Grid Pattern Research Articles

Optimizing Structural Patterns for 3D Electrodes in Lithium-Ion Batteries for Enhanced Fast-Charging Capability and Reduced Lithium Plating

The most common pattern types for anode structuring, in particular the line, grid, and hexagonal-arranged hole pattern were evaluated in a comparable setup in full-cells and symmetrical cells. The cells with structured electrodes were compared to reference cells with unstructured anodes of similar areal capacity (4.3 mAh cm−2) and the onset of lithium plating during fast-charging was determined in situ by differential voltage analysis of the voltage relaxation and ex situ by post-mortem analysis. Furthermore, electrochemical impedance spectroscopy measurements on symmetrical cells were used to determine the ionic resistance of structured and unstructured electrodes of similar areal capacity. All cells with structured electrodes showed lower ionic resistances and an onset of lithium plating shifted to higher C-rates compared to cells with unstructured electrodes. The structure patterns with capillary structures, i.e., lines and grids, showed significant reduced lithium plating during fast-charging and a higher rate capability compared to reference cells with unstructured electrodes and cells with hole structured electrodes. The continuous rewetting of the electrode with liquid electrolyte by capillary forces and the reduced ionic resistance of the 3D electrode are identified as key factors in improving overall battery performance. The data of the studied cells were used to calculate the resulting energy and power densities of prospective commercial pouch cells and potential pitfalls in the comparison to cells with unstructured electrodes were identified.

Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.

Cluster partition-based two-layer expansion planning of grid–resource–storage for distribution networks

In order to realize the optimal planning of grid–resource–storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning.

Current and future development of thermal grids in Switzerland: an organizational perspective

IntroductionThermal grids are key to decarbonizing heating and cooling. However, their development is a complex socio-technical process. This study aims to (1) understand the thermal grid ecosystem’s development under changing political, economic and cultural frame conditions, (2) elicit actors’ perception of options for future development, and (3) identify concrete problem situations for the future development of thermal grids and describe how to address them.MethodsWe draw on 18 semi-structured interviews with decision-makers in utilities, municipal administrations, planning and engineering companies and intermediary organizations.ResultsWith increased strategic importance of thermal grids, the ecosystem has been in a phase of rapid growth and professionalization in the last 10–15 years. This is reflected in a rapid increase in the number of grids, but also in capability and capacity building and greater quality standards. This transformation has been supported by a resource pool (e.g., knowledge base, capacities and capabilities, networks or decision-support tools), built partly through the value network’s activities and partly through purposeful orchestration by associations, authorities, utilities and other actors. While policy developments, energy price fluctuations and changes in customer perceptions spurred demand, thermal grid development is limited by supply-side constraints, especially a skill shortage. Four interrelated problem situations were identified: (1) Municipalities lack the resources to manage the implementation of their energy strategies. (2) The modernization of thermal grids and integration of low-carbon heat sources entails high transaction costs. (3) The implementation of thermal grids is subject to delays and quality risks, with consequences for the timely achievement of decarbonization goals. (4) Customers face uncertainty on available options, as well as on the systemic impacts of their decisions.DiscussionBased on current practices, suggestions by the interviewees, as well as a comparison with the international literature, we identify four development areas to address these problem situations: (1) Empower and support municipalities. (2) Manage the scarcity of qualified workforce. (3) Leverage digital technology for the planning, realization and operation of thermal grids. (4) Pilot technological and organizational innovations together. This complements the set of previously suggested measures to support the development of thermal grids, which focused strongly on technical and regulatory aspects.

Thermo‐mechanical behavior of ball grid packages with different solder ball grid patterns investigated through moiré interferometry

AbstractPlastic ball‐grid array (PBGA) package assembly comprises various materials with different thermo‐mechanical properties, and the solder ball‐grid patterns can significantly influence the package deformation and solder ball stress. This study utilizes moiré interferometry to investigate the deformation behavior of wire bonded plastic ball‐grid array (WB‐PBGA) packages under varying temperatures. Real‐time moiré interferometry experiments were conducted to capture fringe patterns representing package deformation at different temperature stages. Experiments were conducted for three different wire bonded plastic ball‐grid array packages with distinct solder ball array configurations: full grid patterns (WB‐PBGA‐FG) and perimeter patterns with/without central connections (WB‐PBGA‐PC/P). The analysis revealed considerable variations in thermal strain distribution and positioning of the critical solder ball within the package assembly, depending on the solder ball‐grid pattern. Among these wire bonded plastic ball‐grid array packages, WB‐PBGA‐P/C exhibited the most substantial bending deformation, experiencing the highest effective strain, which directly impacts solder ball failure. These findings emphasize the importance of solder ball‐grid patterns in understanding the deformation behavior and reliability of WB‐PBGA packages under varying temperature conditions.

