Grid Scale Research Articles

The contributions of climate and land use/cover changes to water yield services considering geographic scale

BackgroundWater yield services are critical for maintaining ecological sustainability and regional economies. Climate change and land use/cover change (LUCC) significantly affect regional water yield, but the spatiotemporal variability of water yield services has been overlooked in previous studies. This study aims to explore the relative contributions of climate and land use/cover changes to water yield services at both grid and subwatershed scales. MethodsThis study employed the InVEST model to calculate the water yield in the study area and employed a multi-scenario simulation approach to investigate the impacts of climate change and LUCC on water yield at both grid and subwatershed scales. Furthermore, the contributions of these two types of changes to water yield were quantified. ResultsFirstly, upstream areas experience significantly lower annual average precipitation, temperature, and potential evapotranspiration than downstream areas, with worsening drought severity. Secondly, urbanization led to significant LUCC, with decreases in farmland and grassland and increases in forest, water, building land, and unused land. Thirdly, the spatial heterogeneity of water yield services remains consistent across different scales, but more pronounced spatial clustering is observed at the subwatershed scale. Fourthly, climate change is the primary factor affecting regional water yield services, surpassing the influence of LUCC. Lastly, LUCC significantly impacts water cycling in watersheds, with vegetation coverage being a critical factor affecting water yield. ConclusionThese findings highlight the need to consider the complex relationships between climate change, LUCC, and water yield services at multiple scales in water resource management.

A Structural Perspective on Prussian Blue Analogues for Aqueous Zinc‐Ion Batteries

AbstractAqueous zinc‐ion batteries (AZIBs), have been identified as one of the most promising aqueous rechargeable metal‐ion batteries (ARMIBs) for grid scale electrochemical energy storage application, and as such have received much attention by virtue of their unique properties including the high abundance of metallic zinc resources, their intrinsic safety, and cost effectiveness. Among the cathode materials used in AZIBs, Prussian Blue Analogues (PBAs) represent the most promising active materials in view of their improved cyclability and rate performance. Here, we provide a comprehensive review on recent progress on AZIBs obtained using PBAs cathodes with a particular focus on the present understanding of the structure‐property correlation of different PBAs, which in turn guides further developments of improved cathode materials and optimization strategies to ensure an extended cyclability and capacity retention.

How do 2D/3D urban landscapes impact diurnal land surface temperature: Insights from block scale and machine learning algorithms

Urban landscapes significantly affect land surface temperature (LST) and are considered crucial factors affecting urban heat island (UHI). The impacts of urban landscapes on LST have been extensively explored, mainly focusing on grid scale and the daytime. However, how 2D/3D urban landscapes affect diurnal LST at the block scale is unclear. Therefore, taking 1, 536 blocks (including low-rise blocks (LRB), middle-rise blocks (MRB), and high-rise blocks (HRB)) in Beijing as samples, the performances of boosted regression tree (BRT) and random forest (RF) were first evaluated, and the impacts of 2D/3D urban landscapes on diurnal LST across different block types were explored. The results showed that the mean LST was the highest in MRB (daytime) and HRB (nighttime). BRT performed better than RF in investigating diurnal impacts at the block scale. Vegetation and buildings are the domain factors influencing daytime and nighttime LST in LRB and MRB, while buildings are the domain factor in HRB except at 03:09 (impervious surface). The relationships between the key 2D/3D urban landscape metrics and block diurnal LST are nonlinear. The findings can serve as f basis for UHI mitigation and urban renewal strategies by urban planners to develop thermal comfort.

Study into the Evolution of Spatiotemporal Characteristics and Driving Mechanisms of Production–Living–Ecological Spaces on the Indochina Peninsula

Influenced by historical background, regional economic development, and the frequent occurrence of armed conflict, the human–earth relationship in the Central and Southern Peninsula, which is located in a “fragmented zone”, is characteristic of the region. The Indochina Peninsula has now become an area of interest for the study of spatial changes in production–living–ecological spaces (PLES). Taking the Indochina Peninsula as the study area, this paper explores the evolution of the spatiotemporal patterns of PLES and its driving mechanism in the Indochina Peninsula, from 2010 to 2020, based on a grid scale. Methods such as the land-use transition matrix, land-use dynamics index, and geographically and temporally weighted regression (GTWR) were used in our model, which will provide the basic data and reference for sustainable development planning across the Indochina Peninsula. Our results show that, from 2010 to 2020, ecological space dominated the PLES pattern on the Indochina Peninsula, but its area gradually decreased, accompanied by a sharp increase in the areas of productive and living spaces. The area of PLES interconversion on the Indochina Peninsula in 2010–2020 was 212,818.70 km2, and the intertransfer of production and ecological spaces was distributed in a networklike manner throughout the Indochina Peninsula, while the transfer of living space was distributed in a pointlike manner. The migration path of the center of gravity of PLES on the Indochina Peninsula demonstrated a significant directional difference, and the direction and extent of the standard deviation ellipse distribution of the ecological space was similar to that of the production space. The PLES’s pattern evolution was affected by the degree of multiple factors, with a significant spatial and temporal heterogeneity. The positive and negative feedback effects of the factors were distributed in different areas and in different transfer directions.

Fine grid scale increases or decreases the trade-off effect between ES in mountain protected areas?

A crucial challenge in ecosystem management is the identification of trade-off effects among multiple ecosystem services (ESs), particularly in complex and ecologically vulnerable mountainous protected areas. Nevertheless, investigations into the spatio-temporal dynamics of trade-offs and synergies among ES within such protected regions remain limited. This study concentrated on the Qilian Mountain, a critical ecologically fragile protected area, utilized the Invest model to comprehensively analyze six ESs over the period 1985–2020. Employed Pearson correlation coefficient, we scrutinized trade-off and synergy relationships of these ES across varying grid scales (30 m, 1 km, and 10 km). Furthermore, GeoDetector, in tandem with multi-source data, is employed to unveil the potential drivers influencing the patterns of ES. Our findings revealed that: (1) Increasing data spatial resolution refines grid scales, yielding smoother spatial distributions of ES, and accentuates spatial disparities. (2) Synergistic relationships prevailed among protected area ES from 1985 to 2020, with correlations between carbon storage - water yield and habitat quality - water yield shifted from synergy to trade-off in 2015. (3) Distinct grid scales exert non-linear impacts on ES correlations, affecting both the magnitude and direction of these relationships. (4) Interactions among natural factors, climate, and landscape patterns are principal drivers of spatial heterogeneity in ES, with the contributions of dominant drivers exhibiting temporal variability. Remarkably, factors with stronger driving forces demonstrate greater temporal instability in their contribution rates. Our research underscores the necessity for comparative assessments of ES and their interrelations at diverse data spatial resolutions, advocating for enhanced ES management strategies.

Qualitative-quantitative identification and functional zoning analysis of production-living-ecological space: a case study of Urban Agglomeration in Central Yunnan, China.

Territorial space exhibits multiple functional attributes, which comprise production, living, and ecological functions usually. Optimizing the production-living-ecological space (PLES) has become the key to territorial and spatial planning; the scientific identification of the PLES lays a foundation for space optimization and has important guiding significance in territorial spatial zoning. To achieve the integration of macro-scale and micro-scale PLES, with the Urban Agglomeration in Central Yunnan as the research area in this study, the PLES functional identification systems from the administrative unit scale and the grid scale are constructed. The types of PLES are determined by integrating qualitative and quantitative evaluation results and using an improved primacy index model from a composite spatial perspective. On that basis, the division of comprehensive zoning is achieved for land use functions through kernel density analysis. As indicated by the results, the model is capable of reflecting the macro background of the PLES functions in administrative regions while characterizing the micro differences at the grid level in administrative units. There are significant differences in the production, living, and ecological functional spaces in the Urban Agglomeration. Production functions are concentrated in the central and northeastern, living functions are concentrated in the central, and ecological functions are concentrated in the western and northeastern, with significantly consistent or complementary spatial distributions of each other. The PLES of Urban Agglomeration includes production space (PS), ecological space (ES), production-living space (P-LS), production-ecological space (P-ES), living-ecological space (L-ES), and production-living-ecological space (P-L-ES), placing a focus on ES, P-ES, and P-L-ES, which marks significant differences in spatial distribution among different spatial types. The study area is divided into 24 functional zones, which are classified into 6 categories, and optimization paths are proposed. This study will provide a reference for territorial and spatial planning in spatial functional zoning, spatial pattern optimization, and land management applications.

