Network Planning Research Articles

Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices

Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices

Mobile User Traffic Generation via Multi-Scale Hierarchical GAN

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

Prediction of ATFM impact for individual flights: A machine learning approach

Air Traffic Flow Management regulations consist of issuing pre-departure ground delays to deal with demand-capacity imbalances in the airspace, smoothing out the demand at congested infrastructures. This results in discrepancies between the initially planned and executed (regulated) flights, negatively impacting operational efficiency and economic performance. To mitigate downstream effects, airspace users need to assess the severity and impact of these regulations when defining their operational plan. It is crucial to anticipate these potential network issues and plan for mitigation measures during the pre-tactical phase (the day before operations (D−1)). This article presents four independent machine learning models to estimate the impact of Air Traffic Flow Management regulations for individual flights during this pre-tactical phase. An integrated view is proposed to combine the results of the different models, displaying the appropriate level of information. Two models are designed to provide information about the probability of a given flight being affected by Air Traffic Flow Management regulations and the reason for this regulation (airport or airspace congestion). These models have reported accuracy levels between 82% and 87%. The remaining models estimate the impact of the delay (amount of Air Traffic Flow Management delay issued to the flight if regulated), with a Mean Absolute Error of 9.5 min when predicting this amount of delay. The SHapely Additive exPlanations analysis is used to identify the most important factors for detecting Air Traffic Flow Management regulations for individual flights during the pre-tactical phase from a data-driven perspective. An integrated view is proposed to create a system which combines the results of the four different models, displaying the appropriate level of information and avoiding an overload of information. Finally, the results are compared against models considering only static or post-operational data.

The planning method of new energy distribution network in plateau area based on local accommodation

Abstract The planning method of new energy distribution network in plateau area based on local accommodation is studied to improve the local accommodation capacity of new energy distribution network in plateau area. The framework of new energy distribution network planning in the plateau area is constructed, and the distribution network equipment suitable for the plateau environment is selected based on the harsh environment in the plateau area; in the framework of new energy distribution network planning in plateau area, a bi-level planning model considering multi-flexible resource coordinated scheduling and energy storage is established. The upper level planning model takes the maximum accommodation of new energy as the objective, and the lower level planning model takes the optimal daily operation benefit of energy storage under the given configuration as the optimization objective; the probabilistic power flow method and C-PSO are used to solve the bi-level model. The experiment shows that this method ensures the stability of the new energy distribution network in the plateau area, reduces the amount of abandoned wind energy and the operating cost, and improves the local accommodation capacity of the core higher than the distribution network.

Economic Evaluation for 5G Planning of a Distribution Network Considering Network Coupling and Important Node Identification

Economic Evaluation for 5G Planning of a Distribution Network Considering Network Coupling and Important Node Identification

Cyber-Physical Integrated Planning of Distribution Networks Considering Spatial-Temporal Flexible Resources

Cyber-Physical Integrated Planning of Distribution Networks Considering Spatial-Temporal Flexible Resources

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Urban inundation mapping by coupling 1D − 2D models and model comparison

Urban inundation mapping is important for early warning, management, and drainage network planning. Urban flood simulations typically utilize a 1D model focused on pipe nodes, whereas 2D simulations are required over a land area. In this paper, our focus is on their coupling, so that a dynamic 1D-2D model is obtained. We couple the Storm Water Management Model (SWMM), a widely utilized urban drainage model, with 2D models to simulate urban inundation. The 1D-2D coupled inundation model uses the 1D model output, which represents the overflow volume of sparsely distributed pipe nodes. After coupling, surface inundation distribution maps are obtained. We developed a physically-based 2D overflow diffusion model (2D-DM) and compared it with two geostatistical models: Ordinary co-kriging (OCK) and Regression Kriging (RK). The study area is the central area of Haining City in China, where a severe storm occurred on August 3, 2018. The 2D-DM performed best (RMSE = 0.232), followed by RK (RMSE = 0.286), and OCK (RMSE = 0.361), but did not provide uncertainty measures, in contrast to the geostatistical models. We conclude that 1D-2D coupling was done successfully, with the different 2D models showing different qualities. To enhance urban inundation mapping, it is suggested to couple the 1D model with both physical and statistically based 2D models for a more comprehensive assessment of potential inundation areas and depths.

