Optimal Location Selection Research Articles

Minimization of harmonic distortion impact due to large‐scale fast charging station using Modified Lightning Search Algorithm and Pareto‐Fuzzy synergistic approach

The increased number of nonlinear loads in the distribution system has caused a significant impact on its power quality in the form of higher harmonic distortion. The fast charging station (FCS) is an example of a powerful nonlinear load that is currently being widely promoted with the aim of supporting the continuous usage of electric vehicles (EV). The most common and cheapest method to minimize harmonic impact is through the use of a passive filter. This paper presents an optimal sizing and placement of a passive filter design based on the numbers of FCS installed in the distribution system. The MATLAB/m‐file platform is used to model the power system harmonic flow, passive filter, and FCS. The control strategy used to minimize the total harmonic distortion (THD) due to the large number of FCS is presented based on the selection of optimal location and the size of passive filters that will provide minimum THD and apparent power losses to the overall distribution system. A Modified Lightning Search Algorithm (MLSA) technique is applied with the aim of identifying the optimal solution. The Pareto‐Fuzzy technique is used to obtain a better solution among nondominated solutions. From the result, the optimal placements and sizes of passive filter will be able to reduce the maximum THD and also the total losses in the distribution system, which is equipped with 17 FCS units. This study is useful as a guide for grid owners to control the impact of large‐scale FCS deployment in the distribution system. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Integrated Model Approach STELLA Method of Site Identification for Charging Infrastructure

The spread of a charging infrastructure for battery electric vehicles is crucial for both coping with the increasing number of electric vehicles and reducing users’ impediments to adoption. Therefore, the selection of optimal (fast-)charging locations determines acceptance, utilization, and thus, the economic viability of a single site or the whole charging network. The methodology of the Integrated Model Approach STELLA1 for site identification for a charging infrastructure transmits proven methods of traffic modeling in a new context. It is already possible to make statements regarding the positioning of the charging infrastructure as well as the expectable quantitative needs of charging stations during the early stages of infrastructure planning processes to target a nationwide, ubiquitous and needs-based charging network, by combining different data (e. g. transport networks and traffic volumes, settlement structures, vehicle characteristics, power supply data and user requirements) and refining classic four-step traffic modelling. Due to the structure of the method, a flexible combination, extension or transferability to other contents is possible. Currently, this method has been developed for a planning area that covers the entire territory of the Federal Republic of Germany.

Case study of stationary energy storage device in a 3 kV DC traction system

The paper presents the results of economic study of energy storage system (ESS) implemented in 3 kV DC power supply system. Two conceptions of ESS have been investigated: ESS with supercapacitor (SC) and hybrid ESS (HESS) with SC and LFP battery. The investigated locations of energy storage systems are considered among existing traction substations in two railway lines with different density of train operation. The considered aims of energy storage system implementation are decreasing of energy consumption by maximum regenerative energy utilization and reduction of peak 15- min power demand of traction substation. The paper presents a method of regenerative power estimation depending on the location of the considered ESS implementation point. Also the method of optimal location selection of ESS in terms of minimization of Simple Payback Time (SPBT) of investment is presented. Besides the influence of initial cost value as well as energy price on the SPBT value are investigated. The results are compared between two railway lines with different number of trains operating.

A Study on Developing Methodologies for Optimal Location Selection of Regional Logistics Complex

물류단지란 물류단지시설과 지원시설을 집단적으로 유치, 육성하여 화물이동의 효율성을 높이기 위한 시설이다. 정부에서는 매 5년마다 물류시설개발종합계획을 통해 물류단지를 포함한 주요 물류시설의 배치계획을 수립함으로써 국가의 합리적, 균형적 물류시스템 구축을 도모하고 있다. 그러나 거시적인 관점에서 지역별 화물처리 수요, 공급 우선순위에 대해 언급할 뿐 가능입지, 추진방법 등 구체적 추진계획은 포함하지 않고 있다. 따라서 본 연구에서는 ‘물류단지 최적 입지선정을 위한 프로세스’를 제시하고자 한다. 우선 ArcGIS를 활용한 토지분석을 통해 물류단지로서 적합한 대상지를 파악하고, 접근성, 주변환경 등을 종합적으로 고려하여 후보지를 선별한다. 이어서 물류시설 위치 및 처리 물동량을 입력변수로 하는 p-median모형을 적용하여 가장 적합한 후보지를 최종 선정한다. 본 연구에서 개발한 프로세스를 충청남도 사례에 적용함으로써 방법론의 적용 가능성을 검증해 본다. 본 연구의 결과는 물류시설개발종합계획, 지역물류기본계획 등 관련 계획의 효율적 추진 및 물류기업체의 시설 투자를 위한 참고자료로 활용될 수 있다.

Developing an Optimal Location Selection Methodology of Unmanned Parcel Service Box

본 연구에서는 무인택배함의 최적입지를 선정하기 위한 입지조건 정의, 설치개수 결정, 최적입지 선정의 과정을 순차적으로 결합한 방법론을 개발한다. 우선 입지조건으로는 블록화, 접근성, 지역 중심공공시설이 제시되며, 선정된 입지에 대해 set-covering모형과 p-median 모형을 적용하여 설치개수와 최적입지를 각각 결정한다. 이러한 과정을 실제 사례에 적용해 봄으로써 모형의 타당성과 실효성을 검증해 본다. 일반주택과 다세대주택으로만 구성된 안산의 한 지역을 대상으로 분석한 결과, 필요한 무인택배함은 2개로 결정되었으며, 5개 후보지 중 가장 적절한 2개 장소가 최적입지로 제시되었다. 이러한 방법론은 무인택배함 설치시 최적의 결정을 내리기 위한 가이드라인을 제공하며, 택배 관련 강력범죄 예방 및 택배이용 활성화를 통한 경제적 파급효과를 기대해 볼 수 있다.

Optimal location selection for the installation of urban green roofs considering honeybee habitats along with socio-economic and environmental effects

This study proposes a new framework for the selection of optimal locations for green roofs to achieve a sustainable urban ecosystem. The proposed framework selects building sites that can maximize the benefits of green roofs, based not only on the socio-economic and environmental benefits to urban residents, but also on the provision of urban foraging sites for honeybees. The framework comprises three steps. First, building candidates for green roofs are selected considering the building type. Second, the selected building candidates are ranked in terms of their expected socio-economic and environmental effects. The benefits of green roofs are improved energy efficiency and air quality, reduction of urban flood risk and infrastructure improvement costs, reuse of storm water, and creation of space for education and leisure. Furthermore, the estimated cost of installing green roofs is also considered. We employ spatial data to determine the expected effects of green roofs on each building unit, because the benefits and costs may vary depending on the location of the building. This is due to the heterogeneous spatial conditions. In the third step, the final building sites are proposed by solving the maximal covering location problem (MCLP) to determine the optimal locations for green roofs as urban honeybee foraging sites. As an illustrative example, we apply the proposed framework in Seoul, Korea. This new framework is expected to contribute to sustainable urban ecosystems.

Selection of Optimal Locations of Groundwater Tubular Wells based on Spatial Statistical Analysis Techniques

Objectives: To find optimal locations for groundwater tubular wells by analyzing the spatial areas of influence in GIS to assess groundwater tubular wells and analyze their development potential. Methods/Statistical Analysis: The candidates of new tubular well development was selected according to the procedure of the GIS spatial analysis based on the spatial data of location, contamination vulnerability, current land usage, and topographic map of tubular wells, the GIS analysis results based on overlapping and analysis of area of influence, union data, dissolve data, clip data, and DRASTIC index. Findings: The rational development and utilization of groundwater tubular wells, which are an approach to secure local water supply and are expected to offer a relatively stable water supply, is a very important topic to decision-makers at local governments. Previous studies on the development of groundwater focused on the contamination and utilization features of groundwater and can thus be used as important findings to solve environmental issues, but they do not reflect the needs of groundwater development from the perspective of supply right by putting together the local characteristics. This study thus set out to evaluate old tubular wells by analyzing the spatial areas of influence of subject areas and select optimal locations for tubular wells by analyzing their development potential, thus proposing candidate locations for tubular well development and water supply amounts and providing basic data needed to make plans for local water supply. Improvements/Applications: The meaningful findings regarding groundwater development can serve the staff members in charge of the affair at local governments as basic data.

Obtaining energy from tidal microturbines: A practical example in the Nalón River

Tidal energy has significant potential yet to be developed. Sizeable investments, operating costs and environmental impact are barriers that have to date restricted its development. However, microturbine arrays which are viable in coastal locations can help minimize the initial investment as well as operating costs and environmental impact. This paper puts forward a complete smart grid design based on tidal microturbine arrays following a methodology that includes a selection of optimal locations, the design of smart grid tidal generation and consumption components. The methodology has been applied to the estuary of the Nalón River. For the evaluation of the tidal resource and determination of the optimum locations free license software is used to simulate a one-dimensional flow. In order to achieve this, a geometrical model of the river mouth using open source geographic data and nautical information has been built. The results demonstrate high water velocities throughout the year as well as high tidal energy potential in zones that make the installation of a smart grid feasible. This smart grid includes a microturbine tidal array to generate energy and an intelligent Lighting Smart Grid system to light a promenade along the river mouth depicted as an application of the energy produced.

Optimal Selection of Number and Location of Meteo-Hydrological Monitoring Networks on Vu Gia – Thu Bon River Basin using GIS

Meteorological data play a particularly important role in hydrologic research because the climate and weather of an area exert a profound influence on most hydrologic processes. Meanwhile, hydrological data are critical for performing a range of purposes, including water resources assessment, impacts of climate change and flood forecasting and warning. It can be said that the prevention of disasters caused by floods and droughts would be impossible without rational forecasting technology based on an understanding of the rainfall-runoff phenomenon and statistical analysis of past hydrological data, which cannot be achieved without meteo-hydrological observations. The lack of adequate meteo-hydrological data affects the ability to model, predict and plan for catastrophic events such as floods and droughts which have obvious negative impacts on public health and socio-economic aspects. The accurate estimation of the spatial distribution of meteorological and hydrological parameters requires a dense network of instruments, which entails large installation and operational costs. It is thus necessary to optimize the number and location of meteo-hydrological stations. This paper presents a GIS-based approach to establishing an optimal meteo-hydrological station network on Vu Gia- Thu Bon river basin for developing an up-to-date real time flood warning system. Based on statistical analysis of the annual rainfall total data at 9 existing gauges in the study area from 1980 to 2013, it showed that the error of the existing network was about 7.47%. Considering 9 rain gauges as a standard representative of rainfall over the region, if the error decreases from 7.47% to 5%, the number of additional rain gauges should be 20. For adequate and economical network design, these additional rain gauges were spatially distributed between the different isohyetals after considering the relative distances between rain gauges, their accessibility, personnel required for making observations using multi-layers analysis and spatial interpolation. For hydrological stations, based on consideration existing network with the requirements set out by the flood warning system, the number of stations should be five. In terms of spatial distribution, three stations were distributed across two main tributaries of Vu Gia- Thu Bon river basin, behind the dams for water discharge calibration and the others were located on downstream for water stage calibration. The results of the study provided a scientific approach can be applied to optimizing the meteo-hydrological station network over the river basin.

Effect of product recovery and sustainability enhancing indicators on the location selection of manufacturing facility

The inclusion of economical, environmental, and societal issues in all stages of doing business helps to bring about sustainable development. A business begins or expands by establishing new facilities, so selecting a facility location is a strategic and crucial decision. In the context of sustainability, the selection of location for different facilities can be a critical problem, especially for manufacturing firms that endorse the wide footprint of Extended Producer Responsibility policies. This study aims at prioritizing alternative potential locations for manufacturing firms with respect to the three dimensions of sustainability identified above. The three dimensions are assessed by factors obtained through a factor analysis and are grouped by corresponding invariable sub criteria. These sub criteria are chosen from the extant literature review. Then, the preferred order of alternative potential location is obtained by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on each location's overall performance. The performance of each alternative potential location is assessed on the basis of overall weights of alternatives, evaluating factors, and triple bottom line attributes, which were obtained by Analytical Hierarchical Process (AHP). The multi criteria decision making technique, AHP, calculates the weights of the qualitative and quantitative criteria impacting the location selection problem. Then the approach of the study is validated by applying a case from real life; the results are justified by completing a sensitivity analysis on the relative importance weights of the three primary attributes (economical, environmental, and social). The results of the sensitivity analysis demonstrate an effective decision making technique for the optimal selection of sustainable manufacturing locations.

