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DOE OSTI2022

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. Here, the suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

The significant impact of aerosol vertical structure on lower atmosphere stability and its critical role in aerosol–planetary boundary layer (PBL) interactions

The aerosol–planetary boundary layer (PBL) interaction was proposed as an important mechanism to stabilize the atmosphere and exacerbate surface air pollution. Despite the tremendous progress made in understanding this process, its magnitude and significance still have large uncertainties and vary largely with aerosol distribution and meteorological conditions. In this study, we focus on the role of aerosol vertical distribution in thermodynamic stability and PBL development by jointly using micropulse lidar, sun photometer, and radiosonde measurements taken in Beijing. Despite the complexity of aerosol vertical distributions, cloud-free aerosol structures can be largely classified into three types: well-mixed, decreasing with height, and inverse structures. The aerosol–PBL relationship and diurnal cycles of the PBL height and PM 2.5 associated with these different aerosol vertical structures show distinct characteristics. The vertical distribution of aerosol radiative forcing differs drastically among the three types, with strong heating in the lower, middle, and upper PBL, respectively. Such a discrepancy in the heating rate affects the atmospheric buoyancy and stability differently in the three distinct aerosol structures. Absorbing aerosols have a weaker effect of stabilizing the lower atmosphere under the decreasing structure than under the inverse structure. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and can be potentially neutralized by the decreasing structure. Moreover, aerosols can both enhance and suppress PBL stability, leading to both positive and negative feedback loops. This study attempts to improve our understanding of the aerosol–PBL interaction, showing the importance of the observational constraint of aerosol vertical distribution for simulating this interaction and consequent feedbacks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

On the contribution of Pair Distribution Function (PDF) to the characterization of nanocrystalline MOFs: The case of M-MOF-74

Nanoporous Metal-Organic Frameworks (MOFs) have a great potential in so many industrial applications including heterogeneous catalysis and gas storage/capture/separation. MOF-74 is one of the most interesting MOFs in an academic context, as it contains open metal sites, possesses high versatility in metal nature, and can be prepared with so small crystal size. As a consequence, the catalytic activity of these nanocrystalline materials is superior to that given by their conventional counterparts. However, their characterization by conventional X-ray diffraction (XRD) is insufficient. Here, we present a joint study of XRD and Pair Distribution Function (PDF) -both acquired under synchrotron radiation- of a series of nanocrystalline M-MOF-74 materials (M = Mg, Co, Ni, Cu or Zn) prepared at room temperature. Whereas the XRD continues being lacking for an adequate structural characterization, PDF provides key structural information at medium and short range and, therefore, is ideal for studying such nano-domains. It was confirmed that these materials are exclusively composed by MOF-74 phase and that their poor ability to diffract is due solely to their eminent nanocrystallinity, which resulted to be even more pronounced than it has been previously estimated. Beyond Scherrer-like approaches, PDF can give direct information about the range of crystal size in nanocrystalline materials. Moreover, the technique is also able to detect slight structural deviations in short-range environments, just like it is made clear in the case of Cu-MOF-74 compared with the rest of M-MOF-74 materials, which possesses a marked Jahn-Teller effect in the octahedral coordination of Cu.

36 MATERIALS SCIENCE↗

DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗

DOE OSTI2023

Cost-Effectiveness of Local Distribution Tied Solar within KyMEA

KyMEA is a Kentucky joint action agency which provides electric transmission and energy services to eleven municipal utilities. One of KyMEA's member cities, Frankfort, has set a goal to reach 100% renewable generation for the city load by 2023 and 100% carbon free city operations and 100% renewable generation community-wide by 2030. The aim of this research was to determine if the Frankfort city government could reduce its power costs by implementing distribution tied solar while also avoiding any cross subsidization between other KyMEA member utilities or other Frankfort Plant Board customers. The project simulated the KyMEA generation portfolio and transmission system from KyMEA's cost perspective using the NREL Engage capacity expansion model. Running in operate mode, the authors used Engage to compare KyMEA's costs to operate the baseline system to its costs to operate the system with Frankfort meeting some of its load with distribution-tied PV. The cost perspective of Frankfort Plant Board, meanwhile, was simulated using Python. The simulations indicated that it would be cost-beneficial to KyMEA, i.e., that no additional costs would be borne by other members, and that it would be cost-neutral to Frankfort Plant Board for Frankfort city government to implement distribution tied utility-scale PV. The new load attributes resulting from the distribution-tied PV lowered KyMEA's cost to serve load by improving system load factor, reducing system transmission and energy costs, and increasing revenues from market energy sales.