Kajian Alternatif Proyeksi Jaringan Jalan Hutan untuk Perencanaan Pemanenan Kayu di PT Inhutani I Labanan, Propinsi Jawa Barat

Forests are one of the natural resources that can be utilized in the form of both wood and non-wood forest products. Harvesting carried out to extract wood results in negative impacts such as soil damage and damage to standing stands. The negative impacts of wood harvesting can be reduced by planning in advance, such as creating a forest road network. The research was carried out in the mahogany room, utilization division, forest management department, while the assessment of the forest road network used the case study of PT Inhutani I Lebanan. The aim of the research is to examine the planning and selection of the best alternative forest road network for harvesting wood using multi-criteria decision making techniques. The research will be carried out in March 2023. The research stage begins with sketching the road network; assess road networks using economic, ecological and technical criteria; as well as conducting literature studies from various sources. The research results show that there are six alternative forest road networks for harvesting wood. This is based on forest road network assessment parameters that the best alternative road network in the PT Inhutani I Labanan road scheme is the 6th road network alternative. Sustainable management of forest resources can be achieved through a well-planned road network. The highest total utility value (rank 1) is found on the 6th road network which is the best alternative forest road network to be implemented at PT Inhutani Labanan I both in terms of technical aspects, ecological aspects and economic aspects.

A techno-economic model for flexibility-oriented planning of renewable-based power systems considering integrated features of generator, network, and energy storage systems

The transition towards renewable energy sources in power networks introduces challenges characterized by significant fluctuations and uncertainties. Addressing the need to identify factors influencing flexibility planning, this study presents a novel model for Adapting Flexible Resources with Renewable Energy Resources (AFR-RER). This model is designed to capture the complex interactions of generator-grid-storage flexibility, incorporating components such as Flexible AC Transmission Systems (FACTS), tie lines, multipath adjustable units, network structure adjustments, and energy storage. The AFR-RER model is specifically developed to address the challenges arising from varying levels of renewable energy integration at different stages of carbon development. Its adaptability to accommodate diverse integration levels stands as a key feature. Furthermore, custom algorithms are devised to tackle the specific challenges posed by the AFR-RER model. To demonstrate the model's effectiveness, extensive case studies are conducted, evaluating its performance on both a modified IEEE two regional 24-test system and a real-world test system. The results of these case studies validate the resilience and practicality of the proposed AFR-RER model across various scenarios, highlighting its potential as a valuable tool for optimizing flexibility planning in power networks.

Construction of ecological network in Qujing city based on MSPA and MCR models.

With the rapid advancement of urbanization and industrialization, ecological patches within cities and towns are fragmented and ecological corridors are cut off, regional ecological security is threatened and sustainable development is hindered. Building an ecological network that conforms to regional realities can connect fragmented patches, protect biodiversity and regional characteristics, and provide scientific reference for regional ecological protection and ecological network planning. By taking Qilin District, the main urban area of Qujing City as an example, and using geospatial data as the main data source, based on morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR), this study identified ecological source areas, extracted ecological corridors, and build & optimize ecological networks. (1) All landscape types are identified based on MSPA, the proportion of core area was the highest among all landscape types, which was 80.69%, combined with the connectivity evaluation, 14 important ecological source areas were selected. (2) 91 potential ecological corridors were extracted through MCR and gravity models, there were 16 important ones. (3) The network connectivity analysis method is used to calculate the α, β, and γ indexes of the ecological network before optimization, which were 2.36, 6.5, and 2.53, while after optimization, α, β and γ indices were 3.8, 9.5 and 3.5, respectively. The combined application of MSPA-MCR model and ecological network connectivity analysis evaluation is conducive to improving the structure and functionality of ecological network.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

A Comprehensive Assessment of Forest Transport Network Planning Taking into Account the Project's Technical, Economic, Environmental, and Social Aspects

A Comprehensive Assessment of Forest Transport Network Planning Taking into Account the Project's Technical, Economic, Environmental, and Social Aspects

Multistage charging facility planning on the expressway coordinated with the power structure transformation

AbstractThis study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits and rationalization of facility utilization is developed: (i) EV‐expanded network, (ii) multiagent‐based dynamic traffic assignment (MA‐DTA), and (iii) deployment refinement. Embedding the MA‐DTA and customized refinement strategy into the iterative planning structure enables the integration of operations and planning of the EFCS network. A numerical experiment and an empirical study in the Shandong Peninsula urban agglomeration are conducted. It demonstrates that the method can find a high‐quality solution within acceptable computation time and is applicable to realistic large‐scale EFCS planning. The planning method can effectively play the role of economy and facility in inducing EV users' charging demands, thus enhancing the overall ESER benefits. The integration of operation and planning is proven effective in reasonably matching the supply and demand of facility service and charging loads in a full‐time period. Further, the multistage EFCS planning schemes during 2025–2045 are explored, and some insightful policy implications are revealed.

