Station Placement Research Articles

SYNTHESIS OF A COMPUTER NETWORK FOR CONTROLLING MOVING OBJECTS

Today, there are many methods of synthesis of the basic network of the computer control system. But they do not take into account the mobility of control objects. Therefore, the purpose of the article is to develop a method of synthesis of the basic network of the computer control system, which takes into account the mobility of control objects. The method is based on the use of the function of the average loss of messages in the network at the input load and the dynamic resource allocation plan. Regardless of the selected class of the basic network, the developed method assumes: the selection of an appropriate management efficiency assessment function; synthesis of control algorithms for restricting access to the network; synthesis of adaptive routing algorithms that ensure optimal distribution of flows on the short-circuit network. The direction of further research is to take into account the placement of base stations of the computer system.

Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.

A Case Study for an Assessment of Fire Station Selection in the Central Urban Area

With the continual acceleration of urbanization, the amount of urban infrastructure and the quality of public services are increasing in many cities. A pressing concern in this context is the growing problem of incompatible fire protection construction, indicating a need for urban fire stations with well-planned layouts. However, research on optimizing the layout and placement of fire stations by considering the various factors affecting station layouts is lacking. The current study addressed this gap by establishing an optimal fire station layout by using a geographic information system (GIS) and elucidated the trends of GIS application in firefighting and rescue operations. The study’s findings reveal the benefits of avoiding blind spots, enhancing the selection of fire station sites, and optimizing service coverage. Furthermore, this study optimized the layout of CZ city’s downtown fire stations, which could enhance CZ city’s firefighting capabilities.

Optimal Placement of Base Stations in Integrated Design of Wireless Networks

Optimal Placement of Base Stations in Integrated Design of Wireless Networks

Automated Drone Delivery System

Abstract: In the realm of urban logistics, the deployment of automated drone delivery systems has emerged as a transformative solution for urgent package deliveries. This research endeavors to design and construct a fully autonomous drone tailored for rapid and precise deliveries from scratch. Leveraging innovative technologies, including Arduino Uno as the central processing unit, along with GPS modules, NRF24LO1, Ultrasonic sensors, and advanced artificial intelligence algorithms, we create a sophisticated delivery system. Central to our approach is the strategic placement of multiple delivery stations across the city. Each drone is dedicated to a specific route between two delivery stations. When a delivery order is placed, the drone's software autonomously calculates the optimal distance to the nearest delivery station, ensuring swift and energy-efficient deliveries. Crucially, our system operates without human intervention, relying solely on advanced artificial intelligence algorithms to navigate the urban landscape effectively. This paper elucidates the technical intricacies of our drone's design, detailing the integration of sensors, software algorithms, and communication modules. Through rigorous experimentation and analysis, we validate the efficacy of our automated drone delivery system. Our findings not only showcase the practicality of autonomous drone technology in urban logistics but also underscore the potential for its widespread implementation in real-world scenarios, revolutionizing the future of package deliveries.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms.

Unveiling poaching patterns and threat sources for informed conservation in southeast Angola

Poaching poses a significant threat to wildlife and ecosystems in southeast Angola, yet comprehensive studies to inform effective conservation strategies have been lacking. This novel study systematically assesses the spatial distribution and influencing factors of poaching activities in the region. Utilizing data from extensive patrolling efforts and predictive modeling, the hotspots and risk zones for wire-snares, gin traps, elephant poaching, and poaching camps are delineated. Our findings reveal distinctive socio-political and geo-ecological patterns governing each type of illegal activity. Wire-snares and gin traps display clear socio-political correlates, with proximity to towns and villages influencing their prevalence. These findings advocate for strategic placement of patrol stations and underscore the need for expanded law enforcement efforts to mitigate these threats. Conversely, elephant poaching for ivory exhibits transboundary influences, with heightened risk zones along international borders. This highlights the urgency of collaborative, transboundary management strategies to counteract external drivers of elephant poaching. Geo-ecologically, closed habitats dominated by woody vegetation, such as shrublands and closed woodlands, are favored for wire-snares and gin traps, likely due to reduced detection risk. Riparian habitats emerge as high-risk areas for elephant poaching and fishing camps, highlighting their importance for targeted conservation interventions. The fine-scale spatial analysis guides resource allocation and law enforcement strategies, while the identification of distinct threat sources informs proactive measures. To curb local demand-driven poaching, community-based conservation and alternative livelihood initiatives are recommended.

