Grid Level Research Articles

Towards carbon neutrality in China: A systematic identification of China's sustainable land-use pathways across multiple scales

Sustainable land use is crucial for achieving Carbon Neutrality goals, which requires a scientific identification of optimized pathways for land use patterns across multiple scales. Yet, current land use studies predominantly focus on single scales but lack system thinking and fail to establish complementary cross-regional carbon neutrality collaboration schemes. Applying life-cycle thinking to analyze land use sustainability and carbon neutrality potential at multiple scales could address this challenge. This study aims to present China's first multi-scale spatiotemporal optimization pathway for sustainable land use to improve carbon neutrality potential. It systematically integrates the complex spatial coupling relationships between land use intensity and efficiency. We integrate multi-scale sustainable land use pathways, spanning grid, basin, and administrative levels, and unveil significant variations in land use sustainability and carbon neutrality potential across China. Sixty-three percent of China's land is in low sustainability, and the overall carbon neutrality potential in China is relatively low, with regions accounting for <30 % facing more carbon neutrality missions. Implementing sequential and partitioned governance modes can effectively support China in achieving sustainable land use and advancing Carbon Neutrality goals. Our sustainable land use pathways for China provide valuable insights for systematically undertaking carbon neutrality actions across different scales.

A grid-level segmentation model based on encoder-decoder structure with multi-source features for crop lodging detection

Crop lodging assessment plays a critical role in acquiring accurate information regarding the location and area of lodging, which is essential for loss assessment and adjustments of harvester parameters. In this paper, we proposed LDVO (Lodging Detection with Visible-image Only), a comprehensive grid-to-grid semantic segmentation method for timely and accurate identification of crop lodging. The LDVO model uses Inception block and dense connection to construct a lightweight feature extraction network, and complements the texture feature and crop vegetation indices as reference features for semantic segmentation. Besides, the model meshes the aerial images according to the operation characteristics of the harvester, and the accuracy of the segmentation task is reduced from the pixel level to the grid level, which minimizes the network scale and computing cost under the premise of meeting the accuracy requirements of lodging detection. Experimental results demonstrate the superiority of the proposed LDVO model over mainstream semantic segmentation networks in terms of processing speed and model parameters. Remarkably, the LDVO model achieves the highest prediction accuracy of 94.86% by leveraging a combination of RGB semantic features, VIs and texture features. Therefore, the proposed LDVO model provides a fast, feasible and low-cost reference for monitoring crop lodging status in complex field environments. It also offers a universal idea for the improvement of semantic segmentation network in special application scenarios.

Multiscale Analysis of Spatial Accessibility to Acute Hospitals in Carinthia, Austria

Health care accessibility studies are well established in the US but lacking in Austria, even though both experience high costs and have hospital care as the largest contributor to health care spending. This study aims to examine multiscale spatial accessibility to acute hospitals in Carinthia, Austria. Using the most recent data at census block and 250 meter grid levels, we refine proximity and generalized two-step floating catchment area (G2SFCA) methods while accounting for the modifiable areal unit problem (MAUP) and edge effects. For census blocks and 250 meter grids, the mean travel times to the nearest acute hospitals are 16 and 21 min, respectively, covering 58.8% and 76.2% of the population, which, however, increases to 25 and 31 min to the three nearest hospitals with similar populations. People bypassing the nearest hospital to seek hospitals at a longer distance, termed “bypass behavior”, is more influential, as 20% more of the population living in mountainous or rural areas need to travel 30 min longer. The G2SFCA method with a more pronounced distance decay results in a more decentralized polycentric structure of accessibility and identifies poorer access areas. While urban advantage is most evident in Klagenfurt and Villach, not all areas near hospitals enjoy the highest accessibility. A combination of the proximity and G2SFCA methods identifies less accessible areas. The MAUP overestimates accessibility at a coarse level and in less populous areas. Edge effects occur at the border when using proximity only, but they are more sensitive when considering bypass behavior or a weak distance decay effect. This study contributes to our understanding of acute hospitals’ accessibility in Carinthia and highlights the need to improve low-accessible areas in addition to universal health coverage. Cautions need to be exercised when using different geographic units or considering edge effects for health care planning and management.