Digital soil mapping of heavy metals using multiple geospatial data: Feature identification and deep neural network

Monitoring the spatial distribution and sources of heavy metals (HM) in soil is essential for avoiding health risks and achieving sustainable soil utilization. Multiple geospatial data, including remote sensing, climate, soil and topography data, were used to extract environmental covariates. Additionally, the spatial scene was employed as the alternative data of land use/land cover to describe the urban functions and human activity intensity in more detail. After converting to a uniform resolution of 30 m, these environmental covariates were adopted to characterize several common HM in soil, including copper (Cu), chromium (Cr), lead (Pb), nickel (Ni), and zinc (Zn). The RReliefF algorithm was used to identify several important variables. The quantification models of HM were established using back-propagation neural network (BPNN) and deep neural network (DNN). Besides, the impact of distance from the spatial scenes on HM were analyzed. The result demonstrated that the spatial scene is a key environmental covariate in estimating HM in soil. Compared with BPNN, the DNN model provided better accuracy (R2 = 0.67–0.75) for estimation of five HM elements. Therefore, the DNN model was used to map HM concentrations at a grid scale of 30 m. The spatial scenes with the highest risk of HM pollution are industrial areas, residential areas, road, and commercial areas, and the concentration of HM is negatively correlated with the distance from these spatial scenes. The effective impact distances of industrial and residential areas are about 2000 m, and the effective impact distances of road and commercial areas are 500 m.

Critical Nodes Identification for Power Grid Based on Electrical Topology and Power Flow Distribution

The continuous development of the power industry has driven the increasing scale and complexity of power grids. There are critical nodes within these grids, the failure of which can easily lead to serious power system accidents. In order to effectively identify the critical nodes in a power grid, a critical nodes identification algorithm considering electrical topology and power flow distribution (ETPD-CNIA) is proposed. First, a node interaction model based on the line outage distribution factors is presented. Then, the model is introduced into the PageRank algorithm to calculate the node importance based on the electrical network topology. Finally, considering the node importance based on both the electrical network topology and the actual power flow distribution by an adaptive coefficient, the critical nodes are identified effectively. Simulations on the IEEE 39-bus system and the IEEE 118-bus system show that, compared with existing algorithms, the proposed ETPD-CNIA is more effective and accurate in identifying critical nodes in the power grid.

Cosmic-Ray Drag and Damping of Compressive Turbulence

While it is well known that cosmic rays (CRs) can gain energy from turbulence via second-order Fermi acceleration, how this energy transfer affects the turbulent cascade remains largely unexplored. Here, we show that damping and steepening of the compressive turbulent power spectrum are expected once the damping time becomes comparable to the turbulent cascade time. Magnetohydrodynamic simulations of stirred compressive turbulence in a gas-CR fluid with diffusive CR transport show clear imprints of CR-induced damping, saturating at , where is the turbulent energy input rate. In that case, almost all of the energy in large-scale motions is absorbed by CRs and does not cascade down to grid scale. Through a Hodge–Helmholtz decomposition, we confirm that purely compressive forcing can generate significant solenoidal motions, and we find preferential CR damping of the compressive component in simulations with diffusion and streaming, rendering small-scale turbulence largely solenoidal, with implications for thermal instability and proposed resonant scattering of E ≳ 300 GeV CRs by fast modes. When CR transport is streaming dominated, CRs also damp large-scale motions, with kinetic energy reduced by up to 1 order of magnitude in realistic E CR ∼ E g scenarios, but turbulence (with a reduced amplitude) still cascades down to small scales with the same power spectrum. Such large-scale damping implies that turbulent velocities obtained from the observed velocity dispersion may significantly underestimate turbulent forcing rates, i.e., .

Rainbows and climate change: a tutorial on climate model diagnostics and parameterization

Abstract. Earth system models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and chemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an ESM: the Community Earth System Model version 2 (CESM2). Using the “state” of the model, basic physical laws, and some assumptions, we generate a representation of this unique optical phenomenon as a diagnostic output. Rainbow occurrence and its possible changes are related to cloud occurrence and rain formation, which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostic parameterizations such as assumptions, uncertain parameters, and the difficulty of evaluation against uncertain observations. Results agree qualitatively with limited available global “observations” of rainbows. Rainbows are seen in expected locations in the subtropics over the ocean where broken clouds and frequent precipitation occur. The diurnal peak is in the morning over ocean and in the evening over land. The representation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows are projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the future, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic projects that rainbows will increase in the future, with the largest changes at midlatitudes. The diagnostic may be useful for assessing cloud parameterizations and is an exercise in how to build and test parameterizations of atmospheric phenomena.

Supersonic Base Flow With and Without Propulsive Jets: A Brief Review

Supersonic base flow is encountered in many aerospace applications related to satellite launch vehicle, missiles and projectiles. The origin and evolution of large scale structures which govern the dynamics of the base flow is not properly understood. The presence of central propulsive jet further complicates the base flow characteristics. Different semi-empirical, experimental and numerical studies carried out in supersonic base flow is briefly reviewed. Various issues and challenges faced in CFD simulation of base flow problems are described. Apart from resolution of numerics, proper choice of turbulence model remains the key aspect in the Reynolds Averaged Navier Stokes (RANS) simulations. Prediction of axial distribution of centerline velocities and recirculation bubble size remain problematic in RANS simulation. Detached Eddy Simulation (DES) results show dependence on model constants. Large Eddy Simulation (LES) studies with proper filter and Sub Grid Scale (SGS) model is promising, but its applications are limited to canonical problems due to very high computational requirements. Direct Numerical Simulation (DNS) studies reported in the literature consider much lower Reynolds number and hence could not be compared with the experimental data. It is suggested to use RANS and DES methods with anchored turbulence model from well-designed experiments for design data generation. Efforts should be made to use higher order numerical methods (LES and DNS) to explore the origin and evolution of large scale structures observed in compressible base flow.

Climate warming increases extreme daily wildfire growth risk in California.

California has experienced enhanced extreme wildfire behaviour in recent years1-3, leading to substantial loss of life and property4,5. Some portion of the change in wildfire behaviour is attributable to anthropogenic climate warming, but formally quantifying this contribution is difficult because of numerous confounding factors6,7 and because wildfires are below the grid scale of global climate models. Here we use machine learning to quantify empirical relationships between temperature (as well as the influence of temperature on aridity) and the risk of extreme daily wildfire growth (>10,000 acres) in California and find that the influence of temperature on the risk is primarily mediated through its influence on fuel moisture. We use the uncovered relationships to estimate the changes in extreme daily wildfire growth risk under anthropogenic warming by subjecting historical fires from 2003 to 2020 to differing background climatological temperatures and aridity conditions. We find that the influence of anthropogenic warming on the risk of extreme daily wildfire growth varies appreciably on a fire-by-fire and day-by-day basis, depending on whether or not climate warming pushes conditions over certain thresholds of aridity, such as 1.5 kPa of vapour-pressure deficit and 10% dead fuel moisture. So far, anthropogenic warming has enhanced the aggregate expected frequency of extreme daily wildfire growth by 25% (5-95 range of 14-36%), on average, relative to preindustrial conditions. But for some fires, there was approximately no change, and for other fires, the enhancement has been as much as 461%. When historical fires are subjected to a range of projected end-of-century conditions, the aggregate expected frequency of extreme daily wildfire growth events increases by 59% (5-95 range of 47-71%) under a low SSP1-2.6 emissions scenario compared with an increase of 172% (5-95 range of 156-188%) under a very high SSP5-8.5 emissions scenario, relative to preindustrial conditions.