Comparative analysis of unidirectional and bidirectional electric vehicle charging stations (EVCS) optimal configuration in an IEEE 37-bus feeder system using Genetic Algorithm

Various power system problems and challenges may arise in the future due to the large scale of deployment of electric vehicles (EVs). Hence, the proper placement of EV charging stations (EVCS) effectively mitigates the impact of high EV loads connected to the grid. The research intends to explore and analyze differences between the regulation effectiveness of unidirectional and bidirectional charging technologies by utilizing different comparison evaluation indices. Moreover, considering their penetration level, this study tackles the impact analysis of EV and EVCS integration through time. Specifically, this paper aims to identify the optimal EVCS sites in an IEEE 37-bus test feeder system to minimize power loss brought by EV integration. Through MATLAB R2022b simulation and OpenDSS power flow analysis, the EVCS are optimally located near the supply bus. The findings show a direct relationship between the EV penetration level and system power loss. Due to the EV technology growth, there is an observed voltage profile degradation of up to 1.7094 p.u. The paper also highlights that although EV bidirectional charging technology (BCT) might reduce the load on the grid in the next few years of low penetration compared to unidirectional charging technology (UCT), it will give no significant difference due to the rapid increase of load connected during its high EV penetration.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity.

PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical regulations, etc. Many classical and metaheuristic-based approaches provide good solutions, but they are not intuitive, and they do not scale well for large cities and complex constraints. Many classical solution techniques often require prohibitive amounts of memory and their solutions are not easily explainable. We analyzed the layouts of the 50 most populous cities of the world and observed that any city can be represented as a composition of five basic primitive shapes (stretched to different extents). Based on this insight, we use results from classical topology to design a new charging station placement algorithm. The first step is a topological clustering algorithm to partition a large city into small clusters and then use precomputed solutions for each basic shape to arrive at a solution for each cluster. These cluster-level solutions are very intuitive and explainable. Then, the next step is to combine the small solutions to arrive at a full solution to the problem. Here, we use a surrogate function and repair-based technique to fix any resultant constraint violations (after all the solutions are combined). The third step is optional, where we show that the second step can be extended to incorporate complex constraints and secondary objective functions. Along with creating a full software suite, we perform an extensive evaluation of the top 50 cities and demonstrate that our method is not only 30 times faster but its solution quality is also 36.62% better than the gold standard in this area—an integer linear programming (ILP) approach with a practical timeout limit.

Automatic Placement Algorithm of Base Stations Trunking Communication Systems

to increase the efficiency of the trunking communication systems design process, an original algorithm for the automatic placement of base stations is proposed, taking into account the real conditions of radio wave propagation. The analysis showed that the existing methods allow you to place base stations manually, with a given step or automatically, but without taking into account the propagation of radio waves, which leads to poor-quality coverage of the required area with communication. The aim of the work is to reduce the complexity of designing a trunking communication system that is guaranteed to cover a given service area. It is proposed to calculate the service areas of base stations using a statistical model, as well as taking into account additional diffraction losses on the radio signal propagation path. The solution to the problem of automatic placement of base stations is based on the use of the proposed modified Hut model, which takes into account the terrain at the locations of the base and portable stations, as well as on the international recommendation for predicting signal propagation on a specific route for ground services. With automatic placement, the optimality criterion is fulfilled, which consists in placing the minimum number of base stations necessary to cover the required territory. An element of the novelty of the presented solution is that at the first stage of the calculation, the service area of the base station is determined by the statistical method, at the second stage, an updated calculation of the service area takes place taking into account the actual conditions of radio wave propagation at the binding site, and at the third stage, the zone of guaranteed customer service is determined. The use of the proposed algorithm makes it possible to reduce the complexity of the system design process and determine the sufficient number of base stations needed to cover a given area with communication. Appropriate software has been developed to apply the algorithm in real practice. To confirm the effectiveness of the algorithm, the paper presents comparative calculations of the time spent on designing a trunking communication system using the developed software and using the widely used RadioMobile program.