A posteriori error estimates for hierarchical mixed-dimensional elliptic equations

Abstract Mixed-dimensional elliptic equations exhibiting a hierarchical structure are commonly used to model problems with high aspect ratio inclusions, such as flow in fractured porous media. We derive general abstract estimates based on the theory of functional a posteriori error estimates, for which guaranteed upper bounds for the primal and dual variables and two-sided bounds for the primal–dual pair are obtained. We improve on the abstract results obtained with the functional approach by proposing four different ways of estimating the residual errors based on the extent the approximate solution has conservation properties, i.e., (1) no conservation, (2) subdomain conservation, (3) grid-level conservation, and (4) exact conservation. This treatment results in sharper and fully computable estimates when mass is conserved either at the grid level or exactly, with a comparable structure to those obtained from grid-based a posteriori techniques. We demonstrate the practical effectiveness of our theoretical results through numerical experiments using four different discretization methods for synthetic problems and applications based on benchmarks of flow in fractured porous media.

Discretization Error Estimation Using the Unsteady Error Transport Equations

Abstract Computational Fluid Dynamics (CFD) has gained significant utility in the analysis of diverse fluid flow scenarios, thanks to advances in computational power. Accurate error estimation techniques are crucial to ensure the reliability of CFD simulations, as errors can lead to misleading conclusions. This study focuses on the estimation of discretization errors in time-dependent simulations, building upon prior work addressing steady-state problems Wang et al. (2020, “Error Transport Equation Implementation in the Sensei CFD Code,” AIAA Paper No. 2020-1047). In this research, we employ unsteady error transport equations (ETE) to generate localized discretization error estimates within the framework of the finite volume CFD code SENSEI. For steady-state problems, the ETE only need to be solved once after the solution has converged, whereas the unsteady ETE need to be co-advanced with the primal solve. To enhance efficiency, we adopt a one-sided temporal stencil and develop a modified iterative correction process tailored to the unsteady ETE. The time-marching schemes utilized encompass second-order accurate singly diagonally implicit Runge–Kutta (SDIRK) and second-order backward differentiation formula (BDF2), both being implicit. To rigorously assess the accuracy of our error estimates, all test cases feature known analytical solutions, facilitating order-of-accuracy evaluations. Two test cases are considered: the 2D convected vortex for inviscid flow and a cross-term sinusoidal (CTS) manufactured solution for viscous flow. Results indicate higher-order convergence rates for the 2D convected vortex test case even when iterative correction is not applied, with similar observations in the CTS case, albeit not at the finest grid levels. Although the current implementation of iterative correction exhibits lower stability compared to the primal solve, it generally enhances the discretization error estimate. Notably, after iterative correction, the discretization error estimate for the unsteady ETE achieves higher-order accuracy across all grid levels in the 2D CTS manufactured solution.

Impacts of Urban Morphology on Seasonal Land Surface Temperatures: Comparing Grid- and Block-Based Approaches

Climate change is expected to result in increased occurrences of extreme weather events such as heat waves and cold spells. Urban planning responses are crucial for improving the capacity of cities and communities to deal with significant temperature variations across seasons. This study aims to investigate the relationship between urban temperature fluctuations and urban morphology throughout the four seasons. Through quadrant and statistical analyses, built-environment factors are identified that moderate or exacerbate seasonal land surface temperatures (LSTs). The focus is on Seoul, South Korea, as a case study, and seasonal LST values are calculated at both the grid (100 m × 100 m) and street block levels, incorporating factors such as vegetation density, land use patterns, albedo, two- and three-dimensional building forms, and gravity indices for large forests and water bodies. The quadrant analysis reveals a spatial segregation between areas demonstrating high LST adaptability (cooler summers and warmer winters) and those displaying LST vulnerability (hotter summers and colder winters), with significant differences in vegetation and building forms. Spatial regression analyses demonstrate that higher vegetation density and proximity to water bodies play key roles in moderating LSTs, leading to cooler summers and warmer winters. Building characteristics have a constant impact on LSTs across all seasons: horizontal expansion increases the LST, while vertical expansion reduces the LST. These findings are consistent for both grid- and block-level analyses. This study emphasizes the flexible role of the natural environment in moderating temperatures.

