• Proceedings of the KAIS Fall Conference
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• 2002.11a
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• pp.141-144
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• 2002

Recently, the operation of power distribution systems has become more difficult because the peak demand load is increasing continuously and the daily load factor is getting worse and worse. Also, the consideration of deregulation and global environment in electric power industry is required. In order to overcome these problems, a study on the planning and operation in distribution systems of dispersed generating sources such as fuel cell systems, photovoltaic systems and wind power systems has been performed energetically. This study presents a method for determining an optimal operation strategy of dispersed co-generating sources, especially fuel cell systems, in the case of both only electric power supply and thermal supply as well as electric power supply. In other words, the optimal operation of these sources can be determined easily by the principle of equal incremental fuel cost and the thermal merits is evaluated quantitatively through Kuhn-Tucker's optimal conditions. In order to select the optimal locations of those sources, an priority method using the comparison of total cost at the peak load time interval is also presented. The validity of the proposed algorithms is demonstrated using a model system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.4
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• pp.185-194
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• 2000

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied in order to achieve the best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2414-2419
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• 2012

This paper presents an optimal operation control method for energy saving in the water reuse pumping system. A predictive horizon switching strategy is proposed to implement an optimal operation control and a linear programming (LP) algorithm is used to solve optimal problems in each time step. Energy costs are calculated for electricity on both TOU in the light, heavy, and maximum load time period and peak charges. The optimal operation in water reuse pumping systems is determined to reduce the TOU and peak costs. The simulation results show a power energy saving for water reuse pumping systems and power stability improvement.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1279-1281
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• 1999

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied to achieving a best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1801-1810
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• 2016

The OpenDaylight controller has been receiving significant attention as one of the enabling open source framework for SDN, and this paper analyzes the architecture and procedure of OpenDaylight based controller cluster. The OpenDaylight controller cluster uses shard based distributed datastore and Raft algorithm to maintain consistency among controllers inside a cluster. The performance evaluation analyzes the leader re-election time as well as latencies of CRUD and Routed RPC according to cluster size, shard role, and sharding strategy, and we discuss the optimal operation strategy for OpenDaylight controller cluster.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.67-69
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• 2000

This paper deals with a method for determining an optimal operation strategy of dispersed generating sources. For effective utilization of dispersed generating sources, it is indispensable to consider their thermal merits in addition to electric power. And then the optimal operation of these sources can be determined easily by the principle of equal incremental fuel cost. This paper presents an priority method to decide the optimal location of those sources in power systems about the whole year. The validity of the proposed algorithms are demonstrated using a model system.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.183-186
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• 2001

This paper describes the optimal operation scheduling of total cogeneration system which is interconnected with combined heat power plant of utility and district heat devices. The numerical modeling about the cogeneration system and the auxiliary thermal energy devices are established and simulation is carried out by LINDO program in order to minimize the operation cost under the national viewpoint. The results reveal that the established numerical modeling and the operation strategy can be effectively applied to the total cogeneration systems to reduce the energy cost.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.217-224
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• 1998

During the operation of crane system in container yard, the objective is to transport the load to a goal position as quick as possible without rope oscillation. The container crane is generally operated by an expert operator, but recently an automatic control system with high speed and rapid transportation is required. Therefore, we developed an optimal controller which has to control the crane system with disturbances. In this paper, we present a design of optima 2-DOF PID controller for the control of gantry crane which has to control swing motion and trolley position. We used evolution strategy(ES) to tune the parameters of 2-DOF PID controller. It was compared with general PID controller. The computer simulations show that the proposed method has better performances than the other method.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.715-718
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• 2009

The assembly line is one of the typical process hard to analyze with mathematical methods including even stochastic approaches, because it includes many manual operations varying drastically depending on operators' skills. In this paper, we suggest the simulation optimization method to design the optimal assembly line of a gas oven. To achieve the optimal design, firstly, we modeled the real gas oven assembly line with actual data, such as assembly procedures, operation rules, and other input parameters and so on. Secondly, we build some alternatives to enhance the line performance based on business rules and other parameters. The DOE(Design Of Experiment) techniques were used for testing alternatives under various situations. Each alternatives performed optimization process with evolution strategy; one of the GA(Genetic Algorithm) techniques. As a result, we can make about 7% of throughputs up with the same time and cost. By this process, we expect the assembly line can obtain the solution compatible with their own problems.