Protecting healthcare workers and patients during the COVID-19 pandemic: a comparison of baseline and follow-up infection prevention and control needs in Nigerian military healthcare facilities delivering HIV services

BackgroundProtecting the HIV health workforce is critical for continuity of services for people living with HIV, particularly during a pandemic. Early in the COVID-19 pandemic, the Nigerian Ministry of Defence, in partnership with the US Military HIV Research Program, took steps to improve infection prevention and control (IPC) practices among staff working in select PEPFAR-supported Nigerian military health facilities.MethodsWe identified a set of IPC activities a priori for implementation at four Nigerian military hospitals in HIV and related departments in early 2021, including continuous medical masking, physical distancing, placement of additional hand washing stations and hand sanitizers throughout facilities, and training. We fine-tuned planned intervention activities through a baseline needs assessment conducted in December 2020 that covered eight IPC components: ‘IPC program structure, funding and leadership engagement’; ‘IPC policies, guidelines and standard operating procedures (SOPs)’; ‘infrastructure’; ‘triage and screening’; ‘training, knowledge and practice’; ‘personal protective equipment (PPE) materials, availability and adequacy’; ‘biosafety and waste management’; and ‘monitoring and remediation’ prior to implementation. Baseline results were compared with those of a follow up assessment administered in August 2021, following intervention implementation.ResultsIPC readiness remained high at both baseline and follow-up assessments for ‘IPC guidelines, policies, and SOPs’ (96.7%). The components ‘infrastructure’ and ‘monitoring and remediation’, which needed improvement at baseline, saw modest improvements at follow-up, by 2% and 7.5%, respectively. At follow-up, declines from high scoring at baseline were seen in ‘IPC program structure, funding and leadership engagement’, ‘training, knowledge and practice’, and ‘biosafety and waste management’. ‘PPE materials availability and adequacy’ improved to 88.9% at follow-up. Although unidirectional client flow was newly implemented, the score for ‘triage and screening’ did not change from baseline to follow-up (73%).ConclusionVariability in IPC component readiness and across facilities highlights the importance of building resilience and employing a quality improvement approach to IPC that includes regular monitoring, re-assessment and re-training at set intervals. Results can be used to encourage solutions-oriented dialogue between staff and leadership, determine needs and implement action plans to protect staff and people with HIV.

Strategic Planning for Equitable RWIS Implementation: A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems (RWIS) station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables. Previous research primarily centered on single-variable analysis focusing on road surface temperature (RST). The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework. This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty. To demonstrate the effectiveness of this refinement, the authors applied the framework to Maine’s existing RWIS network, conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm. The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations. A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations. The location solutions generated have been adopted by MaineDOT for future implementations, attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

ПОВЕРХНОСТНЫЕ ГРАВИТАЦИОННЫЕ ВОЛНЫ В СЕВЕРНОЙ АТЛАНТИКЕ И В ДРЕЙФУЮЩЕМ ЛЬДУ АРКТИКИ

Sea ice in the Arctic is one of the main dynamic elements in the interacting atmosphere–ice–ocean system. Currently, theoretical and experimental studies of drifting and soldered ice are used to develop predictive models of the mechanical state of sea ice. This paper presents the results of an experimental study of physical and mechanical processes on the surface of the Arctic Ocean. Field work was carried out using the spatial placement of autonomous seismic stations directly on the drifting ice. Data on surface gravitational waves of the ocean, accompanied by periodic deformations of bending and shear in the ice, have been obtained. On the basis of amplitude spectra, the features of the process of propagation of storm waves of swell in the ice cover are considered and a comparative analysis with map of maximum heights of sea waves is performed. A significant factor is the use of microseismic oscillations recorded at coastal stations of the Greenland Sea to determine the time and place of a powerful storm. The spectrum of the wave field in the ice sheet indicates an increase in the frequency of oscillations due to the dispersion of the wave field. New data have been obtained on the large-scale mechanics of the occurrence of cracks in the ice cover and discrete flexural-gravity waves as one of the main indicators of ice destruction. The results of the study are important for improving weather and climate forecasting models, as well as for solving engineering problems on the Arctic shelf.

