• Water for future
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• v.12 no.2
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• pp.70-76
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• 1979

This study aims at rationalization of assessment method of the output such as power generation and water supply avaiable from a multipurpose dam project in its optimization study. The computer program proposed in this stuidy has much contributed toward rationalization of the optimization study of Chungju Multipurpose Dam Project saving much labor as well as time. This computer program which has been developed in this study may be also applicable to other porject similar to Chungju Project.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.6
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• pp.7-13
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• 2002

This paper introduced a new hybrid genetic algorithm, verified its performance, and applied it to the optimization of blade design and pitch control for 30kW pitch-controlled variable-speed horizontal-axis wind turbine system to determine the optimum blade chord and twist distributions that maximize the energy production for a given Weibull wind distribution and the optimum blade pitch angles that maintain constant power output.

• Nuclear Engineering and Technology
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• v.43 no.4
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• pp.383-390
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• 2011

Nuclear power has become an essential part of electricity generation to meet the continuous growth of electricity demand. A Sodium-cooled Fast Reactor (SFR) was developed to extend uranium resource utilization under a growing nuclear energy scenario while concomitantly providing a nuclear waste management solution. Key questions in this scenario are when to introduce SFRs and how many reactors should be introduced. In this study, a methodology using Linear Programming is employed in order to quantify an optimized growth pattern of a nuclear energy system comprising light water reactors and SFRs. The optimization involves tradeoffs between SFR capital cost premiums and the total system U3O8 price premiums. Optimum nuclear growth patterns for several scenarios are presented, as well as sensitivity analyses of important input parameters.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.814-823
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• 2012

Electric power transmission utilities make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability. The development of maintenance strategies for aged components is one of the more effective ways to achieve this goal. The reliability centered approach is a key method in providing optimal maintenance strategies. It considers the tradeoffs between the upfront maintenance costs and the potential costs incurred by reliability losses. This paper discusses the application of the Particle Swarm Optimization (PSO) technique used to find the optimal maintenance strategy for a transmission component in order to achieve the minimum total expected cost composed of Generation Cost (GC), Maintenance Cost (MC), Repair Cost (RC) and Outage Cost (OC). Three components of a transmission system are considered: overhead lines, underground cables and insulators are considered. In regards to aged and aging component, a component state model that uses a modified Markov chain is proposed. A simulation has been performed on an IEEE 9-bus system. The results from this simulation are quite encouraging, and then the proposed approach will be useful in practical maintenance scheduling.

• Electronic Materials Letters
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• v.14 no.6
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• pp.718-724
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• 2018

With the development of photovoltaic industry, the cost of photovoltaic power generation has become the significant issue. And the metallization process has decided the cost of original materials and photovoltaic efficiency of the solar cells. Nowadays, double printing process has been introduced instead of one-step printing process for front contact of polycrystalline silicon solar cells, which can effectively improve the photovoltaic conversion efficiency of silicon solar cells. Here, the relative cheap Cu paste has replaced the expensive Ag paste to form Ag/Cu composite front contact of silicon solar cells. The photovoltaic performance and the cost of photovoltaic power generation have been investigated. With the optimization on structure and height of Cu finger layer for Ag/Cu composite double-printed front contact, the silicon solar cells have exhibited a photovoltaic conversion efficiency of 18.41%, which has reduced 3.42 cent per Watt for the cost of photovoltaic power generation.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.1-4
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• 2008

This paper reports the design of a multiwavelength fiber laser source that utilizes a linear cavity of hybrid Brillouin/Erbium fiber laser (BEFL). The output power, threshold power and free running cavity modes were investigated against the pump powers. The structure exhibited low threshold operation of 4 mW at 2.3 mW injected Brillouin pump power. The optimization of Brillouin pump wavelength, power and Erbium gain led to a maximum possible number of channels generated. Simultaneous and stable operation of 21 channels with 10.88 GHz channels spacing were obtained from this architecture at 1 mW injected Brillouin pump power and 90 m W Erbium doped fiber pump power in the 1555 nm region.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.560-566
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• 2008

Today, as the power trades between generation companies and power customer are liberalized, the uncertainty level of operated power system is rapidly increased. Therefore, transmission operators as decision makers for transmission expansion are required to establish a deliberate investment plan for effective operations of transmission facilities considering forecasted conditions of power system. This paper proposes the methodology for the optimal solution of transmission expansion in deregulated power system. The paper obtains the expected value of transmission congestion cost for various scenarios by using occurrence probability. In addition, the paper assumes that increasing rates of loads are the probability distribution and indicates the location of expanded transmission line, the time for transmission expansion with the minimum cost for the future by performing the Montecarlo simulation. To minimize the investment risk as the variance of the congestion cost, Mean-Variance Markowitz portfolio theory is applied to the optimization model by the penalty factor of the variance. By the case study, the optimal solution for transmission expansion plan considering the feature of market participants is obtained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1504-1511
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• 2009

By development of renewable energies and high-efficient facilities and deregulated electricity market, the operation cost of distributed generation(DG) becomes more competitive. The amount of distributed resource is considerably increasing in the distribution network consequently. Also, international environmental regulations of the leaking carbon become effective to keep pace with the global efforts for low-carbon paradigm. It contributes to spread out the business of DG. Therefore, the operator of DG is able to supply electric power to customers who are connected directly to DG as well as loads that are connected to entire network. In this situation, community energy system(CES) having DGs is recently a new participant in the energy market. DG's purchase price from the market is different from the DG's sales price to the market due to the transmission service charges and etc. Therefore, CES who owns DGs has to control the produced electric power per hourly period in order to maximize the profit. If there is no regulation for carbon emission(CE), the generators which get higher production than generation cost will hold a prominent position in a competitive price. However, considering the international environment regulation, CE newly will be an important element to decide the marginal cost of generators as well as the classified fuel unit cost and unit's efficiency. This paper will introduce the optimal operation of CES's DG connected to the distribution network considering CE. The purpose of optimization is to maximize the profit of CES and Particle Swarm Optimization (PSO) will be used to solve this problem. The optimal operation of DG represented in this paper is to be resource to CES and system operator for determining the decision making criteria.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2227-2235
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• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1098_1099
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• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.