14 SOLAR ENERGY↗

DOE OSTI2022

Residuals-based distributionally robust optimization with covariate information

We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained using Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets within our DRO formulations, and explore cross-validation approaches for sizing these ambiguity sets. Through numerical experiments, we validate our theoretical results, study the effectiveness of our approaches for sizing ambiguity sets, and illustrate the benefits of our DRO formulations in the limited data regime even when the prediction model is misspecified.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Residuals-based distributionally robust optimization with covariate information

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Integration of seismic-pressure-petrophysics inversion of continuous active-seismic monitoring data for monitoring and quantifying CO 2 plume (Final Report)

The overall objective of this project is to develop and validate an integrated package of joint seismic-pressure-petrophysics inversion (jSPPI) of continuous active-source seismic monitoring dataset capable of providing real-time monitoring of CO 2 plume during geologic carbon sequestration (GCS). The three specific developments include: (a) the methodologies for fast seismic full waveform inversion of continuous active source seismic monitoring, (CASSM) datasets for simultaneously estimating velocity and attenuation, and with data assimilation; (b) joint Bayesian petrophysical inversion of seismic models and pressure data for providing and updating CO 2 saturation models; (c) the methods using multiple datasets including (Crainfield and Frio-II borehole) synthetic, laboratory, and field CASSM datasets. The outcomes of jSPPI include (a) a workflow for processing CASSM data, (b) Bayesian inversion algorithms using CASSM data and pressure response data, and (c) integration with data assimilation algorithms for continuously updating site-specific models used for prediction and reservoir management. The validation of joint FWI will be conducted using synthetic models based on the Cranfield and Frio experiments as well as field CASSM datasets collected as part of the Frio-II pilot injection. To quantify and map the mass and distribution of CO 2 (saturation), we will jointly invert velocity and attenuation measurements from the FWI with a Bayesian approach using a rock physics model for attenuation (e.g., White’s attenuation model with two selected patch sizes (White, 1976; Dutta and Seriff, 1979)). The Bayesian inversion will be applied to each time step in the CASSM survey in an updating scheme, which integrates with an ensemble of reservoir simulations at each step. A more complete experimental validation dataset will be collected as part of a mesoscale (2-3 m) gas-CO 2 injection experiment utilizing a higher frequency version of the CASSM system developed for laboratory studies; the integrated inversion will be demonstrated using this dataset which will provide both a dense geometry as well as more precise secondary confirmation measurements (e.g. saturation) typically not available in the field. The resulting real-time map of CO 2 saturation is able to provide a deeper scientific understanding of the complex, time-varying dynamics of subsurface fluid flow migration path as well as the rapid detection of CO 2 leakage hazards.

25 ENERGY STORAGE↗

DOE OSTI2023

The Global DAS Month of February 2023

During February 2023, a total of 32 individual distributed acoustic sensing (DAS) systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the U.S. Geological Survey and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, and dark fiber). Monitored cable lengths vary between 152 and 120,129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and are available for free download. Finally, organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.

58 GEOSCIENCES↗

DOE OSTI2026

Joint X-Ray, Kinetic Sunyaev–Zeldovich, and Weak Lensing Measurements: Toward a Consensus Picture of Efficient Gas Expulsion from Groups and Clusters

There is no consensus on how baryon feedback shapes the underlying matter distribution from either simulations or observations. We confront the uncertain landscape by jointly analyzing new measurements of the gas distribution around groups and clusters—DESI+ACT kinetic Sunyaev–Zel’dovich (kSZ) effect profiles and eROSITA X-ray gas masses—with mean halo masses characterized by galaxy–galaxy lensing. Across a wide range of halo masses ( M 500 = 10 13−14 M ⊙ ) and redshifts (0 < z < 1), we find evidence of more efficient gas expulsion beyond several R 500 than predicted by most state-of-the-art simulations. A like-with-like comparison reveals all kSZ and X-ray observations are inconsistent with the fiducial 1 Gpc 3 hydrodynamical FLAMINGO simulation, which was calibrated to reproduce pre-eROSITA X-ray gas fractions: eROSITA X-ray gas fractions are 2 × lower than the simulation, and the kSZ measurements are combined >8σ discrepant. The FLAMINGO simulation variant with the most gas expulsion, and therefore the most suppression of the matter power spectrum relative to a dark-matter-only simulation, provides a good description of how much gas is expelled and how far it extends; the enhanced gas depletion is achieved by more powerful but less frequent AGN outbursts. Joint kSZ, X-ray, and lensing measurements form a consistent picture of gas expulsion beyond several R 500 , implying a more suppressed matter power spectrum than predicted by most recent simulations. Complementary observables (e.g., thermal Sunyaev–Zel’dovich effect and fast radio bursts) and next-generation simulations are critical to understanding the physical mechanism behind this extreme gas expulsion and mapping its impact on the large-scale matter distribution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

LBNL CRADA (FP00009949) with the American Public Power Association: Electricity Reliability Metrics, Analysis, and Planning (Final Technical Report)

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Cosmological parameters from the joint analysis of density split and second order statistics: An emulator based on the halo occupation distribution

Not Available

Burger, Pierre A. (ORCID:0000000186376305)↗

DOE OSTI2023

Microstructure and texture evolution of friction stir lap welded dissimilar multi-aluminum stack