A novel modified Archimedes optimization algorithm for optimal placement of electric vehicle charging stations in distribution networks

The number of electric vehicle charging stations (EVCSs) is increasing alongside electric vehicles (EVs). The unplanned rollout of EVCSs can lead to problems such as deterioration of the voltage profile, increased active power losses, and maximum load demand in distribution networks (DNs). Therefore, EVCSs should be placed in optimum locations on the DNs. The complexity of the problem requires efficiency and performance. In this paper, a novel modified Archimedes optimization algorithm (MAOA) is proposed for optimal placement of EVCSs in DNs. Five parameters of the AOA were modified using the Honey Badger Algorithm (HBA). This placement is based on a combination of indexes: power loss, voltage deviation, and voltage stability index (VSI). Next, Newton-Raphson based load flow analysis is used to compute the minimum cost. MAOA, AOA and eight other optimization algorithms are compared. The results provide the most appropriate buses in the DN. The efficiency of the proposed algorithm was tested on IEEE 33, 69, and 10-bus Hatay power network systems. In all test systems, the proposed MAOA provided the best results with consistently low standard deviation values. The results suggest that the proposed method is more effective in the integration of EVCSs into the DN. © 2017 Elsevier Inc. All rights reserved.

The application challenge of electric vehicles

The growing demand for sustainable transportation has prompted an accelerated transition toward electric vehicles (EVs) as a promising solution to mitigate the environmental impact of conventional internal combustion engine (ICE) vehicles. This research analyzes the key challenges and technologies associated with EVs, aiming to produce insights into this rapidly evolving field. The challenges surrounding EVs encompass various aspects, including limited driving range, lack of access to electric vehicle charging stations (EVCS), charging times for EVs, and safety considerations. To overcome these challenges, several technological advancements have emerged. Battery technology has advanced to extend the driving range per charge of an electric vehicle. Various models and algorithms to study the EVs charging station placement problem (EVCSPP) have been proposed to find the optimal location to build the charging stations. The advancement of extreme fast charging (XFC) technology and the intelligent transport system (ITS) shorten the charging time to improve the practicality of EVs adoption. Furthermore, the EVs charging standard and risk management have been introduced to ensure a secure charging system. By addressing these challenges and leveraging innovative technologies, a comprehensive and sustainable charging network can be realized, facilitating the transition towards a cleaner and greener transportation system.

Optimal Station Placement Method for Three-Station TDOA Localization Under Signal Beam Constraint

Optimal Station Placement Method for Three-Station TDOA Localization Under Signal Beam Constraint

An intelligent optimal charging stations placement on the grid system for the electric vehicle application

In smart cities, electrified vehicle plays a vital role. Due to the number of electric vehicles increasing rate, the optimised deployment of the charging station without maximum loss and voltage imbalance is required. Many existing strategies studied for the optimal charging station deployment result in higher power utilisation, power loss, harmonic distortion and voltage imbalance. Therefore a novel Dove-based Recursive Deep Network (DbRDN) was planned to implement. The DG grid system is initially created by integrating hybrid wind, solar and hydropower sources. Subsequently, the DbRDN is designed for the optimal location for the placement of the EV charging station by analysing load and line data. Moreover, the efficiency of the developed system is evaluated at both the balanced and unbalanced conditions and the outcomes are computed in terms of power loss, harmonic distortion, voltage imbalance, error and accuracy. The results are compared with prevailing techniques to validate the improvement score.

Improving the Efficiency of Modern Warehouses Using Smart Battery Placement

In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems.