Station Placement Research Articles

Stochastic programming solution for placement of satellite ground stations

Disaster recovery efforts are enhanced through the collection and dissemination of satellite data, which is downloaded from satellites to ground stations. The optimal ground station locations vary depending on the location of the disaster, but ground station construction occurs before the realization of a disaster. Thus, a stochastic optimization problem arises: decide the location of ground stations before disasters with uncertain locations. We use a stochastic programming approach to select the location of ground stations given a set of potential disaster scenarios. The objective is to maximize the expected amount of data downloaded. The problem formulation consists of a two-stage stochastic program where the first-stage determines the locations of the ground stations and the second-stage schedules the uploading and downloading of data. We solve the problem using the L-shaped method; we find that it significantly outperforms solving the deterministic equivalent problem directly. We also find that an alternate second-stage formulation significantly improves solution time. The optimized set of ground stations found by our algorithm is compared to the set of ground stations operated by the National Oceanic and Atmospheric Administration’s; results confirm that the current placement is effective and demonstrate the benefit in adding additional ground stations.

Cost‐based optimal siting and sizing of electric vehicle charging stations considering demand response programmes

Here, the optimal placement and sizing of electric vehicle charging stations (EVCSs) are presented. High penetration of electric vehicles (EVs) and resulted losses in network would consequently impose more complexity to solution of application problem of EVCSs. To overcome this problem, the model would consider the incentive-based demand response programmes (DRPs), which is handled by particle swarm optimisation algorithm. Minimising investment cost, connection cost, total cost of losses, and demand response (DR) cost are the objective functions of this problem here. Finally, the proposed model is applied to a test system and results are discussed. By comparing the results obtained through different scenarios, it is concluded that the application of DRP results in a distinct reduction in grid losses and total costs.

Impact of Electric Vehicle Charging Station Load on Distribution Network

Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.

LOCATION AWARE ALGORITHM FOR ENERGY EFFICIENT HETEROGENEOUS NETWORK

The placement of small base stations (BSs) in heterogeneous network (HetNet) has recently attracted increasing interest to optimize the network energy efficiency (EE). In this paper, we propose a new energy efficient algorithm for small BSs placement in HetNet with a constrained spatial model that is aimed at supporting the decisions in the planning process where to locate new small BSs. The investigation analysis involves both uniform and non-uniform traffic load distribution across the coverage area. For uniform user distribution, our simulation results have shown that the EE of HetNet improved as the small BSs placed towards macrocell edges. However, this prediction is true for the uniform distribution and outcomes could change for a non-uniform distribution. In addition, the improper position of small BSs, i.e., at the cell edge of macrocell instead of the center of the hotspots, result in degrading the EE of HetNet as the number of users being close to the small BSs decreases compared to the uniform case. Finally, our findings tend to agree with suggestions to use a few micro BS (MieNBs) rather than to use a large number of pico BS (PeNBs) to perform energy saving in case of network with spatially spread users.

Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary lightning search algorithm.

The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.

An optimization approach for the placement of bicycle-sharing stations to reduce short car trips: An application to the city of Seoul

Substantial motor vehicle exhaust, a primary cause of air pollution, is emitted on short car trips of three miles or less. Bicycles have been considered an optimum means of completing these short trips because the bicycle is an environmentally friendly, economical, and convenient vehicle. Accordingly, many countries have adopted public bicycle-sharing systems to reduce the use of private vehicles for short trips in central downtown areas. In this paper, we propose a new framework, based on taxi trajectory data, for locating bicycle-sharing stations most efficiently to replace short automobile trips. The proposed framework is applied to Gangnam-gu, a district within the city of Seoul, Korea. Results using two different location-allocation models are demonstrated. As expected, when the p-median model was implemented, the selected stations were more scattered over the whole district, whereas when the MCLP model was implemented, the stations were more concentrated on central areas. Our approach is applicable to any city considering a bicycle-sharing system and can contribute to the system’s efficiency in improving environmental conditions in a central downtown area.

Evaluating public ambulance service levels by applying a GIS based accessibility analysis approach

Ambulance vehicles are required to respond rapidly to medical emergencies. A number of factors may affect response times, most importantly the location of emergency services stations, the number of ambulance vehicles available at each station, road conditions and traffic volumes. To support spatial planning and provide input to the Integrated Development Plan of the City of Tshwane, a GIS based analysis of ambulance response time was undertaken. The purpose was to make recommendations aimed at the improved placement of stations and allocation of ambulances per station to improve service delivery through reducing the likely response times of ambulance services within the Tshwane Metropolitan area. The key input parameters used included the spatial distribution of demand (population), supply (station locations and ambulance numbers) and emergency road speeds These were tested against a provision standard of one ambulance per 30 000 people. The response target applied in suburban areas was 15 minutes but 40 minutes in low density outlying areas. The results show that the current emergency services stations’ locations are well distributed within the metropole; however, there seems to be a spatial mismatch in vehicle allocation. The paper discusses the impact of the two demand scenarios, namely: universal reliance on public ambulance services versus public ambulances only serving those without medical insurance. The paper concludes that GIS provide an extremely versatile and useful means of testing the implications of demand scenarios and standards relating to these factors of service provision and thereby contribute to better informed decision making.Keywords: Ambulance vehicles, response time, emergency services, accessibility, GIS

Multi-agent target searching with time constraints using game-theoretic approaches

PurposeThe purpose of this paper is to propose a model for a target searching problem in a two-dimensional region with time constraints. The proposed model facilitates the search operation by minimizing the mission time and fuel usage, and the search operation is performed by a set of agents divided into a number of groups.Design/methodology/approachThe authors have applied optimization techniques, Cartesian product, inclusion–exclusion principle, cooperative strategy, Shapley value, fuzzy Shapley function and Choquet integral to model the problem.FindingsThe proposed technique optimizes the placement of base stations that minimizes the sortie length of the agents. The results show that the cooperative strategy outperforms the non-cooperative strategy. The Shapley values quantify the rewards of each group based on their contributions to the search operation, whereas the fuzzy Shapley values determine the rewards of each group based on their contributions and level of cooperation in the search operation.Practical implicationsThe proposed model can be applied to model many real-time problems such as patrolling in international borders, urban areas, forests and managing rescue operations after natural calamities, etc. Therefore, defence organizations, police departments and other operation management sectors will be benefitted by applying the proposed approach.Originality/valueTo the best of the authors’ knowledge, determining the optimal locations of base stations in a region is not explored in the existing works on target searching problems with fuel constraints. The proposed approach to cooperatively search the targets in a region is new. Introducing the Shapley function and fuzzy Shapley function is a novel idea to quantify the rewards of each group based on their contributions and level of cooperation in the search operation. This paper addresses these unexplored areas.

Migrant deaths at the Arizona–Mexico border: Spatial trends of a mass disaster

Geographic Information Science (GIScience) technology has been used to document, investigate, and predict patterns that may be of utility in both forensic academic research and applied practice. In examining spatial and temporal trends of the mass disaster that is occurring along the U.S.–Mexico border, other researchers have highlighted predictive patterns for search and recovery efforts as well as water station placement. The purpose of this paper is to use previously collected spatial data of migrant deaths from Arizona to address issues of data uncertainty and data accuracy that affect our understanding of this phenomenon, including local and federal policies that impact the U.S.–Mexico border. The main objective of our study was to explore how the locations of migrant deaths have varied over time. Our results confirm patterns such as a lack of relationship between Border Patrol apprehensions and migrant deaths, as well as highlight new patterns such as the increased positional accuracy of migrant deaths recorded closer to the border. This paper highlights the importance of using positionally accurate data to detect spatio-temporal trends in forensic investigations of mass disasters: without qualitative and quantitative information concerning the accuracy of the data collected, the reliability of the results obtained remains questionable. We conclude by providing a set of guidelines for standardizing the collection and documentation of migrant remains at the U.S.–Mexico border.

Risk-based framework for optimizing residual chlorine in large water distribution systems.

Managing residual chlorine in large water distribution systems (WDS) to minimize human health risk is a daunting task. In this research, a novel risk-based framework is developed and implemented in a distribution network spanning over 64km2 for supplying water to the city of Al-Khobar (Saudi Arabia) through 473-km-long water mains. The framework integrates the planning of linear assets (i.e., pipes) and placement of booster stations to optimize residual chlorine in the WDS. Failure mode and effect analysis are integrated with the fuzzy set theory to perform risk analysis. A vulnerability regarding the probability of failure of pipes is estimated from historical records of water main breaks. The consequence regarding residual chlorine availability has been associated with the exposed population depending on the land use characteristics (i.e., defined through zoning). EPANET simulations have been conducted to predict residual chlorine at each node of the network. A water quality index is used to assess the effectiveness of chlorine practice. Scenario analysis is also performed to evaluate the impact of changing locations and number of booster stations, and rehabilitation and/or replacement of vulnerable water mains. The results revealed that the proposed methodology could facilitate the utility managers to optimize residual chlorine effectively in large WDS.

Influence of charging behaviour given charging infrastructure specification: A case study of Singapore

Electric vehicles (EVs) are set to play a crucial role in making transportation systems more sustainable. However, charging infrastructure needs to be built up before EV adoption can increase. A crucial factor that is ignored in most existing studies of optimal charging station (CS) deployment applying agent-based nanoscopic traffic simulation is the role played by the charging behaviour of drivers. In this study, through an agent-based traffic simulation, we analyse the impact of different driver charging behaviour under the assumption that CSs are placed at existing petrol stations and residential car park locations in Singapore. Three models are implemented: a simple model with a charging threshold and two more sophisticated models where the driver takes the current trip distance and existing CS locations into account. We analyse the effect of these three charging behaviour models on the performance of the charging infrastructure with respect to a number of different measures. Results suggest that charging behaviours do indeed have a significant impact on the simulation outcome. We also discover that the sensitivity of model parameters in each charging behaviour and initialisation parameters of the agents are an important factor to consider. Variations in model and initialisation parameters can lead to significant different results. In addition, we investigate into a different charging infrastructure distribution using a grid-based approach for Singapore. Results propose that a more evenly distributed charging infrastructure with the grid-based approach is less effective than the one with charging station placement at existing petrol stations and residential car park locations.

A power storage station placement algorithm for power distribution based on electric vehicle

Inspired by the concept of energy internet and energy problems including greenhouse gas emission and regional shortage, this article will propose an idea of electric power distribution using city bus lines running electric vehicles. The designed power distribution system includes renewable energy sources as the energy input, power storage stations placed at some bus stops as the fixed energy output, and bus lines running electric vehicles as the connections between them. As the basic and priority problem, choosing the suitable location to place power storage stations can not only reduce the total number of construction but also increase the utilization rate of each one. Main work involves the two branch algorithms for solving the placement problem; simulation while using real-world transportation data of two city bus maps and analysis about the advantages and disadvantages of algorithms.

Method of Optimizing Placement of Mobile Car Service Stations in Sparsely Populated Areas

Abstract The article considers the development of a method of optimizing placement of mobile car maintenance stations (MMSs) in sparsely populated areas. The method includes the analysis of the territorial location of settlements, number of vehicles in those settlements, calculation of the MCSS travel time between settlements, determination of the optimum base site for MMS by a series of criteria.

Where Do Bikeshare Bikes Actually Go?: Analysis of Capital Bikeshare Trips with GPS Data

Bikeshare systems with docking stations have gained popularity in cities throughout the United States—and have increased from six programs with 2,300 bikes in 2010 to 74 systems with 32,200 bikes in 2016. Even though bikeshare systems generate a wealth of data about bicycle checkout and check-in locations and times at docking stations, virtually nothing is known about routes and activities undertaken between checkout and check-in. Such information could greatly enhance expansion of bikeshare systems, placement of new docking stations, and location of new bike lanes and paths. In pursuit of such information, the District Department of Transportation, Washington, D.C., placed GPS trackers on 94 Capital Bikeshare (CaBi) bikes in the spring of 2015. On the basis of these data, this geographic information system analysis distinguished riders by type of CaBi membership, identified popular routes, analyzed bicycle infrastructure use, and examined stops and dwelling times at places of interest. Results showed strong differences in trip attributes between types of membership. Trips taken by short-term users were longer in distance, slower than long-term users’ trips, and concentrated in and around the National Mall, whereas long-term users’ trips were concentrated in mixed-use neighborhoods. Short-term users rode 12% of their miles on dedicated bicycle infrastructure, 61% in parks, and 27% on roadways with motorized traffic, whereas for long-term members the percentages were 33%, 17%, and 50%, respectively. On the basis of the routes taken in this study, potential locations were recommended for bicycle infrastructure improvements and new bikeshare stations.

Algorithms for Maintaining Consistency of Cached Data for Mobile Clients in Distributed File System

A cache stores data in order to serve future requests to those data faster. In mobile devices, the data have to be transferred from a server through the mobile network before being stored in the cache. The mobile network is prone to failure caused by users' movements and by the placement of base transceiver stations. Moreover, the mobile devices use various telecommunications technologies and therefore the speed of the network is highly variable. Using a cellular network for communication is also expensive. The cache is an intermediate component which addresses this problem. Once the data are downloaded, they can be stored in the cache for possible future reuse. When using a cache, the system designer presumes that the data will be requested again in the future. On the other hand, the original data stored on the server can be changed. Then, the cached data are in an inconsistent state. In this paper, authors present an adaptive method for maintaining the consistency of cached data which saves network traffic by reducing the number of messages needed for inconsistency detection.

Electric Vehicle Charging Station Placement for Urban Public Bus Systems

Due to the low pollution and sustainable properties, using electric buses for public transportation systems has attracted considerable attention, whereas how to recharge the electric buses with long continuous service hours remains an open problem. In this paper, we consider the problem of placing electric vehicle (EV) charging stations at selected bus stops, to minimize the total installation cost of charging stations. Specifically, we study two EV charging station placement cases, with and without considering the limited battery size, which are called ECSP_LB and ECSP problems, respectively. The solution of the ECSP problem achieves the lower bound compared with the solution of the ECSP_LB problem, and the larger the battery size of the EV, the lower the overall cost of the charging station installation. For both cases, we prove that the placement problems under consideration are NP-hard and formulate them into integer linear programming. Specifically, for the ECSP problem we design a linear programming relaxation algorithm to get a suboptimal solution and derive an approximation ratio of the algorithm. Moreover, we derive the condition of the battery size when the ECSP problem can be applied. For the ECSP_LB problem, we show that, for a single bus route, the problem can be optimally solved with a backtracking algorithm, whereas for multiple bus routes we propose two heuristic algorithms, namely, multiple backtracking and greedy algorithms. Finally, simulation results show the effectiveness of the proposed schemes.

Measurement and Analysis of Quality of Service of Mobile Networks in Afghanistan – End User Perspective

Enhanced Quality of Service (QoS) and satisfaction of mobile phone user are major concerns of a service provider. In order to manage network efficiently and to provide enhanced end to end Quality of Experience (QoE), operator is expected to measure and analyze QoS from various perspectives and at different relevant points of network. The scope of this paper is measurement and statistically analysis of QoS of mobile networks from end user perspective in Afghanistan. The study is based on primary data collected on random basis from 1,515 mobile phone users of five cellular operators. The paper furthermore proposes adequate technical solutions to mobile operators in order to address existing challenges in the area of QoS and to remain competitive in the market. Based on the result of processed data, considering geographical locations, population and telecom regulations of the government, authors recommend deployment of small cells (SCs), increasing number of regular performance tests, optimal placement of base stations, increasing number of carriers, and high order sectorization as proposed technical solutions.

A Constellation Design Methodology Based on QoS and User Demand in High-Altitude Platform Broadband Networks

An investigation into the constellation design methodology of high-altitude platform (HAP) broadband networks is presented. The quality of service (QoS) metrics and user demand model are established in the HAP broadband networks. Each HAP is seen as a mobile base station, and a methodology of mobile base station placement is proposed with QoS and user demand guarantee for the first time. The multiple HAP coverage is analyzed with link geometry based on the single HAP coverage model. Besides, a cost-efficient optimization design framework for the HAP constellation is established by optimizing the design vector to maximize the network capacity per cost under the constraints of QoS metrics. In addition, the optimization model and improved artificial immune algorithm based on immune review are designed, and performance of the proposed method is demonstrated through in-depth numerical simulations. Simulation results show that the proposed constellation design method can effectively solve the problem of the mobile base station layout in the HAP broadband networks.

Planning of electric vehicle infrastructure based on charging reliability and quality of service

This paper presents an optimization model for charging station placement in order to minimize the overall cost by satisfying the charging reliability and quality of service expected by electric vehicle owners/drivers. To better understand and illustrate the problem's complexity, set modeling is used to represent the road network and the electric vehicle driving trajectories. By involving the Euclidean distance, this optimization model also considers the charging reliability as well as the driving range limitation of electric vehicles. The disposable charging time of electric vehicle owners/drivers is reflected by incorporating the new Quality of Service index. The numeric results illustrate the application of the proposed optimization in achieving the minimal cost of charging station locations while also achieving both charging reliability and expected quality of service through the analysis of electric vehicle owners'/drivers' behavior.

Efficient reduction of optimal disinfectant booster station placement formulations for security of large-scale water distribution networks

ABSTRACTIn response to a contamination incident in water distribution networks, disinfectant booster stations can be used to neutralize the contaminant and protect the public. In this paper, two mixed-integer linear programming formulations are proposed for optimal placement of disinfectant booster stations. The first formulation minimizes the contaminant mass consumed by the public, while the second formulation minimizes the number of people who ingest the contaminant above a mass threshold. The proposed formulations consider uncertainty in both the location and time of the contamination incident, resulting in an intractably large stochastic programming problem. This manuscript proposes a series of reductions that decrease the size of the problem by up to five orders of magnitude. The results show that the use of a small number of disinfectant booster stations can be very effective as a response strategy.