An Efficient Dynamic Coupling Calculation Method for Dam–Reservoir Systems Based on FEM-SBFEM

In the dynamic analysis of dam–reservoir interactions, the computational efficiency of coupling system is relatively low. When numerical methods such as the scaled boundary finite element method (SBFEM) or the finite element method (FEM) are used to deal with hydrodynamic pressure, the additional mass matrix for the hydrodynamic pressure of incompressible reservoir water obtained is the full matrix. In this study, an efficient three dimensional (3D) dynamic fluid–solid coupling analysis method for dam–reservoir systems based on the FEM-SBFEM is proposed and applied to the dynamic calculation and analysis of an arch dam under seismic conditions, which adopts the SBFEM to solve the hydrodynamic pressure of the reservoir and employs the FEM to discretize the dam. In the proposed method, the hydrodynamic pressure additional mass matrix is simplified according to the physical meaning and distribution characteristics of the additional matrix with only a reduction coefficient α (0 < α ≤ 1.0), which is simple and easy to implement. The suggested value of the reduction coefficient α for the added mass matrix of the hydrodynamic pressure is selected to be 0.6 so as to ensure that the error of the maximum value of the dynamic response of the dam is limited within 5%, which is acceptable, and the elapsed time of calculation can be reduced to one twentieth of the accurate solution, which is a great jump in calculation efficiency. The proposed method provides a practical and effective process for the analysis of dam–reservoir dynamic interaction systems with a large computational scale and a fine grid scale.

Interactive response and spatial heterogeneity of alpine grassland biodiversity to soil, topography, climate, and human factors on the Qinghai‐Tibet Plateau

AbstractThe alpine grassland ecosystem is a critical part of the Qinghai‐Tibet Plateau (QTP), mainly including alpine meadows and alpine steppes. However, the influence factors of alpine grassland biodiversity are not clear for different alpine grassland types. In this study, pathway analysis was applied to explore the influences of soil, topography, climate, and human factors on alpine grassland biodiversity. Then, by using the geographically weighted regression (GWR) model, the spatial sensitivity response of alpine grassland biodiversity to soil, topography, climate, and human activities was revealed in different grassland types. The results were as follows: (1) The data and pathway analysis passed the validity and reliability analysis, indicating that the model fit of alpine grassland biodiversity was good for different grassland types. (2) For alpine meadows, the total effect coefficients of altitude, slope, and topographic wetness index (TWI) on normalized difference vegetation index (NDVI) (net primary productivity [NPP]) were 0.50, 0.21, and −0.48 (0.59, 0.17, and −0.41), respectively, suggesting that altitude had the strongest and positive effect on alpine meadow diversity. In addition, among human activity and climate variables, human activity intensity and precipitation exhibited significant effects on NDVI (NPP), with total effect coefficients of −0.17 and 0.61 (−0.19 and 0.63). The influence of soil, topography, climate, and human variables on alpine steppe diversity showed similarities with alpine meadow diversity. (3) Alpine grassland biodiversity was directly and indirectly influenced by multiple driving factors for different grassland types. The total coefficients of soil, topography, climate, and human factors on NDVI (NPP) were 0.41, 0.38, −0.21, and 0.71 (0.32, 0.50, −0.21, and 0.91), respectively, which indicated that alpine meadow diversity was mainly regulated by climate on the QTP, consistent with alpine steppe diversity. (4) For different grassland types, the spatial effects of various driving factors on alpine grassland biodiversity all showed significant spatial heterogeneity at the grid scale. Based on the knowledge about the driving mechanism and spatial response of alpine grassland biodiversity to its driving factors in different grassland types of the QTP, we aim to provide guidance for alpine grassland biodiversity conservation and rational ecosystem management.

Optimizing limit lines in urban-rural transitional areas: Unveiling the spatial dynamics of trade-offs and synergies among land use functions

The spatial non-stationarity of the trade-off/synergy among land use functions is manifested in the complexity of interrelationships and the heterogeneity of spatial distributions. However, existing research rarely applies these spatial non-stationarity features to fine-scale zoning controls, reducing the applicability of zoning results and limiting the refined development of territorial space. This is essential for high-quality development of territorial space in urban-rural transitional areas with rapid land use function transitions. Taking the region between the first and second bypass expressways of Hangzhou as an example, this research identified and measured the spatial non-stationarity characteristics of the trade-off/synergy among different function changes from a production-living-ecological function perspective during 2010–2020. A territorial space zoning control framework from the grid scale to the township scale, which considered the spatial non-stationarity of trade-offs/synergies and the dominant land use functions, was constructed to support synergistic territorial space development in urban-rural transitional areas. The results demonstrated that the trade-off/synergy among distinct function changes during 2010–2020 had significant spatial heterogeneity and gradient characteristics. The interaction between living function and other functions showed an increasing trade-off, while that between production and ecological functions varied from trade-off to synergy. These interrelationships produced six spatial non-stationarity forms, which exhibited obvious spatiotemporal differences in different trade-off/synergy bundles. 12 territorial spatial functional zones at the grid scale and 5 dominant functional zones at the township scale were subdivided for territorial spatial management. Our logical framework and outcomes provide a scientific guide to achieve the synergistic development of territorial space in urban-rural transitional areas and other similar regions.

Improving the Ionic Conductivity and Electrochemical Performance of NASICON Based Ca2+ Doped NZSP Solid-State Electrolyte for Solid State Sodium Batteries

Major efforts have been made in the search of high-energy and power density batteries to keep up with the demands of the rapidly growing portable electronics market and EV industry. Though lithium-ion batteries have dominated this field, sodium ion batteries are increasing in popularity due it’s wide availability and low cost. Conventional Sodium ion batteries with liquid electrolyte usually have several problems, including limited electrochemical window, flammability, and leakages which are a potential safety hazard. One of the most promising approaches to improve the safety of a battery is to replace the liquid ion conducting electrolyte to a solid ion conducting electrolyte. Even so, how to develop such a high-performance solid-state electrolyte (SSE) that is compatible with both the anode and cathode interface is still challenging. Furthermore, low ionic conductivity electrolytes, dendrite formation and poor cycling capabilities are adding on to the challenges. As one means to tackle these challenges and improve the electrochemical performances of the battery, cation substitution can be considered. Here, NASICON (Sodium Super Ionic Conductor) based Na1+xZr2SixP3-xO12 (NZSP) solid state electrolyte, its phase behavior and its performances in solid state batteries are introduced. Current trends & perspectives on interface engineering and cation substitution of the NZSP electrolyte is also discussed. Although NASICON material lacks mechanical flexibility, it has high ionic conductivity. Substitutions with La3+ and Sc3+ have shown to improve the ionic conductivity of the battery but have also proven to be expensive. In order to make it more feasible, cations such as Ca2+ should be considered. Pure phase NZSP materials with Ca2+ substitution for Zr4+ will be synthesized using solid-state sintering. Along with the Ca-ion substitution, addition of excess sodium into the Ca-ion substituted NZSP system will be performed and a comparative analysis will be conducted. Here, we will analyze the electrochemical performance through XRD Analysis, SEM Analysis, Impedance Spectroscopy and Galvanostatic Charge/Discharge testing. The development of solid-state sodium electrolytes lead to a brighter future where all solid state sodium batteries could be used to power electric vehicles, electronic devices and contribute towards large scale storage and grid applications.

High Voltage Long-Lifetime Mild pH-Decoupling Aqueous Flow Battery with in-Situ pH Recovery

Aqueous redox flow batteries (ARFBs) are being developed for grid scale electricity storage. However, the 1.23 V water splitting window makes the development of a long lifetime high-voltage ARFB challenging. Though the pH-decoupling design, with an acidic posolyte and an alkaline negolyte has been demonstrated to broaden the operating voltage window, long term operational stability and overall energy efficiency are penalized because of acid-base leakage along the pH gradient. Mild acid and base are used as supporting electrolytes to diminish proton and hydroxide crossover rates, resulting in higher overall energy efficiency. An acid-base in-situ regeneration system is implemented to restore the negolyte and posolyte pH to starting values. The capacity fade rate achieved is < 0.08%/day, with > 75% overall energy efficiency, and the Coulombic efficiency is around 99%. This work demonstrates a possibility of solving the critical issues (lifespan, rate capability, long term practicability and energy efficiency) for pH-decoupling ARFBs and guides the design for next generation high voltage ARFBs.

