• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.1
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• pp.113-123
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• 2007

This study analyzed the appropriateness of the spatial distribution of fire stations and emergency medical facilities, the main providers of emergency medical care, in Busan. The area over which the 119 emergency medical services were situated in relation to the dispatch and transport of urgent rescue services was examined. Addresses of patients requiring 119 emergency services were obtained and stored as individual units so that they could be analyzed in a Geographic Information System(GIS). The time taken by emergency services to reach patients and transport them to a hospital or other facility was measured in seconds. By inputting additional information such as the location of the 119 dispatch centers, jurisdictions, and emergency medical facilities, the GIS allowed for analyses not only of the temporal but also the spatial aspects of emergency medical services. The results showed that of 16 Gu/Gun and 226 Eup/Myen/Dong in the Busan area, only 41% of Busan's emergency medical services could respond to and transport patients within five minutes. In all districts, most emergency medical services were provided within five to ten minutes. However, the pattern of hospital use to transfer patients to hospitals was inefficient. Based on the temporal and spatial distributions of fire stations and emergency medical agencies, and on their dispatch and transport times, this study sets out and compares ideal dispatch and transportation patterns for the efficient use of Busan's emergency medical services and resources.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.667-673
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• 2021

Purpose: The aim of this study is to analyze the trend for the dispatch of the 119 rescue teams to remove the beehive according to the distribution of temperature and time in Busan metropolitan city for 5 years from 2015. Method: From January 2015 to December 2019, 11 fire stations in Busan were dispatched and the source data of rescue and emergency activities were collected. The number of beehive removal dispatches was determined by the Busan Metropolitan Fire Station's jurisdiction over the past five years, and the temperature meteorological factors and honeycomb removal dispatches were analyzed in frequency and percentage. Result: The frequency of dispatch began to increase at an monthly average temperature of more than 20℃ and was higher at 23℃ to 29℃ than other temperature range. The highest frequency of dispatch was 7,900 cases in 2017. In particular, we found that the start timing of the honeycomb removal is getting faster as the year goes by. Gijang-gun had the largest frequency of dispatch, and Haeundae-gu, Geumjeong-gu, and Nam-gu were found to have a higher that. Conclusion: We found that the start timing of the honeycomb removal is getting faster as the year goes by and temperature changes. The results of this study are considered to be useful in future studies of wasps in urban areas.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.329-337
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• 2016

With the increasing deployment of distributed generators in the distribution system, a very large search space is required when dynamic programming (DP) is applied for the optimized dispatch schedules of voltage and reactive power controllers such as on-load tap changers, distributed generators, and shunt capacitors. This study proposes a new optimal voltage and reactive power scheduling method based on dynamic programming with a heuristic searching space reduction approach to reduce the computational burden. This algorithm is designed to determine optimum dispatch schedules based on power system day-ahead scheduling, with new control objectives that consider the reduction of active power losses and maintain the receiving power factor. In this work, to reduce the computational burden, an advanced voltage sensitivity index (AVSI) is adopted to reduce the number of load-flow calculations by estimating bus voltages. Moreover, the accumulated switching operation number up to the current stage is applied prior to the load-flow calculation module. The computational burden can be greatly reduced by using dynamic programming. Case studies were conducted using the IEEE 30-bus test systems and the simulation results indicate that the proposed method is more effective in terms of saving electric charges and improving the voltage profile than loss minimization.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.9-16
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• 2012

The present study presents the Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm, an effective optimization technique, the multi-dimensional vectors of which consist of magnitudes and phase angles. PDPSO is employed in the configuration of micro-grids. Micro-grids are concepts of distribution system that directly unifies customers and distributed generations (DGs). Micro-grids could supply electric power to customers and conduct power transaction via a power market by operating economic dispatch of diverse cost functions through several DGs. If a large number of micro-grids exist in one distribution system, the algorithm needs to adjust the configuration of numerous micro-grids in order to supply electric power with minimum generation cost for all customers under the distribution system.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.152-156
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• 2007

