Network Planning Research Articles

Optimization Principles Applied in Planning and Operation of Active Distribution Networks

Optimization principles play an important role in the planning and operation of active distribution networks (ADNs), which are designed to handle the inrush of distributed generation (DG) resources like renewables and, nowadays, battery energy storage systems (BESSs) and electric vehicles (EVs) [...]

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Mutual Causality Between Urban Transport Superiority Degree and Urban Land Use Efficiency: Insights from County Cities in Gansu Province Under the Belt and Road Initiative

Exploring the coupled coordination and interaction between urban transport superiority degree (UTSD) and urban land use efficiency (ULUE) is the key to promoting efficient land use in cities and coordinated development. This paper adopts the improved UTSD model, super-efficiency slack-based measure–undesirable output model, coupling coordination degree model (CCDM), panel Granger causality test, random forest model, and the mixed geographically and temporally weighted regression model to reveal the spatial and temporal evolution and coupling characteristics of UTSD and ULUE in Gansu from 2005 to 2020 and to validate and explore the interaction mechanism between UTSD and ULUE. The results show that (1), from 2005 to 2020, the average UTSD in Gansu increased from 0.56 to 1.01 and the Belt and Road Initiative accelerated the construction of the transportation network in Gansu. The average ULUE increased from 0.52 to 0.62; the spatial distribution of ULUE was high in the west and north and low in the east and south. (2) From 2005 to 2020, the average CCDM of UTSD and ULUE in Gansu increased from slightly unbalanced (0.37) to slightly balanced (0.52). A spatially high UTSD and high ULUE agglomeration area can be found along the transportation arteries. (3) The UTSD and ULUE were mutually causal, with the degree of transportation arterial influence degree being the strongest driver of ULUE among the components of UTSD (30.41% contribution) and tax revenue being the strongest driver of UTSD among the components of ULUE (15.10% contribution). Overall, the connotation of ULUE puts forward the demand for improving the transportation infrastructure and, at the same time, provides the guarantee for UTSD upgrading, which in turn affects the ULUE. In the future, the Xinan region of Gansu should prioritize planning and construction of a transportation network. The results of this study can provide a scientific basis for the construction of transportation networks and the efficient use of urban land in Gansu and other regions.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Research on wireless channel model based on improved ray tracing algorithm that considers multiple diffuse scattering and employs pyramid-shaped ray tubes as the carrier

This paper extensively utilizes fine three dimensional environmental data obtained from laser point clouds. Based on theories such as geometrical optics and effective roughness theory, a deterministic wireless channel model is established, which integrates higher-order diffuse scattering. This model is referred to as the ray tracing fusion with higher-order diffuse scattering model. To expedite the collision calculation between rays and the scene, this paper introduces a combined approach of voxelization and signed distance field, resulting in a remarkable 16-fold improvement in computational speed. Moreover, aiming to balance accuracy and efficiency, the paper systematically analyzes the optimization computation parameters of the model. Finally, the proposed model is validated using measurement data in the frequency range of 1 GHz to 6 GHz in mountainous terrain. The results indicate that the predicted outcomes of the proposed model have an accuracy within 6 dB compared to the measurement results, and are superior to ITU-R P.1546, which is an international standard recommended by the International Telecommunication Union for modeling electromagnetic wave propagation in undulating terrain. This provides necessary technical support for network planning and optimization.

Aircraft human‐machine interaction assistant design: A novel multimodal data processing and application framework

AbstractDuring aircraft operations, pilots rely on human‐machine interaction platforms to access essential information services. However, the development of a highly usable aerial assistant necessitates the incorporation of two interaction modes: active‐command and passive‐response modes, along with three input modes: voice inputs, situation inputs, and plan inputs. This research focuses on the design of an aircraft human‐machine interaction assistant (AHMIA), which serves as a multimodal data processing and application framework for human‐to‐machine interaction in a fully voice‐controlled manner. For the voice mode, a finetuned FunASR model is employed, leveraging private aeronautical datasets to enable specific aeronautical speech recognition. For the situation mode, a hierarchical situation events extraction model is proposed, facilitating the utilization of high‐level situational features. For the plan mode, a multi‐formations double‐code network plan diagram with a timeline is utilized to effectively represent plan information. Notably, to bridge the gap between human language and machine language, a hierarchical knowledge engine named process‐event‐condition‐order‐skill (PECOS) is introduced. PECOS provides three distinct products: the PECOS model, the PECOS state chart, and the PECOS knowledge description. Simulation results within the air confrontation scenario demonstrate that AHMIA enables active‐command and passive‐response interactions with pilots, thereby enhancing the overall interaction modality.

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development: A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization. Urban integration is an emerging trend in metropolitan areas. This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development. Rail transit in metropolitan areas offers both technical and economic advantages, improving the efficiency of time and space resource utilization, fostering economic cooperation, and ultimately contributing to an integrated development model. However, the integration of rail transit networks faces several challenges, including road network planning, technical standards, and operational organization. Using the Wuhan metropolitan area as a case study, this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Data model to enable multidimensional process mining for data farming based value stream planning in production networks

AbstractInternational industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.

Multi-Stage Rolling Grid Expansion Planning for Distribution Networks Considering Conditional Value at Risk

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users’ electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users’ electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account.

Offshore grid planning considering restricted areas: An evolution game approach

In this paper, offshore wind farm cluster transmission network planning is examined in depth, based on the finite rational evolutionary game method. In the actual project, the game scenarios of offshore wind farm developers and transmission system operators and their respective interests are considered, while cable laying exclusion zones owing to the submarine environment are fully taken into account. Through simulation experiments, the significant effect of the proposed method in improving grid adaptability and interference immunity is verified. The results demonstrate that the proposed finite rational evolutionary game method increases the return on investment by 1.83 % compared to the traditional method, while the payoff ratio of the game participants is 12.81 % lower. In addition, the method excels in ensuring the feasibility of project implementation by successfully balancing the interests between the OWF developer and the power transmission system operator. This offers a new perspective for efficient planning of transmission networks for offshore wind farms that can better cope with complex engineering environments and balance the interests of all parties.

Construction, assessment, and protection of green infrastructure networks from a dynamic perspective: A case study of Dalian City, Liaoning Province, China

The green infrastructure (GI) network, an important nature-based solution (NBS) strategy, is pivotal for sustainable urban development. However, current research perspectives focus on constructing a static GI network, and research on evaluating and protecting GI networks in the context of spatiotemporal changes has been limited. This research aims to comprehensively characterise the spatio-temporal changes in the GI network and protect its efficiency. We selected Dalian City as a case study and applied the Future Land-use Simulation (FLUS) model to predict its land use for 2030 and 2040. Based on predicted and historical land-use data, the 1990–2040 GI network of Dalian City was constructed utilising the Integrated valuation of ecosystem services and trade-offs (InVEST), morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR) model, while its spatiotemporal changes were evaluated. The results revealed that the hub area exhibited positive growth over time, whereas the link lengths showed an opposite trend. Furthermore, the overall position of the GI network shifted toward the southwest. The flatness indicator detected that the direction of the GI network shape gradually became less pronounced. Consequently, the structure of the GI network shifted from multi-centre to mono-centre. Additionally, priority protection for hubs is mainly in the northeast, while links are primarily in the coastal regions. The urgency of protection for both increases over time. This research developed a dynamic GI network construction and assessment method, providing a scientific basis and reference for future rational GI network planning, ecological protection planning, and related land policy formulation.

Specialized Genetic Operators for the Planning of Passive Optical Networks

Specialized Genetic Operators for the Planning of Passive Optical Networks

Large Language Models Meet Next-Generation Networking Technologies: A Review

The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data

Green technologies such as solar photovoltaic systems and electric vehicles play a fundamental role in the global decarbonization effort. To enable their diffusion, electricity distribution networks need to be upgraded, which is a complex and expensive endeavor. However, utilities face budgetary constraints and seek to reduce planning uncertainty. Here, we utilize 7,527 real-world grid topologies and land cover data to develop a model for estimating conductor and capital investment needs for electrifying a specific area. Our work yields three main contributions: First, we demonstrate the important role of land cover data in power line planning. We show that, for medium and large networks, distinct methodologies are needed due to the significant impact of land cover, particularly buildings and roads. Second, we introduce a parsimonious model of power line length and identify the number of consumption points as the primary determinant of network investment costs. Third, we present a cost assessment model tailored for regulators and investors, offering valuable insights for network planning, policymaking, due diligence, and research. Our work highlights the importance of combining land cover data and operations research algorithms in distribution network planning and provides policymakers with a tool to ensure cost-efficient network expansion.

Empowering super-stars or rising-stars? Dynamics of the high-speed rail network planning in China

ABSTRACT The development of high-speed rail (HSR) systems as a critical component of the global 'infrastructure turn' is deeply embedded in regional socioeconomic contexts. Drawing on advances in actor-oriented network modelling and longitudinal inter-city HSR data in China during 2008-2022, this study explores how the evolution of the HSR network is dynamically and differentially underpinned by attribute-based effects of socioeconomic scale and growth rate to balance the short-term efficiency and long-term equity of nationwide development. After controlling for structure-based effects (transitive triads and balance) as well as attribute-based effects related to geographic, natural and cultural contexts, the results provide evidence that while the scale and the growth rate of economy and population both positively impact HSR link formation, the impacts of scale-related effects significantly diminish over time whereas the impacts of growth-rate-related effects exhibit a significant strengthening trend. The results further illustrate that while the scale-related and growth-rate-related effects of economy function as substitutes in driving HSR link formation, the two corresponding effects of population complement each other, providing evidence for the heterogeneous roles of economic and social contexts in underpinning HSR network planning. The findings provide implications for more effective and equitable regional socioeconomic development through strategic HSR network planning.

Improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

Study on the planning and scheduling strategies of the hydrogen supply network for road transportation in deep decarbonization scenarios

As the energy decarbonization continues to advance, hydrogen is gaining momentum. With the increasing demand for hydrogen and the lack of hydrogen infrastructure, it is urgent to solve the problem of hydrogen supply network layout planning. In order to reduce redundant construction of hydrogen supply network infrastructure and increase the rationality of its layout, this paper conducts research on the optimal layout planning of hydrogen supply network infrastructure in the transportation sector. By constructing a two-stage real-time scheduling model of hydrogen supply network, the unit cost composition of hydrogen supply network is more complete and accurate, and the location, technology type and quantity of each link of hydrogen supply network are obtained. The results show that the unit cost of China's hydrogen supply network is 27.8 CNY/kg in 2035 and 19.04 CNY/kg in 2050 under the baseline scenario. Compared with the results of existing literature, the two-stage real-time scheduling model can reduce by 7 % of the hydrogen energy supply network unit cost by decreasing the transportation vehicles and the investment capacity of fixed hydrogen storage equipment. The proposed method of the hydrogen supply network planning can provide policy-makers with the selection of sustainability pathways for dynamic hydrogen infrastructure development planning.

Applying Ant Colony Optimization to Reduce Tram Journey Times

Nature-inspired algorithms allow us to solve many problems related to the search for optimal solutions. One such issue is the problem of searching for optimal routes. In this paper, ant colony optimization is used to search for optimal tram routes. Ant colony optimization is a method inspired by the behavior of ants in nature, which as a group are able to successfully find optimal routes from the nest to food. The aim of this paper is to present a practical application of the algorithm as a tool for public transport network planning. In urban public transport, travel time is crucial. It is a major factor in passengers’ choice of transport mode. Therefore, in this paper, the objective function determining the operation of the algorithm is driving time. Scheduled time, real time and theoretical time are analyzed and compared. The routes are then compared with each other in order to select the optimal solution. A case study involving one of the largest tramway networks in Poland demonstrates the effectiveness of the nature-inspired algorithm. The obtained results allow route optimization by selecting the route with the shortest travel time. Thus, the development of the entire network is also possible. In addition, due to its versatility, the method can be applied to various modes of transport.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.