Simulated Annealing in Optimization of Energy Production in a Water Supply Network

In water supply systems, the potential exists for micro-hydropower that uses the pressure excess in the networks to produce electricity. However, because urban drinking water networks are complex systems in which flows and pressure vary constantly, identification of the ideal locations for turbines is not straightforward, and assessment implies the need for simulation. In this paper, an optimization algorithm is proposed to provide a selection of optimal locations for the installation of a given number of turbines in a distribution network. A simulated annealing process was developed to optimize the location of the turbines by taking into account the hourly variation of flows throughout an average year and the consequent impact of this variation on the turbine efficiency. The optimization is achieved by considering the characteristic and efficiency curves of a turbine model for different impeller diameters as well as simulations of the annual energy production in a coupled hydraulic model. The developed algorithm was applied to the water supply system of the city Lausanne (Switzerland). This work focuses on the definition of the neighborhood of the simulated annealing process and the analysis of convergence towards the optimal solution for different restrictions and numbers of installed turbines.

An improved hybrid multi-criteria/multidimensional model for strategic industrial location selection: Casablanca industrial zones as a case study.

In this paper, we examine the issue of strategic industrial location selection in uncertain decision making environments for implanting new industrial corporation. In fact, the industrial location issue is typically considered as a crucial factor in business research field which is related to many calculations about natural resources, distributors, suppliers, customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis with analytical capabilities that OLAP systems can provide for successful and optimal industrial location selection. The proposed model mainly consists in three stages. In the first stage, a decision-making committee has been established to identify the evaluation criteria impacting the location selection process. In the second stage, we develop fuzzy AHP software based on the extent analysis method to assign the importance weights to the selected criteria, which allows us to model the linguistic vagueness, ambiguity, and incomplete knowledge. In the last stage, OLAP analysis integrated with multi-criteria analysis employs these weighted criteria as inputs to evaluate, rank and select the strategic industrial location for implanting new business corporation in the region of Casablanca, Morocco. Finally, a sensitivity analysis is performed to evaluate the impact of criteria weights and the preferences given by decision makers on the final rankings of strategic industrial locations.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-1404-x) contains supplementary material, which is available to authorized users.

Optimal well placement and brine extraction for pressure management during CO2 sequestration

Large-scale pressure increases resulting from carbon dioxide (CO2) injection in the subsurface can potentially impact caprock integrity, induce reactivation of critically stressed faults, and drive CO2 or brine through conductive features into shallow groundwater. Pressure management involving the extraction of native fluids from storage formations can be used to reduce such pressure increases. However, dealing with large volumes of extracted brine can be technically challenging and expensive. Selection of optimal well locations and pumping rates are critical for maximizing CO2 storage and minimizing brine extraction during geologic CO2 sequestration (GCS). Robust and efficient computerized algorithms combining reservoir models and optimization methods are needed to make proper decisions on well placement and pumping rates. This study presents a constrained differential evolution (CDE) algorithm for solving global optimization problems involving pressure management of GCS projects. Application of the CDE optimization methodology was demonstrated for a hypothetical CO2 storage scenario in a deep sandstone reservoir in the Southern San Joaquin Basin in California, USA. Industrial-scale storage of CO2 would generate significant pressure buildup in this formation, which in turn would raise concerns about induced seismicity due to presence of multiple faults surrounding the injection site. Through the CDE optimization algorithm coupled to a vertically-averaged reservoir simulator, we successfully estimated optimal solutions for brine extraction wells in the reservoir that would limit the local pressure along the faults to a prescribed threshold. Multiple realizations of the reservoir permeability field were created to understand the impact of reservoir heterogeneity on optimization results. Our results indicate that the reservoir slope and heterogeneity have significant impact on optimum extraction rates. Reservoir heterogeneity is also a significant factor for extraction well locations, suggesting that in practice decisions about extraction well placement through optimization should be made at later project stages when data from a few years of CO2 injection have allowed iterative updating and refining of the reservoir forward models. Although the study focused on optimization of brine extraction, the CDE optimization methodology presented in this paper has also potential to solve other complex optimization problems related to GCS, such as increasing storage efficiency by enhancing injectivity and capillary and dissolution trapping.

Monte Carlo optimization for site selection of new chemical plants

Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies.

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads.

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.

Developing Operational Optimization Strategies Through Optimal Location Selection for the Logistics Center -Based on a Case Study of a Fresh Food Provider-

Developing Operational Optimization Strategies Through Optimal Location Selection for the Logistics Center -Based on a Case Study of a Fresh Food Provider-

Optimal Location, Sizing, and Appropriate Technology Selection of Distributed Generators for Minimizing Power Loss Using Genetic Algorithm

Genetic algorithm (GA) is utilized to select most suitable Distributed Generator (DG) technology for optimal operation of power system as well as determine the optimal location and size of the DG to minimize power loss on the network. Three classes of DG technologies, synchronous generators, asynchronous generators, and induction generators, are considered and included as part of the variables for the optimization problem. IEEE 14-bus network is used to test the applicability of the algorithm. The result reveals that the developed algorithm is able to successfully select the most suitable DG technology and optimally size and place the DGs to minimize power loss in the network. Furthermore, optimum multiple placement of DG is considered to see the possible impact on power loss in the network. The result reveals that multiple placements can further reduce the power loss in the network.

Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China

Optimal selection of observation locations is an essential task in designing an effective ecohydrological process monitoring network, which provides information on ecohydrological variables by capturing their spatial variation and distribution. This article presents a geostatistical method for multivariate sampling design optimization, using a universal cokriging (UCK) model. The approach is illustrated by the design of a wireless sensor network (WSN) for monitoring three ecohydrological variables (land surface temperature, precipitation and soil moisture) in the Babao River basin of China. After removal of spatial trends in the target variables by multiple linear regression, variograms and cross-variograms of regression residuals are fit with the linear model of coregionalization. Using weighted mean UCK variance as the objective function, the optimal sampling design is obtained using a spatially simulated annealing algorithm. The results demonstrate that the UCK model-based sampling method can consider the relationship of target variables and environmental covariates, and spatial auto- and cross-correlation of regression residuals, to obtain the optimal design in geographic space and attribute space simultaneously. Compared with a sampling design without consideration of the multivariate (cross-)correlation and spatial trend, the proposed sampling method reduces prediction error variance. The optimized WSN design is efficient in capturing spatial variation of the target variables and for monitoring ecohydrological processes in the Babao River basin.

Comparative evaluation of newborn bloodspot specimen cards by experienced laboratory personnel and by an optical scanning instrument

A major factor in determining the suitability of a dried blood spot (DBS) specimen is the subjective nature of evaluation by laboratory personnel. Using newborn screening DBS specimen cards as they were submitted to a public health NBS program, we conducted a systematic pilot study of DBS evaluation by multiple experienced laboratory personnel (ELP) and by an automated optical scanning instrument (OSI) (CardScan (tm), BSD Robotics). OSI confirmed the satisfactory status of all newborn DBS specimen cards that passed initial review by the first ELP. Among the questionable cards selected for further review, 58% passed multiple ELP consensus assessment, and 62% passed OSI evaluation. The overall agreement between ELP and OSI was 86%. Among questionable specimen cards, ELP and OSI were more strongly correlated when multiple ELP assessment was unanimous. We conclude that subjective assessment by ELP is essential and that OSI evaluation is a useful adjunct when ELP assessment does not reach consensus. OSI further allows the selection of optimal locations for punching DBS from unsatisfactory or questionable specimens, optimizing the quality of interim analyses that may be conducted while repeat specimens are being collected. Instrument evaluation of specimen cards would also be valuable as an independent reference method for training laboratory and specimen collection personnel. OSI technology merits further studies to confirm and extend our findings.

Optimal selection of aggregation locations for participatory sensing by mobile cyber-physical systems

Mobile cyber-physical systems entail large volumes of data collected by various sensors and cameras embedded in vehicles and mobile devices. Participatory sensing is a model widely encouraged by cyber-physical systems (CPS) to provide a smart knowledge base to consumers using various sensed data contributed by voluntary participants. Aggregation is a viable approach to convert the sensed data into a usable form. An efficient way to perform aggregation is to divide the network into a number of geographical regions, called aggregation regions. In this paper, we propose a novel mechanism to determine aggregation regions based on the location of Access Points (APs). Besides, we propose a strategy to determine the optimal location of aggregation that minimizes the delay of dissemination of sensed data.