Performance Evaluation and Analysis of Urban-Suburban 5G Cellular Networks

5G is the fifth-generation technology standard for the new generation of cellular networks. Combining 5G and millimeter waves (mmWave) gives tremendous capacity and even lower latency, allowing you to fully enjoy the 5G experience. 5G is the successor to the fourth generation (4G) which provides high-speed networks to support traffic capacity, higher throughput, and network efficiency as well as supporting massive applications, especially internet-of-things (IoT) and machine-to-machine areas. Therefore, performance evaluation and analysis of such systems is a critical research task that needs to be conducted by researchers. In this paper, a new model structure of an urban-suburban environment in a 5G network formed of seven cells with a central urban cell (Hot spot) surrounded by six suburban cells is introduced. With the proposed model, the end-user can have continuous connectivity under different propagation environments. Based on the suggested model, the related capacity bounds are derived and the performance of 5G network is studied via a simulation considering different parameters that affect the performance such as the non-orthogonality factor, the load concentration in both urban and suburban areas, the height of the mobile, the height of the base station, the radius, and the distance between base stations. Blocking probability and bandwidth utilization are the main two performance measures that are studied, however, the effect of the above parameters on the system capacity is also introduced. The provided numerical results that are based on a network-level call admission control algorithm reveal the fact that the investigated parameters have a major influence on the network performance. Therefore, the outcome of this research can be a very useful tool to be considered by mobile operators in the network planning of 5G.

Optimal planning for distribution networks considering system uncertainties using pseudo-inspired gravitational search algorithm

Optimal planning for distribution networks considering system uncertainties using pseudo-inspired gravitational search algorithm

Unveiling the evolution of urban rail transit network: considering ridership attributes

ABSTRACT Previous research on urban rail transit (URT) evolution mainly focused on network topology, neglecting ridership attributes. This study extracts ridership and network topology indicators from Chinese URT data. Employing a self-organizing mapping neural network model, it divides China’s URT development into four stages. The initial stage and the development stage form the framework of URT network. The network diameter reaches the maximum in the networked operation stage. In the mature stage, URT network densification occurs alongside a significant increase in resident ridership. It is also found that each network indicator has a significant nonlinear relationship with ridership attributes. These findings are of guiding significance for urban planners to accurately understanding URT’s future development and rational network planning and construction.

Management of extreme weather impacts on electricity grids: An international review

Abstract Extreme weather events, such as high winds, storms, flooding and temperature extremes, are a major cause of disruption to the supply of electricity to consumers. System Operators (SOs) are responsible for ensuring stable real-time operation of large-scale power networks, and will act to prevent adverse impacts of such events on consumer supply, contain the extent of supply interruptions that do occur, and restore supply to affected consumers in an efficient and timely manner. SOs will also generally be involved in some way in the long-term planning of the transmission network and generation capacity required to ensure future resilience. In this paper we review some of the strategies adopted by SOs across the globe in ensuring high levels of reliability and resilience to extreme weather, with reference to learning generated from specific recent events. In the face of the potential for both the frequency of such events and for their consequent impacts to increase in the future, we recommend that regulatory control of investment in networks is informed by quantified understanding of the climate-energy interface, including assessment of the potential frequency and impacts of future weather events and shared learning from events experienced by different operators. The statutory role of utilities should include robust assessment of future weather-related risks and appropriate investment in their asset resilience, as well as assisting in the preparedness of supporting agencies to mitigate the impacts of weather-related disturbances on energy consumers.

Using public participation gis to understand texas coastal communities’ perceptions and preferences for urban green space development in connection to their perceptions of flood risk

Urban green spaces, a form of green infrastructure, provide cities with a number of opportunities to build urban resilience to withstand a wide range of urban challenges, such as land-use change, climate change, biodiversity loss, and physical and mental health. Despite their benefits to communities, urban green spaces are among the most valuable and attractive areas for development and are most susceptible to urban development. Therefore, careful planning of the green space network is needed,requiring diverse voices from not only city policymakers and planning experts but also local residents. This study investigated residents’ perceptions and preferences for green space development and its association with perceived flood risk. Data were collected from residents residing in two Texas Gulf Coast cities via a Public Participation Geographic Information System (PPGIS) platform. Our analyses revealed that Texas Gulf Coast residents assigned a full spectrum of landscape values to their existing urban green spaces with aesthetic and recreation values being most frequently identified. Notably, across the city landscape, urban green spaces that were most favored by residents tended to be green spaces with water bodies, large in scale and offered a broader array of facilities/amenities. These spaces were also identified as flood-prone areas, presenting opportunities for green space improvement, flood protection, and preservation. In addition, we observed a significant association between residents’ risk perception and the number of locations across the city landscape they perceived as posing the greatest flooding threat. Our findings illustrate a need to promote multiple landscape values of urban green spaces, especially in its ability to protect the city from future flooding. This research highlights how PPGIS can support green infrastructure planning as well as enhance public participation in green infrastructure planning processes.