributary that mitigated contamination in the stream by diluting solutes, raising pH, and driving metal precipitation, but only when flow was present during wet seasons. Consequently, AMD-derived reactive solutes (H + , Fe, Mn, Al) decreased with increasing stream discharge while relatively conservative solutes (e.g., Ca, Mg, K, Na) decreased only slightly or were chemostatic. This study highlights both the unique C-Q patterns of reactive solutes when compared to those of non-reactive solutes and the potential for intermittent streams to control C-Q behaviour in headwater catchments.

58 GEOSCIENCES↗
DOE OSTI2021

Resilience and economics of microgrids with PV, battery storage, and networked diesel generators

Current designs and assessments of microgrids have ignored component reliability, leading to significant errors in predicting a microgrid’s performance while islanded. Existing life cycle cost studies on hybrid microgrids—which combine photovoltaics (PV), battery storage and networked emergency diesel generators—also have not identified all the potential economic opportunities. Reducing the number of emergency diesel generators through reliance on PV and battery, retail bill savings, and demand response and wholesale market revenue streams are all important. This paper provides a new statistical methodology that calculates the impact of distributed energy reliability and variability on a microgrid’s performance and a novel use of the optimization platform REopt to explore multiple cost savings and revenue streams. We examine the impacts for microgrids in California, Maryland, and New Mexico and show that a hybrid microgrid is a more resilient and cost-effective solution than a diesel-only system. Under realistic conditions, a hybrid microgrid can provide higher system reliability when islanded and have a lower life cycle cost under multiple market conditions than a traditional diesel generator-based system. The improved performance of the hybrid system is resilient to conditions experienced over the last 20 years in solar irradiance and sees little degradation in performance immediately after a hurricane. The cost savings to provide this more resilient backup power system as compared to a diesel-only microgrid are significant. The net present cost for a hybrid microgrid is 19% lower in New Mexico and 35% lower in Maryland than a diesel-only microgrid. In California, the net present cost of the hybrid microgrid is negative because, unlike a diesel-only microgrid, a hybrid microgrid has lower life cycle costs than the power costs without a microgrid.

25 ENERGY STORAGE↗