Here this work investigates the microstructural evolution of a dissimilar multiple-aluminum alloy stack (AA7055, AA7055, AA6022) produced by friction stir lap welding (FSLW). A through-thickness analyses of grain orientation map, grain size and distribution, strain distribution, and texture evolution by electron backscattered diffraction-based characterization technique were performed. Two different welding parameters (combination of tool rotation and traverse speed) were used to study the effects of the welding parameters on these microstructural characteristics. The study shows that higher welding speed and higher rotation speed of the FSLW tool yield finer grains. FSLW resulted in shear texture formation in the weld regions due to high welding speeds. This texture development in the nugget zone is consistent with shear deformation, and (111) pole figures show ideal shear texture components. The Stir Zone (SZ) exhibits shear texture (A/A ̅ and B/B ̅ components), consistent with other shear-assisted processes. Recrystallization mechanisms observed are CDRX in SZ and DDRX in TMAZ (Thermo-Mechanically Affected Zone). Interface analysis reveals the presence of oxide layers between different aluminum sheets, affecting joint mechanical performance. The mechanical property of the lap welded joints was presented in terms of microhardness distribution across the weld cross section. Slightly higher hardness in higher speed welding setup was observed throughout the SZ and AA6022 layer, indicating greater grain refinement and uniform grain structure distribution.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Electricity Reliability Metrics, Analysis, and Planning (CRADA Final Report)

LBNL and APPA (the team) jointly examined the extent to which differences in distribution feeder characteristics are correlated with differences in their reliability performance when exposed to three different types of natural hazards (wildlife, weather, and vegetation). The team employed data-driven approaches to quantify the relationships between various measures of feeder reliability and a suite of feeder characteristics individually and jointly via a statistically-based clustering method. The team developed suggestions on how comparisons across groupings of feeders and review of the relative contributions of the constituents of SAIFI and SAIDI could be used to help prioritize utility actions to improve reliability. However, they also caution that their suggestions require further evaluation because they are based on only one year of information from a modest number of small utilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency

A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is maximizing the supply of electricity, water, and gas to critical customers after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. Additionally, the characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to non convex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on an electricity-water-gas integrated system, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Private Tabular Survey Data Products through Synthetic Microdata Generation

We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights ∈[0,1] based on their identification disclosure risks to producing tabular products for survey data. Our method applied to an observed survey database achieves an asymptotic global probabilistic differential privacy guarantee. Our two approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a privacy guarantee. The privacy-protected outcome and survey weights are used to construct tabular cell estimates (where the cell inclusion indicators are treated as known and public) and associated standard errors to correct for survey sampling bias. Through a real data application to the Survey of Doctorate Recipients public use file and simulation studies motivated by the application, we demonstrate that our two microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive noise approach of the Laplace Mechanism. Moreover, our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.

Mathematical Methods In Social Sciences↗

DOE OSTI2020

PhycoCosm, a comparative algal genomics resource

Abstract Algae are a diverse, polyphyletic group of photosynthetic eukaryotes spanning nearly all eukaryotic lineages of life and collectively responsible for ∼50% of photosynthesis on Earth. Sequenced algal genomes, critical to understanding their complex biology, are growing in number and require efficient tools for analysis. PhycoCosm (https://phycocosm.jgi.doe.gov) is an algal multi-omics portal, developed by the US Department of Energy Joint Genome Institute to support analysis and distribution of algal genome sequences and other ‘omics’ data. PhycoCosm provides integration of genome sequence and annotation for >100 algal genomes with available multi-omics data and interactive web-based tools to enable algal research in bioenergy and the environment, encouraging community engagement and data exchange, and fostering new sequencing projects that will further these research goals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Effect of Electroacupuncture at Wushu Acupoints of the Cardiopulmonary Meridian on the Autophagy in Rats with Acute Myocardial Ischemia

Wushu acupoints are the five acupoints distributed below the human elbow and knee joint. They are all located on the same meridian and divided into five categories: Jing, Ying, Shu, Jing, and He. It has been shown that electroacupuncture (EA) at Shenmen point of heart meridian can improve acute myocardial ischemia (AMI) early. However, it is still unclear if all the Wushu acupoints of the heart meridian can improve AMI. Hence, this study emphasizes Wushu acupoints of heart meridian, compares them with Wushu acupoints of lung meridian, and studies the therapeutic effect of EA at Wushu acupoints on AMI and its possible mechanism. It also discusses the specificity of the heart meridian to heart disease. The AMI model is established by ligation of the left anterior descending coronary artery. The detection methods like the physiological recorder, TTC staining, ELISA, and so forth were used to determine the ECG, myocardial infarct size, serum myocardial enzymes, and myocardial tissue-related protein expression in rats. The heart rate (HR) and ST segment along with creatine kinase (CK), creatine kinase isoenzymes (CK-MB), lactate dehydrogenase (LDH), and myocardial infarctions increased after the induction with AMI. Furthermore, the expressions of PINK1 and Parkin protein also showed an increase. However, EA at Wushu acupoints in the heart meridian can reverse the above changes, whereas EA at the lung meridian exhibits limited effect. It is depicted that the heart meridian has a relatively specific relationship with the heart in a diseased state.

Zhu, Chao↗