An examination of sUAS operations in proximity to a major U.S. Airport

The use of small, Unmanned Aircraft Systems (sUAS) has proliferated in the past several years. While operators are supposed to avoid operations in the immediate vicinity of airports, there are sometimes violations. In extreme cases, these violations can result in the complete grounding of flights at major airports around the world. These disruptions can lead to thousands of flights canceled and hundreds of thousands of passengers displaced. A compounding problem is the lack of empirical data to understand the amount of, and types of, sUAS operations around major airports. The purpose of this study was to assess the characteristics of sUAS operations near the Dallas-Fort Worth International Airport (DFW) by objectively analyzing data collected over a 36-month timeframe. The findings provide empirical data on the types of sUAS operations around DFW, yearly variance in operations, levels of UAS facility map compliance, types and times of sUAS flown, and sUAS launching points. The data indicates a growing trend in operations between 2018 and 2020, with a possible plateau in 2021 and beyond. sUAS operations in the airport vicinity are high, with approximately 70 % operating in the 400-foot grid levels or at points farther away from critical airport operations. Flights appear to follow seasonality and daily operation patterns, and growth seems to be in smaller vehicles with weights less than 250 g. In addition to the results, the authors discuss practical implications for National Airspace System (NAS) safety and the implications of integrating new operations into communities, such as drone deliveries and Advanced Air Mobility operations.

Robust spatiotemporal crash risk prediction with gated recurrent convolution network and interpretable insights from SHapley additive explanations

The vigorous progress in artificial intelligence and the widespread availability of computing power and big data have resulted in remarkable achievements in applying deep learning methods to traffic engineering. However, the complexity of deep learning models poses a significant challenge to their interpretability, despite their scalability to big data. As a result, academia has shown a keen interest in interpreting these intricate models. To address these limitations, this study introduces the Gated Recurrent Convolution Network (GRCN) for predicting spatiotemporal crash risks. Moreover, to transform the opaque nature of the GRCN into a transparent framework, SHapley Additive Explanations (SHAP) was employed, a game theory-based approach. The application of SHAP analysis not only enhances the explainability of the GRCN model but also reveals complex causal relationships regarding crash risks, using extensive datasets encompassing taxi trips data, law enforcement, and weather conditions. The study incorporates both local and global interpretability analyses at the grid level. Additionally, it indicates that intersection red light violations tend to elevate crash risks. The findings further demonstrate that adverse weather conditions, such as low visibility and strong crosswinds, negatively affect driving behavior and correlate with higher crash risks. However, the presence of cloudy skies is associated with a decrease in crash risks. By shedding light on the intricate dynamics of crash risk factors, this study contributes to the field of traffic engineering and emphasizes the importance of interpretability in deep learning models. The insights gained from this research can inform the development of effective interventions and policies aimed at reducing crash risks and improving overall transportation safety.

Peer-to-peer energy trading for demand response of residential smart electric storage water heaters

Environmental concerns and emission reduction targets are driving a transition from fossil fuel to renewable-based electricity generation. However, intermittent and distributed renewable generation brings challenges for the grid operation, as the low voltage distribution grid increasingly becomes constrained during high residential solar PV generation. The electrification of water heating presents a large opportunity to address this challenge by intelligently responding to electrical network conditions. Smart water heaters with thermal storage can soak up excess PV as a thermal battery, which facilitates maximum renewable generation and solves local grid problems. However, the centralized operation of electricity markets tends to impede the benefits of the localized distributed energy resources (DER). In such a scenario, peer-to-peer (P2P) energy trading allows neighbouring prosumers to trade energy between themselves with minimum interference from electricity grid operators. This research develops a P2P energy trading framework, using advanced, multi-zone electric storage water heaters with autonomous and aggregated control. The financial benefits identified in this research are forthcoming due to new participation in the electricity market ancillary services, demand management and P2P energy trading. Simulated results have identified that up to 92% of household water heating energy can originate from their own rooftop PV. At the electricity grid level, P2P energy trading demonstrates an increase in the aggregated PV self-consumption from 39% to 83%, importantly allowing the remaining 17% to target grid support in periods of supply shortfall. Average retail consumer energy savings of AUD$369/annum are identified, which include 17% of savings that are attributed to P2P energy transactions, delivering a capital payback of <3 years.

