• 한국정보시스템학회지:정보시스템연구
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• 제28권1호
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• pp.133-153
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• 2019

Purpose The purpose of this study is to design a regional electricity planning model rather than the existing single region ones and verify its usefulness. The regional electricity planning model is to determine both electricity distribution among regions and power plant planning at the same time satisfying regional demands and distribution networks. Design/methodology/approach This study made a regional electricity planning model by integrating power plant planning and electricity distribution among regions. The regional electricity planning model is formulated into a linear programming problem, and coded and run using the OSeMOSYS, one of open source energy systems. Findings According to the empirical analysis result, this study confirmed that the regional electricity planning model proposed in this study deducts the unfairness among regions in view of electricity and green house gas. In addition, the model is expected to be used in evaluating and developing the national policies concerning fine dust and/or green house gas.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.433-440
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• 2014

In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and $CO_2$ emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.20-28
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• 2008

This paper proposes a method for solving distribution system planning problems taking into account demand uncertainty and geographical information. The proposed method can automatically select appropriate location and size of a substation, routing of feeders, and appropriate sizes of conductors while satisfying constraints, e.g. voltage drop and thermal limit. The demand uncertainty representing load growth is modeled by fuzzy numbers. Feeder routing is determined with consideration of existing infrastructure, e.g. streets and canals. The method integrates planner's experience and process optimization to achieve an appropriate practical solution. The proposed method has been tested with an actual distribution system, from which the results indicate that it can provide satisfactory plans.

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.58-66
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• 2001

This paper presents an application of a newly designed Adaptive Genetic Algorithm (AGA) to solve the Optimal Feeder Routing (OFR) problem for distribution system planning. The main objective of the OFR problem usually is to minimize the total cost that is the sum of investment costs and system operation costs. We propose a properly designed AGA, in this paper, which can handle the horizon-year expansion planning problem of power distribution network in which the location of substation candidates, the location and amount of forecasted demands are given. In the proposed AGA, we applied adaptive operators using specially designed adaptive probabilities. we also a Simplified Load Flow (SLF) technique for radial networks to improve a searching efficiency of AGA. The proposed algorithm has been evaluated with the practical 32, 69 bus test system to show favorable performance. It is also shown that the proposed method for the OFR can also be used for the network reconfiguration problem in distribution system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1453-1455
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• 1999

This paper presents an economical generation planning for operating power systems with dispersed generation. As dispersed generation introduced into an electric power distribution system, the power system will be complicated and have much variable aspects. So there is need for developing new generation scheduling. In this paper, the proposed method is tested for distribution system with two battery storage resources. Optimal generation planning of 15 thermal units in 24-hours is achieved by improved genetic algorithm. Also, to show its effectiveness, the results are compared with those of not including battery storage resources.

• KEPCO Journal on Electric Power and Energy
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• 제5권3호
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• pp.235-237
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• 2019

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.480-482
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• 2000

The KEPCO is developing the practical power distribution operating system. The system adopt Genetic Algorithm and will be used loss reduction, load balancing, service planning for large capacity load and various kinds of simulations in the distribution power system. This paper presents the some obstacles and solutions on practical simulation system development, and some problems that need more study.

• KEPCO Journal on Electric Power and Energy
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• 제7권1호
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• KIEE International Transactions on Power Engineering
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• 제5A권1호
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• pp.9-15
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• 2005

In the operation of distribution systems, DGs (Distributed Generations) are installed as an alternative to extension and the establishment of substations, transmission and distribution lines according to the increasing power demand. In the operation planning of DGs, determining optimal capacity and allocation achieves economical profitability and improves the reliability of power distribution systems. This paper proposes a determining method for the optimal number, size and allocation of DGs in order to minimize the operation costs of distribution systems. Capacity and allocation of DGs for economical operation planning duration are determined to minimize total cost composed with power buying cost, operation cost of DGs, loss cost and outage cost using the GA (Genetic Algorithm).

• 한국초전도ㆍ저온공학회논문지
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• 제6권2호
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• pp.15-19
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• 2004

As the power demand has increased and power industry deregulation has progressed, the transmission and distribution system will have more complicated problems by the influence of investment reduction and NIMBY phenomena for overall power system. It is expected that the route length per MW demand will reduce gradually from 0.6[C-km/MW] to 0.53 [C-km/MW] in 2010. This comes up to a real serious problem of system planning and operational viewpoints. HTS technologies related to power system have properties to solve these complex transmission and distribution constraints, especially for metropolitan area, in the future. As the HTS technology has developed, the HTS cable technology can be the most effective alternative to solve the future expected power network constraints. This paper describes the application methodology of developing 22.9㎸ HTS cable by CAST for practical distribution system. 22.9㎸ HTS cable under development with step-by-step application methodology can substitute the existing and planning conventional 154㎸ cable. If this scheme is applied, part of downtown 154㎸ substation of metropolitan city such as Seoul can be changed into 22.9㎸ switching station. It can give great economic, environmental and additional benefits to all of the concerned authorities.