• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.82-85
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• 2006

With the fast growing number of skyscrapers and large ultrahigh apartment complexes, the concerns on fire caused by electricity also grow. Among about 30,000 fires recorded annually, roughly one third of them are hewn to be caused by electricity. If one of such high and densely populated buildings or apartments catches a fire, the consequence can potentially be quite catastrophic. However, with the rapid development of the techniques in the fields of communications and computers, electric power distribution systems for such buildings and apartments have been largely digitalized in recent years. More detailed informations on the operating status are now available, which enables more sophisticated monitoring and early detection of potential fire caused by electricity. In this paper, we present an inference technique that can be used as one of the basic techniques in building intelligent distribution panel systems that can effectively monitor, prevent and detect the occurrence of fire caused by electricity. The technique can accommodate production rules in linguistic expressions on high abstraction levels. Fire finding strategies can be easily modified to provide more effective countermeasures. Simulation results show that inference capabilities and thus the capability of fire monitoring in power distribution panel systems can be significantly enhanced with our approach.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.8
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• pp.418-428
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are newly defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.3
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• pp.254-259
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• 2016

Recently, DC distribution systems have become a hot issue because of the increase in digital loads and DC generation systems according to the expansion of renewable energy technologies. To obtain the practical usage of DC electricity, safety should be guaranteed. The main concerns for safety are twofold: one side is human protection against electric shocks, and the other is facility protection from short faults. "Effects of current on human beings and livestock" (IEC 60479) defines a human body impedance model in electric shock conditions that consists of resistive components and capacitive components. Although the human body impedance model properly works in AC electricity, it does not well match with the electric shock behavior in DC electricity. In this study, the contradiction of the human body impedance model defined by IEC 60479 in case of DC electricity is shown through experiments for the human body. From the analysis of experimental results, a novel unified human body impedance model in electric shock conditions is proposed. This model consists of resistive components, capacitive components, and an inductance component. The proposed human impedance model matches well for AC and DC electricity environments in simulation and experiment.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.627-640
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• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1125-1129
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• 2015

Increasing peak load is one of the major concerns about operation of urban railroad systems. Since ESSs (Energy Storage Systems) have a great potential for shaving the peak load, there has been a growing interest in the use of ESS for peak load reduction. Also, ESS can be optimally scheduled to minimize the electricity purchasing cost under a given ToU (Time-of-Use) tariff by taking advantage of electricity price difference between peak and off-peak time. This paper presents a Mixed Integer Programming (MIP)-based ESS scheduling method to minimize the electricity purchasing cost under a ToU tariff for an urban railroad system.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2004.05a
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• pp.353-358
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• 2004

In recent, the trends in national energy Policy are established in the context of the integrated risk estimation for various national electricity generating options. The approach takes account of health, environmental, economic, and social aspects of electricity generation systems. In the present work, nuclear, coal, and LNG sources are chosen because these hold more than 90% of national total electricity generation in a descending order. A life cycle assessment (LCA) methodology is used for comparing environmental impacts of these options during the life cycle such as construction, operation as well as disposal stages. Here, the LCA consists of life cycle inventory analysis, classification/selection process of impact categories, characterization process, and normalization process of each category. LCA can be an useful tool for environmental impact assessment of future national energy options. At the planning stage of future energy Policies, the results of LCA would be taken into consideration. According to data update at the construction and disposal stages, the LCA needs to be conducted iteratively.

• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.15-20
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• 2008

The objectives of this study are to analyze the energy independence plan and to propose a suitable sewage treatment plant in Korea. The total amount of electricity consumption for public sewage treatment plant was estimated as 1,182 GWh in 2007. It was estimated that total 16 sewage treatment plants with renewable energy systems produced electricity of 15.2 GWh per year, which could replaced 0.8% of total electricity used for sewage treatment. It was found that domestic sewage treatment plants with power generation plants by digestion gas were installed in 7 places and produced electricity of 13 GWh per year. It was also found that the power generation plants by digestion gas were the most cost-effective for sewage treatment out of the renewable energy systems based on the benefit-cost analysis.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.25-37
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• 2015

The early studies indicated that the firm with monopoly power is likely to engage in X-inefficiency such as a managerial slack. The reflection of the X-inefficiency theory has led to the issue that the public sector may be more inefficient than the private sector. In Korea like other many countries the electricity gas water service which can be considered as natural monopoly have been provided mostly by the public sector. In order to provide the empirical evidence to the argument that the public sector may be more inefficient than the private sector this paper estimated the four types of Solow residual which is called the total factor productivity in the electricity gas water service industry with the associated empirical model and compared its productivity with one in the manufacturing industry. The empirical results do not support the argument that the public sector may be more inefficient or less productive than the private sector.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.1-4
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• 2022

The growth in global population and industrialization has led to an increasing demand for electricity. Accordingly, the electricity providers need to increase the electricity generation. Due to the economical and environmental concerns associated with the generation of electricity from fossil fuels. Alternative power recourses that can potentially mitigate the economical and environmental are of interest. Renewable energy resources are promising recourses that can participate in producing power. Among renewable power resources, solar energy is an abundant resource and is currently a field of research interest. Photovoltaic solar power is a promising renewable energy resource. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. this paper investigates the utilization of machine learning unsupervised neural network techniques that potentially improves the reliability of PV solar power systems during integration into the electrical grid.

• Korean Journal of Chemical Engineering
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• v.36 no.1
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• pp.12-20
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• 2019

An energy storage system consisting of a battery and a power-to-methanol (PtM) unit was investigated to develop an energy storage system for renewable energy systems. A nonlinear programming model was established to optimize the energy storage system. The optimal installation capacities of the battery and power-to-methanol units were determined to minimize the cost of the energy system. The cost from a renewable energy system was assessed for four configurations, with or without energy storage units, of the battery and the power-to-methanol unit. The proposed model was applied to the modified electricity supply and demand based on published data. The results show that value-adding units, such as PtM, need be included to build a stable renewable energy system. This work will significantly contribute to the advancement of electricity supply and demand management and to the establishment of a nationwide policy for renewable energy storage.