(Invited) Grid Scale Energy Storage Poised for Success: Developing New Technologies, Finding New Value Propositions Towards Long Duration Energy Storage

Energy storage (ES) has become one of the hottest topics to the world’s clean energy future. Research on materials and devices has made successful progress in reducing the cost of Li-ion systems and of several flow battery systems. However, safety, material sustainability, and other factors such as maintenance and decommissioning costs that affect the levelized cost of storage also need to be considered in developing a circular economy to enable reliable, resilient, and flexible electric grid.ES in the United States (US) has become a resounding success. We have done well in short-duration energy storage, by developing viable business models, evaluation and planning tools, and so on. ES has achieved a great feat in frequency regulation, smoothing renewable energy, demand charge reduction, and substation upgrade deferrals. Going forward, energy storage of medium and long duration as well as short duration with further lower cost will be needed.The presentation will give a high-level overview on the current state of DOE’s Office of Electricity (OE) supported energy storage technologies. Sodium-ion batteries, aqueous zinc batteries, and several new lines with earth abundant, inexpensive materials, and reliable supply chains and a wide industrial base will be covered. Reliability, safety, and representative examples of the successful deployment of OE technologies across multiple states in US will be touched.As the return on investment is what really counts for the commercial success of energy storage applications, the presentation also will discuss several real cases of diverse monetized and unmonetized benefit streams from multi-megawatt deployments and how these assessments are needed for new technologies and longer duration applications.

Towards efficient and accurate CO2 sequestration optimization: Integrating hierarchical spatio-temporal information into deep-learning-based surrogate models

Optimizing CO2 storage in brine water is a critical task that requires process-level methods based on geological modeling and numerical simulation. However, these methods can be computationally expensive, especially when simulating larger grid scales and longer simulation times. To address this challenge, we proposed a novel approach that utilizes advanced deep learning techniques to develop a U-LSTM-net neural network capable of incorporating spatio-temporal information at varying hierarchies. The method employs an encoder-decoder architecture that extracts, memorizes, and integrates localized and holistic information from changing receptive fields. Multi-task learning algorithms enable simultaneous learning and predicting multiple flow fields. Additionally, we employed transfer learning to transfer learned knowledge and experience to new tasks and a fine-tuning algorithm to improve the retention of domain-general knowledge. We demonstrated the effectiveness of our model on synthetic models and an adjusted 3D Brugge mode. The U-LSTM-net effectively captures and integrates spatio-temporal information, exhibiting powerful memory capacity. Balancing algorithms improve multiple tasks' learning efficiency and avoid the dominance of tasks with higher loss values. The fine-tuning algorithm reduces overfitting and preserves pre-trained knowledge better, thereby reducing the demand for extensive information. Furthermore, when transferred to the 3D authentic problem, the surrogate model exhibits impressive predictive capabilities for various attributes across different layers. This study integrated multiple learning methods and established a precise and practical learning workflow for accelerating the prediction of the CO2 sequestration dynamic.

Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions

Transportation has become one of the fastest-growing fields for greenhouse gas emissions. It is important to promote the coordinated development of cities and transportation. To deeply understand the emission distribution for urban travel, this study first applies a map matching algorithm to correct the vehicle Global Positioning System (GPS) trajectories on the road network and calculates the travel emissions by the COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Most studies on the impact of built environment on travel emissions only consider the spatially heterogeneity of variables. Although the traditional GTWR can consider the spatio-temporally heterogeneous effects, the spatio-temporal bandwidth is selected at the same scale in the operation process, which limits the relationship analysis among diverse variables. Therefore, this study adopts a unilateral geographically and temporally weighted regression model (UGTWR) and its multiscale extended model (MUGTWR) to estimate the spatio-temporally heterogeneous effects of the built environment on urban travel emissions. In addition, considering the scale effect and zoning effect of the spatial geographical unit selection on the results, we conducted experiments separately on three spatial units: sub-district scale, Traffic Analysis Zones (TAZs) and grid scale to compare and obtain the most appropriate partitioning schemes. The results show that, the fitting effects of UGTWR and MUGTWR are better than that of GTWR, indicating that considering flexible bandwidth selection can effectively improve the accuracy of simulation. Moreover, the temporal and spatial bandwidth values of each independent variable calculated by MUGTWR reflect the heterogeneous characteristics in space and time of different built environment factors, which can provide scientific suggestions for formulating urban planning. The research results can provide planning strategies for optimizing the allocation of local transportation resources and guiding low-carbon travel behaviors.