This paper proposes a method for real-time control of both capacitors and ULTC in a distribution system to reduce the total power loss and to improve the voltage profile over the course of a day. The multi-stage consists of the off-line stage to determine dispatch schedule based on a load forecast and the on-line stage generates the time and control sequences at each sampling time. It is then determined whether one of the control actions in the control sequence is performed at the present sampling time. The proposed method is presented for a typical radial distribution system with a single ULTC and capacitors.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.298-299
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• 2011

The impacts of EV charging demands on power system such as increased peak demands may be developed by means of modeling a stochastic distribution of charging and a demand dispatch calculation. Optimization processes are proposed to determine optimal demand distribution portions so that charging costs and demands can be managed optimally. There are two optimization methods which have different effects on the outcome. These focus either on the Electric vehicle customer side (cost optimization) or the System Operator side (Load-weighted optimization).

• 한국디지털정책학회:학술대회논문집
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• 2006.12a
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• pp.379-386
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• 2006

The purpose of this paper is to analyze the implementation of the GIS infrastructure systems for the u-City, GIS base u-City represents spatial information derived fields, such as geographical distribution of the urban boundaries, physical configuration of the urban locations and cultural characteristics of the urban history. These three aspects relate to urban infrastructure systems implementation, urban monitoring center implementation, and spatial database implementation. In terms of the GIS based u-Ci쇼 urban infrastructure implementation systems, the u-City depends on IT contents and spatial features. IT contents are strongly related to IT839 strategy due to the national agenda is "u-Korea". GIS should contribute to u-City construction through the spatial analyses methods. For these methods various GIS functions will guide to u-City's distribution, location, and characteristics of urbanization. The infrastructure consists of road and road facilities, underground facilities, related agencies facilities, dispatch systems, environmental systems, and urban planning. These six units of the urban infrastructures have spatial databases that consist of spatial configuration, such as dots, lines, and polygons in order to draw the spatial distribution of the u-City GIS based u-City urban infrastructure implementation systems should deal with It convergence to generate fusion affects.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.783-790
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• 2012

Electric Vehicles(EVs) and Plug-in Hybrid Electric Vehicles(PHEVs) which have the grid connection capability, represent an important power system issue of charging demands. Analyzing impacts EVs charging demands of the power system such as increased peak demands, developed by means of modeling a stochastic distribution of charging and a demand dispatch calculation. Optimization processes proposed to determine optimal demand distribution portions so that charging costs and demand can possibly be managed. In order to solve the problems due to increasing charging demand at the peak time, alternative electricity rate such as Time-of-Use(TOU) rate has been in effect since last year. The TOU rate would in practice change the tendencies of charging time at the peak time. Nevertheless, since it focus only minimizing costs of charging from owners of the EVs, loads would be concentrated at times which have a lowest charging rate and would form a new peak load. The purpose of this paper is that to suggest a scenario of load leveling for a power system operator side. In case study results, the vehicles as regular load with time constraints, battery charging patterns and changed daily demand in the charging areas are investigated and optimization results are analyzed regarding cost and operation aspects by determining optimal demand distribution portions.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.1-12
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• 2009

In recent years, integration of new distributed generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. This paper presents a dynamic methodology of optimal allocation and sizing of DG units for a given practical distribution network, so that the cost of active power can be minimized. The approach proposed is based on a combined Genetic/Fuzzy Rules. The genetic algorithm generates and optimizes combinations of distributed power generation for integration into the network in order to minimize power losses, and in second step simple fuzzy rules designs based upon practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensator (SVC, STATCOM) in order to improve the system loadability. This proposed approach is implemented with the Matlab program and is applied to small case studies, IEEE 25-Bus and IEEE 30-Bus. The results obtained confirm the effectiveness in sizing and integration of an assigned number of DG units.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.