Regulatory Implications for considering flexible resources in network expansion planning: main lessons from regional cases in the H2020 FlexPlan project

The FlexPlan Horizon2020 project aimed at establishing a new grid planning methodology which considers the opportunity to introduce new storage and flexibility resources in electricity transmission and distribution grids as an alternative to building new grid elements, in accordance with the intentions of the European Commission regulatory package "Clean Energy for all Europeans". FlexPlan creates a new innovative grid planning tool which is intended to go beyond the state of the art of planning methodologies by including the following innovative features: assessment of best planning strategy by analysing in one shot a high number of candidate expansion options provided by a pre-processor tool, simultaneous mid- and long-term planning assessment over three grid years (2030-2040-2050), incorporation of full range of cost benefit analysis criteria into the target function, integrated transmission distribution planning, embedded environmental analysis (air quality, carbon footprint, landscape constraints), probabilistic contingency methodologies in replacement of the traditional N-1 criterion, application of numerical decomposition techniques to reduce calculation efforts and analysis of variability of yearly RES and load time series through a Monte Carlo process. Six regional cases covering nearly the whole European continent are developed in order to cast a view on grid planning in Europe till 2050. The final step in FlexPlan was formulating guidelines for regulators and planning offices of system operators, by indicating to what extent system flexibility can contribute to reduce overall system costs (operational and investment) yet maintaining current system security levels and which regulatory provisions could foster such process. This paper focuses on the regulatory issues, which were uncovered during the initiation phase and of the project and refined in throughout six regional cases. In order to substantiate this, the paper explains in brief the developed and applied FlexPlan methodology and its testing in six regional cases.

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

DWT-BILSTM-based models for day-ahead hourly global horizontal solar irradiance forecasting

Accurate forecasting of electricity generation from renewable energy sources is crucial for the operation, planning and management of smart grids. For reliable planning and operation of photovoltaic (PV) systems in grid-connected or islanded utilities, an hourly day-ahead forecast of PV output is critical. The forecast of PV power can be done indirectly by estimating solar irradiance. For forecasting day-ahead hourly global horizontal irradiance (GHI), two forecasting models with different multivariate inputs are proposed in this paper, and the results are compared. These models use a hybrid algorithm of discrete wavelet decomposition and bidirectional long short-term memory (BILSTM). The inputs of the first model contain GHI and weather type data. The other model allows for observation of the effect of meteorological values including GHI, temperature, humidity, wind speed, and weather type data. The forecasting performance of deep learning algorithms which contain recurrent neural network (RNN), long short-term memory (LSTM), and BILSTM algorithms for day ahead hourly solar irradiance forecasting problems are also compared. To evaluate the performance of proposed models, two datasets are used for Model 1 and one dataset is used for Model 2. An experiment is also done to demonstrate that the proposed Model 1 is applicable in datasets collected in the vicinity of the city of Trabzon. On the other hand, BILSTM algorithm outperforms RNN and LSTM algorithms. It is seen that the test successes of both proposed models are better than the results given in the literature.

Optimizing Cattle Behavior Analysis in Precision Livestock Farming: Integrating YOLOv7-E6E with AutoAugment and GridMask to Enhance Detection Accuracy

Recently, the growing demand for meat has increased interest in precision livestock farming (PLF), wherein monitoring livestock behavior is crucial for assessing animal health. We introduce a novel cattle behavior detection model that leverages data from 2D RGB cameras. It primarily employs you only look once (YOLO)v7-E6E, which is a real-time object detection framework renowned for its efficiency across various applications. Notably, the proposed model enhances network performance without incurring additional inference costs. We primarily focused on performance enhancement and evaluation of the model by integrating AutoAugment and GridMask to augment the original dataset. AutoAugment, a reinforcement learning algorithm, was employed to determine the most effective data augmentation policy. Concurrently, we applied GridMask, a novel data augmentation technique that systematically eliminates square regions in a grid pattern to improve model robustness. Our results revealed that when trained on the original dataset, the model achieved a mean average precision (mAP) of 88.2%, which increased by 2.9% after applying AutoAugment. The performance was further improved by combining AutoAugment and GridMask, resulting in a notable 4.8% increase in the mAP, thereby achieving a final mAP of 93.0%. This demonstrates the efficacy of these augmentation strategies in improving cattle behavior detection for PLF.