A multi-level framework for assessing the spatial equity of urban public space towards SDG 11.7.1 - a case study in Beijing

Rapid urbanization is reducing the limited urban public space available for residents to improve urban life. SDG 11.7.1 is used to assess the equity of urban public spaces, but it focuses only on the equity of public space at the city level, ignoring whether the public space resources in areas within the city match the needs of residents. Therefore, this paper proposes a multi-level framework for assessing the spatial equity of urban public space towards SDG 11.7.1. Based on this, the calculation method of SDG 11.7.1 is refined and the progress of SDG 11.7.1 is assessed at the city level. Then, the spatial equity of open public spaces (OPS) at the grid level is explored based on an improved 2SFCA model. The results show that Beijing has made rapid progress in promoting the implementation of SDG 11.7.1, but there is still a significant gap in the requirements of SDG 11.7. In addition, the spatial equity assessment results indicate significant spatial inequity in the allocation of OPS resources in Beijing. The study can provide a scientific reference for other cities to conduct quantitative measurements of SDG 11.7.1, and a scientific basis for planners and policy-makers to allocate urban OPS more rationally and equitably.

AN APPROACH TO THE IMPLEMENTATION OF THE MULTIGRID METHOD WITH FULL APPROXIMATION FOR CFD PROBLEMS

This paper is devoted to the use of the multigrid method to accelerate calculations of compressible flows in a stationary formulation on unstructured grids. The multigrid method is used with the construction of a full approximation for each grid level (FAS MG—Full Approximation Scheme Multigrid). In the case of an unstructured grid, such a method can cause difficulties associated both with the construction of grid levels and transition operators between them, and with software implementation in the existing simulation code. The program needs to deal with several different discretizations at once. If the entire data structure, including arrays with grid data, topology, and time integration data, was designed to work on a single grid, then the implementation of the FAS MG can turn into a disaster involving rewriting the entire code. The purpose of this work is to achieve multiple acceleration of calculations at the cost of minimal effort. The problem of implementing the multigrid method on the basis of an existing software package that was not designed to work with several grid levels is solved. The implementation of the multigrid method in an MPI parallel code is carried out in such a way that there is no need to rewrite the program to work with multiple grids at all. Also, difficulties with constructing grid levels for an unstructured grid are avoided; agglomeration of cells is not used, and the number of faces per cell at coarse levels is not increased. In fact, this paper describes how to deploy a FAS MG accelerator in literally a week, even in code that is outdated from the point of view of software architecture.

Identification of Ecological Risk “Source-Sink” Landscape Functions of Resource-Based Region: A Case Study in Liaoning Province, China

Ecological risk assessment plays an important role in ecosystem management and conservation. Conventional landscape-level assessment can only estimate the ecological risk level. It does not define ecological risk types, resulting in a lack of targeted regulation methods. This study establishes a model for identifying ecological risk-related “source-sink” landscape functions according to (1) “source-sink” landscape theory, (2) the responses of landscape types to ecological risks, and (3) the key influences on ecological risk. Four ecological risk “source-sink” landscape functions were mapped as a grid to understand their distribution. Natural and human activity factors were analyzed to determine their effects. After comprehensively considering the ecological risk levels, types of ecological risk, “source-sink” landscape functions, and their influencing factors, six principles and twenty-four targeted regulation strategies were proposed. Take the Liaoning province, China, as an example. The results prove that more than 80% of the grids were affected by the ecological risk “sink” landscape function for different and multiple ecological risks in the study area. Landscapes with the “source” function were mainly located in central cities and coastal areas. About 65% of the grids with “sink” landscape functions had medium, moderate-high, and high ecological risks. More than 75% of the grids with “source” landscape functions had medium, moderate-low, and low ecological risks. Local terrain features, vegetation, and climate were closely related to the “source” or “sink” landscape function of a grid. The land use type converted to artificial surface had the highest driving effects (q value) on multiple ecological risk “source-sink” landscape functions, and had a significant difference between other factors. The driving effects of land use type converted to artificial surface and road network density gradually increased with the risk level. The influences of GDP and population density gradually weakened with the level. The influence of interaction between any two factors was stronger than the influence of a single factor on ecological risk. The proposed assessment model can help to identify specific ecological risk at the grid level, and combined with the regulation strategy, the scientific basis can be provided for the regulation and management of different ecological risks.

Grid-scale demand-side flexibility services using commercial buildings lighting loads

Flexibility Services can be used to account for the high variability in electricity generation due to increasing renewable energy sources such as wind and solar energy. As buildings become smarter with the adoption of new technologies for sensing and control, more integration between buildings and the electric grid is possible. Building loads such as air conditioning and lighting in commercial buildings have the potential to provide these Flexibility Services. In commercial buildings, lighting accounts for approximately 10–15 % of the load at time. Past studies have shown that lighting can be dimmed by 15–20 % without causing visual discomfort to the occupants. Overall, this study aims to estimate the instantaneous demand reduction that can be provided due to lighting loads using prototypical building models aggregated across electric grid nodes in the Midcontinent Independent System Operator (MISO) region in the United States. Findings suggest, in a future case with 100 % LED fixtures and 30 % technology penetration for smart lighting controls, lighting loads can provide around 250 to 475 MW (0.21 to 0.39 % of MISO’s peak load) of Flexibility Services in the MISO region. The results can be used as inputs for grid levels models to predict future generation, transmission, and distribution investments for future high renewable energy scenarios.

Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System

In pursuit of widespread adoption of renewable energy and the realization of decarbonization objectives, this study investigates an innovative system known as a wind-solar-hydrogen multi-energy supply (WSH-MES) system. This system seamlessly integrates a wind farm, photovoltaic power station, solar thermal power station, and hydrogen energy network at the power grid level. Central to the study is the introduction of a bi-level collaborative optimization model—an innovative algorithmic framework specifically tailored for complex multi-energy systems. This model co-optimizes both the capacity planning of essential system components and their annual load distribution, adeptly navigating the complexities of optimizing capacity and annual load distribution under uncertain energy sources and load conditions. A layered methodology synergistically combines linear programming with an advanced version of non-dominated sorting genetic algorithm-II. When applied to a real-world case study in Zhangbei, China, this approach identifies an optimal system capacity, leading to annual green power generation of 201.56 GW and a substantial reduction of over 173,703 tons of CO2 emissions. An economic analysis further reveals that each 1% reduction in CO2 emissions corresponds to a modest 1.7% increase in the system’s levelized cost of energy. Moreover, a comprehensive exploration of the impacts of various capacity parameters on the WSH-MES system’s performance is conducted. These insights offer invaluable guidance for the large-scale advancement of efficient renewable energy utilization and the attainment of decarbonization targets.

Influence of system inertia time constant on transient stability level of AC power grid

AbstractPower system inertia is receiving increasing attention because the inclusion of renewable energy generation equipment weakens the inertia of the regional equivalent system and affects the transient stability of the system. In this paper, the influence of variation of inertia constant on the transient stability of the system is studied analytically by adding short‐circuit faults, Equal Area Criterion, and derivation of equations. On the basis of theoretical analysis, multi‐machine system is established to simulate and verify the correctness of the analysis results.

A hybrid finite element/neural network solver and its application to the Poisson problem

AbstractWe analyze a hybrid method that enriches coarse grid finite element solutions with fine scale fluctuations obtained from a neural network. The idea stems from the Deep Neural Network Multigrid Solver (DNN‐MG) which embeds a neural network into a multigrid hierarchy by solving coarse grid levels directly and predicting the corrections on fine grid levels locally (e.g., on small patches that consist of several cells) by a neural network. Such local designs are quite appealing, as they allow a very good generalizability. In this work, we formalize the method and describe main components of the a‐priori error analysis. Moreover, we numerically investigate how the size of training set affects the solution quality.

A Comprehensive Review: Mitigating Techniques for Power Variability Due to Integration of Renewable Energy Sources With Grid

Renewable energy sources like solar panels and wind turbines are becoming more important for generating power. The inconsistent and unreliable output of solar panels and wind turbines is due to factors like clouds, weather conditions, and wind speed that cause their energy production to fluctuate. Connecting intermittent sources to the utility grid creates difficulties in different technical areas, such as ensuring a steady and reliable power supply, protecting the system from disruptions, controlling the distribution of generated power, and maintaining a consistent level of power quality. In this situation, adjusting the amount of electricity produced by an intermittent energy source to keep the power grid stable is essential. This issue must be resolved as soon as possible. The extension to these locations diminishes the grid's strength. It leads to a scenario where a solar power plant is integrated into a subpar electric grid. However, integrating solar power into a shoddy AC infrastructure has problems with power quality (PQ) problems, which restricts penetration levels. This review article suggests various FACTS devices and various conventional, adaptive, and AI-based algorithms to reduce PQ issues brought on by a weak grid and increase renewable energy source (RES) penetration levels. This paper discusses the PQ issues that occur when RES like solar power plants (SPP) and wind power plants (WPP) or both as hybrid are connected to the electrical grid. It also explores different ways to reduce the fluctuation in power output from the solar panels. It analyzes the numerous power quality issues that arise with solar penetration and PQ mitigation methods using several FACTS devices and control algorithms, including traditional control, adaptive control, and AI-based control algorithms. For the benefit of engineers and academics working in this field of study, various research publications have been carefully evaluated, organized, and placed on this paper for convenient reference. It also briefly discusses the plan for controlling battery storage system for solve this problem.

Trade-off efficiency: Linking urban socioecological quality with land use efficiency from return on investment perspective

Effectively coordinating urban development and urban socioecology is crucial for achieving sustainable development goals. However, efficacious measurements and assessment frameworks in this field are still insufficient. This study proposes a novel metrics, termed trade-off efficiency, which measures the relationship between urban development and urban socioecology from a return on investment perspective. Using three major Chinese cities, Zhengzhou, Nanchang and Changsha as a case study, we initially quantified regional socioecological states, including urban heat island effect, air quality, habitat quality, and green space accessibility, at a 1 km2 grid level. We also measured regional land use efficiency at the same resolution. The super-slack-based measure model was employed to examine the trade-off efficiency. The results revealed that the regional socioecological quality, in general, exhibits an opposite spatial pattern to the land use efficiency, underscoring the practicality of quantifying the trade-off efficiency from a return on investment perspective. The trade-off efficiency, which decreases from the inside to the outside, demonstrates a similar spatial pattern as the land use efficiency, implying that urban development and urban socioecology may conform to the Environmental Kuznets Curve. The proposed framework, with its fine resolution, holds the potential to serve specific urban planning and assist decision-makers in targeting low-efficiency areas.

Detecting and classifying man-in-the-middle attacks in the private area network of smart grids

The sustainable development of smart grids requires the massive deployment of renewable energy, in a highly distributed manner, introducing new challenges for the system operation. Therefore, the integration of information and communication technologies in sites with Distributed Energy Resources (DERs) is needed to monitor and control the DERs operation. In this scheme, a local controller is installed at each DER site to interact with the centralized applications at the grid level and the power equipment at the site level. This local controller uses client–server protocols (e.g., Modbus TCP/IP and IEC 61850 Manufacturing Message Specification) to communicate with different power equipment in the Private Area Network (PAN) of the site. Such protocols often lack information confidentiality and integrity mechanisms. As a result, the smart grids become vulnerable to cyber-attacks. To safeguard smart grid applications, this paper proposes a Hybrid Network Intrusion Detection System approach (HNIDS), where machine learning-based anomaly and signature-based are combined. The proposed methodology detects and classifies Man-In-The-Middle (MITM) attacks in eavesdropping mode in PANs, without violating customer privacy. The ability to detect unknown MITM attack techniques, identify affected packets, and determine the victim device(s) are the major advantages of this approach. An experimental testbed has been used to collect real-life data and validate the effectiveness of the proposed approach in smart grid applications. The proposed HNIDS is evaluated using a simulation as well as real-life laboratory experiments, demonstrating very high accuracy in detection rate, from 97.6% to 100%, with an average of the weighted F1-score over 98%.

Terrorism and child mortality.

How does terrorism affect child mortality? We use geo-coded data on terrorism and spatially disaggregated data on child mortality to study the relationship between both variables for 52 African countries between 2000 and 2017 at the 0.5×0.5° grid level. Our estimates suggest that moderate increases in terrorism are linked to several thousand additional annual deaths of children under the age of five. A panel event-study points to economic effects that are larger and compound over time. Interrogating our data, we show that the direct impact of terrorism tends to be very small. Instead, we theorize that terrorism causes child mortality primarily by triggering adverse behavioral responses by parents, medical workers, and policymakers. We provide tentative evidence in support of this argument.