• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.347-353
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• 2017

In order to prohibit global warming, various kinds of regulatory policies have been established in the whole world. One example is the establishment of the Renewable Portfolio Standard. It requires the increased portion in energy production from renewable energy sources. The Republic of Korea adopted the act on the promotion of the development, use, and diffusion of new and renewable energy since 2012. However, in spite of the effort on the consideration of the renewable energy sources, it was reported the carbon intensity of electricity in Korea was not that low in 2015. Thus, it is required to examine the recent state of the utilization degree of the renewable energy sources in Korea. This paper analyzed the statistical data provided by Korea Power Exchange (KPX) to examine any problems and solutions for generating electricity from the renewable energy sources. We focused on the generation capacity provided by the power plants participated in the market, the electric power trading amount, and the utilization coefficient for 10 years. By analyzing the data, we provide an alternative to solve some imbalance among the factors contributing to renewable energy use.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.2
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• pp.85-93
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• 2006

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. In an electricity market the short-term market price affects considerably the short-term trading between the market entities. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a new methodology for a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) model based on the time-series method. And also the correction algorithm is proposed to minimize the forecasting error in order to improve the efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the case studies are performed using historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.1-6
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• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.475-485
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• 2017

This paper develops a conversion function and method transforming from daily peak load curve used $LOLE_D$ [days/year] to hourly load curve used $LOLE_H$[hours/year]and describes relationship between $LOLE_D$ [days/year] and $LOLE_H$ [hours/year]. The indices can not only be transformed just arithmetically but also have different characteristics physically because of using their different load curves. The conversion function is formulated as variables of capacity and forced outage rate of generator, hourly load daily load factor and daily peak load yearly load factor, etc. Therefore, the conversion function (${\gamma}={\varphi}$(.)) can not be simple. In this study, therefore, the function is formulated as linear times of separated two functions. One is an exponential formed conversion function of daily load factor. Another is formulated with an exponential typed conversion function of daily peak load yearly load factor. Futhermore, this paper presents algorithm and flow chart for transforming from $LOLE_D$[days/year] to $LOLE_H$[hours/year]. The proposed conversion function is applied to sample system and actual KPS(Korea Power System) in 2015. The exponent coefficients of the conversion functions are assessed using proposed method. Finally, assessment errors using conversion function for case studies of sample system and actual system are evaluated to certify the firstly proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.23-30
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• 2017

In the AEC(Architecture, Engineering and Construction) industry, BIM(Building Information Modeling) technology not only helps design intent efficiently, but also realizes an object-oriented design including building's life cycle information. Thus it can manage all data created in each building stage and the roles of BIM are greatly expanded. Contractors and designers have been trying to adopt BIM to design competitions and validate it for the best result in various aspects. Via the computational simulation which differs from the existing process, effective evaluation can be done. For this process, a modeling guideline for each kind of BIM tool and a validation system for the confidential assessment are required. This paper explains a new process about design evaluation methods and process using BIM technologies which follow the new paradigm in construction industry through complement points by an example of a competition activity of the Korea Power Exchange(KPX) headquarter office. In conclusion, this paper provides a basic data input guideline based on open BIM for automatic assessment and interoperability between different BIM systems and suggests a practical usage of the rule-based Model Checker.