Enhancing hourly electricity forecasting using fuzzy cognitive maps with sample entropy

Accurate electricity consumption forecasting is the crucial to ensure the stable operation of the power system and optimize the smart grid dispatch. However, existing methods may not effectively capture complex potential features and change patterns, and are susceptible to noise, leading to deficiencies such as suboptimal electricity consumption forecasting accuracy, poor stability, and weak generalization capabilities. To fill this gap, a high order fuzzy cognitive map (FCM) forecasting method based on sample entropy and minimax concave penalty (MCP) regularization is proposed. Firstly, two mode decomposition methods are innovatively fused by utilizing sample entropy to extract the complex features of electricity consumption data, then a high order FCM model with powerful system state prediction capability and interpretable knowledge representation is constructed, and finally a FCM learning method based on MCP regularization is proposed. The forecasting method has the merits of low forecasting error, high stability and strong generalization capabilities. Comprehensive experimental results on multiple electricity consumption datasets show that the proposed method achieves optimal performance compared to 10 state-of-the-art baseline methods, demonstrating effectiveness in the task of electricity consumption forecasting, which contributes to more efficient energy management and cost savings, and provides a reliable tool for smart grid planning and management.

IoT CONTROLLED LED NOTICE BOARD

The IoT Controlled LED sign board design represents a slice- edge bid aimed at revolutionizing display results for a multitude of purposes including advertising, information dispersion, and aesthetic improvement. At its core, the sign board features an intricate array of LEDs strictly arranged in a grid pattern, each strictly orchestrated by a central microcontroller unit( MCU) harmoniously integrated with a Bluetooth module for flawless wireless communication. This community enables druggies to apply their smartphones or any Bluetooth- enabled bias as important remote controls, decreeing the content showcased on the LED board with unknown ease and perfection. Through a devoted mobile operation, druggies gain a gateway to a realm of creative expression, empowered to transmit a myriad of content types ranging from brief textbook dispatches to witching, robustness and images for immediate display. specially, the system's hallmark lies in its capability to grease real- time content operation, preventing the need for physical intervention and fostering dynamic rigidity to evolving demands and scripts. likewise, the mobile operation's robust point set extends beyond bare content transmission, offering sophisticated tools for communication scheduling and contemporaneous operation of multiple sign boards, thereby accelerating scalability and usability across different surroundings similar as bustling retail spaces, bulging transportation capitals, and bustling public spaces likewise. With its intimately designed interface and multifaceted functionality, the IoT Controlled LED sign board design emerges as a lamp of invention, promising to review the veritably geography of information dispersion and visual communication in the digital age. KEYWORDS: IoT Controlled LED subscribe Board, Microcontroller Unit( MCU), Wireless Communication, Mobile Application, Management, Remote Control.

Workflow of a Preclinical Robotic Magnetic Resonance Imaging-guided Focused Ultrasound Body System

Abstract Background: Establishing an efficient workflow is crucial for the success of magnetic resonance-guided focused ultrasound (MRgFUS) procedures. The current study provides a comprehensive description of the workflow of a customized MRgFUS robotic body device for preclinical use and accompanied software through experiments in excised porcine tissue. Methods: The employed system comprises a single-element spherically focused transducer of 2.6 MHz that can be moved along four PC-controlled axes. A detailed description of essential software functionalities and its integration with a 3T Siemens magnetic resonance imaging (MRI) scanner through Access-I for interactive remote control of the scanner and real-time access to imaging data is provided. Following treatment planning on preoperative MR images, porcine tissue samples were sonicated in rectangular and irregular grid patterns with varying ultrasonic parameters and spatial step under software-based monitoring. Results: MRgFUS ablations of ex vivo porcine tissue were successfully performed utilizing a multimodal monitoring approach combining MRI-based temperature, thermal dose, and necrotic area mapping, thus demonstrating an efficient procedural workflow. The simulated necrotic regions were in excellent agreement with the actual lesions revealed upon tissue dissection and highly consistent with the planned sonication patterns. The software’s ability to accurately identify regions where necrosis did not occur and indicate to the user the specific points to be re-sonicated was demonstrated. Conclusion: Overall, the study highlights critical aspects in accurately planning and executing preclinical MRgFUS protocols within an efficient workflow. The provided data could serve as the basis for other researchers in the field.

Development of eutectic gallium-indium-based transparent conductive electrodes on flexible substrates for touch sensor integration

In this study, the adaptability of liquid metal (LM)-based transparent conductive electrodes (TCEs) to flexible substrates and their potential applications in various electronics fields were investigated. To achieve this, conventional microfabrication techniques were employed to pattern polydimethylsiloxane (PDMS) channels and introduce an Eutectic Gallium-Indium (EGaIn) LM with the aim of enhancing transmittance and reducing sheet resistance. Microfluidic channel structures with varying pitch lengths and widths were fabricated in the types of grid patterns, labeled Pattern A (2000 μm pitch, 40 μm width), Pattern B (2000 μm pitch, 80 μm width), Pattern C (200 μm pitch, 40 μm width), and Pattern D (200 μm pitch, 80 μm width. The corresponding average transmittance values for the EGaIn LM-based TCE were 90 %, 71.8 %, 61.8 %, and 43.2 % for Patterns A, B, C, and D, respectively. To illustrate the potential in touch sensor applications, resistance changes in Patterns A, B, C, and D were assessed under applied forces ranging from 0 N to 150 N, revealing resistance changes of 0.0265 Ω, 0.03617 Ω, 0.03977 Ω, and 0.11629 Ω, respectively.

Genetic Algorithm-Driven Optimization of Pattern for Parametric Facade Design Based on Support Position Data to Increase Visual Quality

One of the most critical challenges for architects in façade design is providing an effective view from the indoors to the outdoors of a building for users, although the main role of the parametric façade is covering openings to control daylight and temperature. This study uses a genetic algorithm to optimize and evaluate the number and place of nodes and the position of supports required for a parametric façade based on the geometric patterns. Using the dataset with genetic algorithms is effective in reducing or replacing the nodes and supports of the façade. It also creates broader and irregular patterns just around the windows, which decreases the visual disturbance experienced by occupants. Accordingly, optimal building facade operation in terms of both building aesthetics and performance is important. The method used in this study, validated through three geometric grid patterns based on node positions, can be used to analyze dataset-incorporated patterns for potential irregular façade extensions. The nodes are considered by analyzing the cross-section optimization using the Galapagos program, and then data are obtained with Karamba based on reaction force, node force, and the deformation energy. The results show that among the three grid patterns, i.e., triangular, square, and hexagonal, the hexagonal grid is most efficient, exhibiting up to 60% lower reaction force, 40% lower node force, and 30% less deformation energy than the square grid pattern. The proposed GA also shows its effectiveness in enhancing the performance of parametric façades with patterns, thereby improving the occupants’ visual experience.

A state‐space average model of a three‐level PV inverter for transient short‐circuit currents analysis

AbstractThis paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning.

Moth Flame Algorithm to Optimize the Size and Location of Energy Storage Units in a Radial Distribution Network to mitigate Power Loss

The ideal placements and capacities for Battery Energy Storage Systems (BESS) sources in a distributed generation (DG) environment have an impact on power losses in the distribution network. Therefore, engineers are quite concerned with DG and BESS. The output from DG is, however, typically unpredictable and inconsistent. The secure and stable functioning of the power system will be impacted to varying degrees when it is connected to various sites, capabilities, and power grids. To provide a safer, more stable, more dependable, as well as more effective power grid operation, power grid planners must take the impact of capacity, type, and location into account while choosing the best BESS accessibility method. The objective is to choose bus stops with the right potential and minimal loss. This study demonstrates how to use a Moth Flame Optimization Algorithm (MFOA) to establish the appropriate battery energy storage system (BESS) sizing in a hybrid solar and wind energy conversion system (WECS) generation integrated in a radial distribution network. This research investigates the optimal capacities and allotment of the BESSs within a 33-bus and 69-bus radial network of distribution for the maximum power dissipation reduction. This research also provides a framework for carrying out conservation voltage reduction (CVR) when distributed energy resources like solar photovoltaic (PV) systems, WECS, and energy storage systems (ESSs) are connected into distribution networks. The simulation has been conducted using Matlab code and the suggested method's output is compared with Firefly Algorithm (FA), Genetic Algorithm (GA) and Ant Colony Algorithms (ACA) and evaluated against other results.

Establishing a Mining Group of Deposit and an Exploration Grid Pattern for Uranium Ore Intercalated in Sandstone at Pa Lua - Pa Rong Area, Nong Son Basin

This research shows the results of establishing a mine group and a uranium ore exploration network in Nong Son depression sandstone, Quang Nam province based on the results of exploration of uranium ore bodies in the Pa Lua - Pa Rong area. Some main findings include: i) The uranium content in most industrial ore bodies can be attributed to a lognormal distribution function. The ore bodies (two main ore bodies are referred to as TQ1 and TQ2) have an average uranium concentration from 0,032% to 0,054% U3O8 with high variations (with the amount of variation in content (Vc) greater than 100%). The average thickness of the ore bodies is from 2,30 meters to 2,35 meters. The thickness variation coefficient (Vm) varies from 89,4% to 100,3%, indicating the unstable to very unstable type of ore body; and ii) Uranium mines in sandstones of the Pa Lua - Pa Rong type, Nong Son basin mainly belong to exploration mine group III. The most reasonable exploration approach is to use a parallel and fan-shaped network. To calculate the reserves, the distance between drilling routes is set at 50 to 60 meters. To determine the road and pavement, wet set the drilling routes to be 25 to 30 meters apart from each other. This exploration network can be applied to other sandstone mines in the Nong Son basin with similar characteristics to the Pa Lua - Pa Rong mine.
 
 

Assessment of Large Span Multistory Building Using Various Slab Under Earthquake Response

With the growing need for multistory buildings in a variety of industries, the need for novel structural solutions has become critical. This research looks at the structural behaviour of wide span structures, with an emphasis on the use of various slab forms to meet the problems provided by growing architectural trends. The study undertakes a detailed literature analysis, looking into the use of Grid Slabs, Flat Slabs, Waffle Slabs, and Ribbed Slabs alongside regular slabs to improve stiffness and load-carrying capability in large-span buildings. Architects are challenged to accommodate bigger spans while minimising the number of columns, which has led to the introduction of novel slab layouts. Grid Slabs, which have unique grid patterns, and well-established Flat Slabs are popular alternatives. The analysis of the literature reveals an increasing trend among academics who have effectively used various slab designs to meet the issues given by long spans. The findings provide architects, structural engineers, and construction industry stakeholders significant insights into the performance features and advantages associated with each slab type. This study adds to the continuing discussion about optimising structural designs for large-span structures to meet the changing demands of current construction practises. The complete analysis is a helpful resource for professionals involved in multistory building design and construction, assisting in informed decision-making and supporting developments in the field.

Grid Planning Method of Medium Voltage Distribution Network Including Distributed Power Sources based on Timing Characteristics

Aiming at the problems of unbalanced load distribution of feeders and low utilization rate of system resources in existing medium voltage(MV) distribution network planning, it is difficult to meet the requirements of future grid construction, Propose a grid planning method of MV distribution network including distributed generator(DG) based on timing characteristics. Firstly, based on the existing grid planning achievements and temporal characteristics analysis, the grid structure and planning strategy of the MV distribution network are formed. Secondly, based on the network planning strategy, establish a dual level planning model for both internal and external distribution networks. The inner model uses comprehensive performance indicators as fitness functions to solve the optimal division of the distribution network grid, while the outer model calculates the distributed power capacity configuration of each grid with the goal of maximizing the consumption of DG. Then, based on the established dual layer planning model for the distribution network, genetic algorithms and self programming algorithms are used to solve the model by interconnecting the internal and external layers. Finally, the practicality and effectiveness of the proposed model and method were verified through practical examples combined with evaluation indicators.

Vertex Spans of Multilayered Cycle and Path Graphs

In this paper, we observe a special class of graphs known as multilayered graphs and their subclasses, namely multilayered cycles and multilayered paths. These graphs model layouts of shopping malls, city street grids, and even resemble the topology of certain famous board games. We analyze the values of all vertex spans (strong, direct, and Cartesian span) for these subclasses of graphs. Surprisingly, our results for multilayered cycles reveal that, regardless of the chosen movement rules, the span values depend solely on the length of the individual cycles, rather than the number of layers. This finding carries significant implications for the application of graph spans in maintaining safety distances.