Pacific Northwest National Laboratory

Maintaining stability in an islanded microgrid is an essential task, especially with the increasing penetration of inverter-based resources (IBRs) due to the advancement in power electronics. Unlike conventional synchronous generators (SGs), IBRs have low inertia, posing significant stability challenges. To address the stability issues, grid-forming inverters (GFMs) and virtual synchronous generators (VSGs) are proposed to enhance damping and emulate the dynamics of synchronous machines. However, the distinct behaviors of IBRs and SGs can lead to instability in hybrid-DER microgrids without proper coordination between IBRs and SGs. A feedback control system for both IBRs and SGs can be deployed to stabilize the hybrid-DER microgrids. For effective operation, several technical issues can be considered in the feedback control loop. Partial state feedback control, relying only on measurable states without the need for an observer, is used instead of full state feedback. Decentralized state feedback uses local states as inputs without the requirement of a communication system.

Promptly perceiving distribution system states is challenged by frequent topology changes and uncertain power injections. To address these issues, a Meta-learning enhanced physics-informed graph attention convolutional network (Meta-PIGACN) model is proposed to handle topological variability in distribution system state estimation (DSSE). Specifically, physics information is integrated into the graph convolutional network, enabling a physics-informed edge-weighting process that incorporates physical information to control the aggregation of neighboring nodes. Besides, the graph attention mechanism automatically adjusts the importance of different neighboring nodes, allowing the capture and preservation of inherent system features across varying topologies, thereby improving state estimation accuracy. Furthermore, meta-learning is proposed to acquire empirical knowledge across multiple topologies so that the model can rapidly adapt to new configurations through iterative gradient descent updates even in large-scale systems. The simulation results based on the 33/118/1746-node distribution systems show the high accuracy and efficiency of the proposed model.

The efficiency of marine cloud brightening in cooling Earth's surface temperature is investigated using an ensemble of simulations with the Community Earth System Model version 2 (CESM2). We employ a susceptibility‐based cloud seeding strategy, previously developed under the Community Climate System Model version 3 (CCSM3) to counteract the warming of CO2 doubling, in which we target the regions of the ocean most easily brightened, to determine what area extent will be required to induce 1°C cooling under SSP2‐4.5. The results indicate that cloud seeding over 5% of the ocean area is capable of achieving this goal in CESM2. Under this seeding scheme, cloud seeding is mainly deployed over lower latitudes which leads to a La Niña‐like pattern of response which is a major unintended consequence. Potential mechanisms behind such side effects are presented and discussed. The simulations also reveal that the 5% cloud seeding scheme induces an overall reduction in global precipitation, with an increase over land and a decrease over the ocean.

Maternal obesity puts the offspring at high risk of developing obesity and cardio-metabolic diseases in adulthood. Here, we utilized a mouse model of maternal high-fat diet (HFD)-induced obesity that recapitulates metabolic perturbations seen in humans. We show increased adiposity in the offspring of HFD-fed mothers (Off-HFD) when compared to the offspring regular diet-fed mothers (Off-RD). We have previously reported significant immune perturbations in the bone marrow of newly-weaned Off-HFD. Here, we hypothesized that lipid metabolism is altered in the bone marrow of Off-HFD vs. Off-RD. To test this hypothesis, we investigated the lipidomic profile of bone marrow cells collected from three-week-old Off-RD and Off-HFD. Diacylgycerols (DAGs), triacylglycerols (TAGs), sphingolipids and phospholipids were remarkably different between the groups, independent of fetal sex. Levels of cholesteryl esters were significantly decreased in Off-HFD, suggesting reduced delivery of cholesterol. These were accompanied by age-dependent progression of mitochondrial dysfunction in bone marrow cells. We subsequently isolated CD11b+ myeloid cells from three-week-old mice and conducted metabolomics, lipidomics, and transcriptomics analyses. The lipidomic profiles of myeloid cells were similar to bone marrow cells and included increases in DAGs and decreased TAGs. Transcriptomics revealed altered expression of genes related to immune pathways including macrophage alternative activation, B-cell receptors, and TGFβ signaling. All told, this study revealed lipidomic, metabolomic, and gene expression abnormalities in bone marrow cells broadly, and in bone marrow myeloid cells particularly, in the newly-weaned offspring of mothers with obesity, which might at least partially explain the progression of metabolic and cardiovascular diseases in their adulthood.

In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy.

The power sector is currently undergoing significant changes, driven by a combination of factors, including decarbonization and technology innovation. This study aims to assess implications of these drivers on U.S. power sector technology futures and the associated water and environmental implications for cooling thermoelectric power plants. Specifically, we evaluate four decarbonization scenarios for the contiguous United States that vary in assumptions concerning demand growth and technology costs, with technology costs driving alternative outcomes that prioritize either technologies that require low amounts of water (such as wind, solar, and battery) or high amounts of water (such as nuclear and carbon capture and storage). These scenarios are executed in a power sector capacity expansion model and compared to two reference scenarios that assume status quo with policy and cost drivers. Our analysis indicates that future U.S. thermoelectric water withdrawals could decrease by 25%–60%, but water consumption could more than triple in some scenarios. These changes are driven by a combination of retirement of some power facilities, shifts in cooling technologies, and new technology deployment. The water use patterns vary across the United States, with the eastern regions demonstrating a lot more variability in water consumption across scenarios than western regions. However, local concerns can influence these possible investments, since increased water consumption can exacerbate water scarcity, leading to conflicts among competing users and affecting regional social, environmental, and economic dynamics. Future work should consider possible costs associated with alternate water sources, as well as improve the representation of water constraints within simulations. Inclusion of extreme events and alternate modeling platforms (e.g. production cost modeling and resource adequacy) may also be warranted to further stress test the robustness of these possible technology futures. Such assessments will be critical for ensuring decarbonization and other infrastructure-oriented investments lead to a reliable and resilient power grid.

Background In prior work we identified cortical resilience proteins associated with the linear trajectories of cognitive decline independent of the effects of neuropathology. Some of these proteins were associated with slower and some with faster cognitive decline. We tested the hypothesis that the temporal onset and duration of effects of cortical resilience proteins associated with cognitive trajectories may vary in aging adults. Method We used data from 1,088 older participants (31% males, mean age at death =89.7, SD = 6.4; mean education = 16.3 years, SD = 3.6) from two clinical‐pathologic studies. Participants completed a battery of 19 cognitive tests annually, underwent neuropathologic examination, and had targeted proteomic analysis performed in the dorsal lateral prefrontal cortex. To identify cortical resilience proteins, we first ran linear mixed effects models with cognitive decline as outcome, and the proteins as predictors, controlling for the effects of neuropathologic indices. To estimate the temporal effect of the identified resilience proteins on cognitive decline, we then ran functional mixed effects models, which allow i) individuals to have heterogeneous (linear and nonlinear) patterns of cognitive decline, and ii) the influence of predictors to vary flexibly over time. Result We identified 40 cortical resilience proteins which linear and nonlinear effects on cognitive decline spanned the entire studied trajectory of up to 25 years of follow‐up. We found that the effects of 17 proteins were significantly nonlinear: while 10 were associated with higher resilience, 7 were associated with lower resilience. The onset of effects of these proteins showed that i) the effects of higher resilience (slower rate of decline) proteins start up to 23 years before death, while the effects of the lower resilience (faster rate of decline) proteins are restricted within the last 7 years of life. Conclusion Multiple cortical proteins may underlie the resilience afforded by varied lifestyles and behaviors. Yet, the temporal window during which different proteins may provide cognitive resilience may vary. Different analytic techniques may be needed to elucidate the molecular mechanisms underlying cognitive resilience and their critical temporal window during later life.

There is considerable uncertainty regarding the impact of irrigation on heat stress, partly stemming from the choice of heat stress index. Moreover, existing simulations are at scales that cannot appropriately resolve population centres or clouds and thus the potential for human impacts. Using multi-year convection-permitting and urban-resolving regional climate simulations, we demonstrate that irrigation alleviates summertime heat stress across more than 1,600 urban clusters in North America. This holds true for most physiologically relevant heat stress indices. The impact of irrigation varies by climate zone, with more notable irrigation signals seen for arid urban clusters that are situated near heavily irrigated fields. Through a component attribution framework, we show that irrigation-induced changes in wet-bulb temperature, often used as a moist heat stress proxy in the geosciences, exhibit an opposite sign to the corresponding changes in wet bulb globe temperature—a more complete index for assessing both indoor and outdoor heat risk—across climate zones. In contrast, the local changes in both wet-bulb and wet bulb globe temperature due to urbanization have the same sign. Our results demonstrate a complex relationship between irrigation and heat stress, highlighting the importance of using appropriate heat stress indices when assessing the potential for population-scale human impacts.

Coastal ecosystems are at the nexus of many high priority challenges in environmental sciences, including predicting the influences of compounding disturbances exacerbated by climate change on biogeochemical cycling. While research in coastal science is fundamentally transdisciplinary—as drivers of biogeochemical and ecological processes often span scientific and environmental domains—traditional place–based approaches are still often employed to understand coastal ecosystems. We argue that a macrosystems science perspective, including the integration across distributed research sites, is crucial to understand how compounding disturbances affect coastal ecosystems. We suggest that many grand challenge questions, such as advancing continental‐scale process understanding of extreme events and global change, will only be addressed in coastal ecosystems using a network‐of‐networks approach. We identify specific ways that existing research efforts can maximize benefit across multiple interested parties, and where additional infrastructure investments might increase return‐on‐investment along the coast, using the coastal continental United States as a case study.

Traditional trial-and-error methods for materials discovery are too slow to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multistep chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical sampling constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a new approach that allows tailoring the ML decision-making to suit the practical constraints in individual HTP platforms, followed by performing resource-efficient yield optimization using already existing and available open-source Python libraries.

The impacts of climate change on human health are often underestimated or perceived to be in a distant future. Here, we present the projected impacts of climate change in the context of COVID-19, a recent human health catastrophe. We compared projected heat mortality with COVID-19 deaths in 38 cities worldwide and found that in half of these cities, heat-related deaths could exceed annual COVID-19 deaths in less than ten years (at + 3.0 °C increase in global warming relative to preindustrial). In seven of these cities, heat mortality could exceed COVID-19 deaths in less than five years. Our results underscore the crucial need for climate action and for the integration of climate change into public health discourse and policy.

Lithium–sulfur (Li–S) all-solid-state batteries (ASSBs) hold great promise for next-generation safe, durable and energy-dense battery technology. However, solid-state sulfur conversion reactions are kinetically sluggish and primarily constrained to the restricted three-phase boundary area of sulfur, carbon and solid electrolytes, making it challenging to achieve high sulfur utilization. Here we develop and implement mixed ionic–electronic conductors (MIECs) in sulfur cathodes to replace conventional solid electrolytes and invoke conversion reactions at sulfur–MIEC interfaces in addition to traditional three-phase boundaries. Microscopic and tomographic analyses reveal the emergence of mixed-conducting domains embedded in sulfur at sulfur–MIEC boundaries, helping promote the thorough conversion of active sulfur into Li2S. Consequently, substantially improved active sulfur ratios (up to 87.3%) and conversion degrees (>94%) are achieved in Li–S ASSBs with high discharge capacity (>1,450 mAh g–1) and long cycle life (>1,000 cycles). The strategy is also applied to enhance the active material utilization of other conversion cathodes.

Multiferroic materials host both ferroelectricity and magnetism, offering potential for magnetic memory and spin transistor applications. Here, we report a multiferroic chalcogenide semiconductor Cu 1−x Mn 1+y SiTe 3 (0.04 ≤ x ≤ 0.26; 0.03 ≤ y ≤ 0.15), which crystallizes in a polar monoclinic structure (Pm space group). It exhibits a canted antiferromagnetic state below 35 kelvin, with magnetic hysteresis and remanent magnetization under 15 kelvin. We demonstrate multiferroicity and strong magnetoelectric coupling through magnetodielectric and magneto-current measurements. At 10 kelvin, the magnetically induced electric polarization reaches ~0.8 microcoulombs per square centimeter, comparable to the highest value in oxide multiferroics. We also observe possible room-temperature ferroelectricity. Given that multiferroicity is very rare among transition metal chalcogenides, our finding sets up a unique materials platform for designing multiferroic chalcogenides.