• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.2
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• pp.97-103
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• 2015

This paper introduces two on-going projects for DC high temperature superconducting (HTS) cable systems in South Korea. This study proposes the application of DC HTS cable systems to enhance power transfer limits of a grid-connected offshore wind farm. In order to develop the superconducting DC transmission system model based on HTS power cables, the maximum transfer limits from offshore wind farm are estimated and the system marginal price (SMP) calculated through a Two-Step Power Transfer (TSPT) model based on PV analysis and DC-optimal power flow. The proposed TSPT model will be applied to 2022 KEPCO systems with offshore wind farms.

• Korean System Dynamics Review
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• v.2 no.2
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• pp.25-40
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• 2001

This paper describes the forecast of power plant construction in a competitive korean electricity market. In Korea, KEPCO (Korea Electric Power Corporation, fully controlled by government) was responsible for from the production of the electricity to the sale of electricity to customer. However, the generation part is separated from KEPCO and six generation companies were established for whole sale competition from April 1st, 2001. The generation companies consist of five fossil power companies and one nuclear power company in Korea at present time. Fossil power companies are scheduled to be sold to private companies including foreign investors. Nuclear power company is owned and controlled by government. The competition in generation market will start from 2003. ISO (Independence System Operator will purchase the electricity from the power exchange market. The market price is determined by the SMP(System Marginal Price) which is decided by the balance between demand and supply of electricity in power exchange market. Under this uncertain circumstance, the energy policy planners such as government are interested to the construction of the power plant in the future. These interests are accelerated due to the recent shortage of electricity supply in California. In the competitive market, investors are no longer interested in the investment for the capital intensive, long lead time generating technologies such as nuclear and coal plants. Large unclear and coal plants were no longer the top choices. Instead, investors in the competitive market are interested in smaller, more efficient, cheaper, cleaner technologies such as CCGT(Combined Cycle Gas Turbine). Electricity is treated as commodity in the competitive market. The investors behavior in the commodity market shows that the new investment decision is made when the market price exceeds the sum of capital cost and variable cost of the new facility and the existing facility utilization depends on the marginal cost of the facility. This investors behavior can be applied to the new investments for the power plant. Under these postulations, there is the potential for power plant construction to appear in waves causing alternating periods of over and under supply of electricity like commodity production or real estate production. A computer model was developed to sturdy the possibility that construction will appear in waves of boom and bust in Korean electricity market. This model was constructed using System Dynamics method pioneered by Forrester(MIT, 1961) and explained in recent text by Sternman (Business Dynamics, MIT, 2000) and the recent work by Andrew Ford(Energy Policy, 1999). This model was designed based on the Energy Policy results(Ford, 1999) with parameters for loads and resources in Korea. This Korea Market Model was developed and tested in a small scale project to demonstrate the usefulness of the System Dynamics approach. Korea electricity market is isolated and not allowed to import electricity from outsides. In this model, the base load such as unclear and large coal power plant are assumed to be user specified investment and only CCGT is selected for new investment by investors in the market. This model may be used to learn if government investment in new unclear plants could compensate for the unstable actions of private developers. This model can be used to test the policy focused on the role of unclear investments over time. This model also can be used to test whether the future power plant construction can meet the government targets for the mix of generating resources and to test whether to maintain stable price in the spot market.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.08a
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• pp.308-312
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• 1999

Developed the model equations which calculate roll force, roll power during hot rolling in real time. The variables which mainly effect on the roll force, roll power are shape factor, reduction, roll diameter, roll velocity, strip inlet temperature, carbon content of strip and strip-roll contact friction coefficient. Among these variables roll diameter, roll velocity, inlet temperature, carbon content and friction coefficient can be excluded in interpolated model equation by introducing equation of die force(F'), power(p') of the frictionless uniform plane strain compression which can be calculated without iteration. At the case of coulomb friction coefficient of 0.3, we evaluated coefficient of polynomial equations of {{{{ { F} over {F' } }}}}, {{{{ { Pf} over {Pd }, { Pd} over {P' } }}}} from the result of finite element analysis using interpolation. It was found that the change of values of {{{{ { F} over {F' }, { P} over {P' } }}}} with the friction coefficient tend to straight line which slope depend only on shape factor. With these properties, developed model equations could be extended to other values of coulomb friction coefficient. To verify developed roll force, roll power model equation we compared the results from these model equation with the results from these model equation with the results from finite element analysis in factory process condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1497-1502
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• 2014

Electric load forecasting is essential for stable electric power supply, efficient operation and management of power systems, and safe operation of power generation systems. The results are utilized in generator preventive maintenance planning and the systemization of power reserve management. Development and improvement of electric load forecasting model is necessary for power system maintenance and operation. This paper proposes daily maximum electric load forecasting methods for the next 4 weeks with a seasonal autoregressive integrated moving average model and an exponential smoothing model. According to the results of forecasting of daily maximum electric load forecasting for the next 4 weeks of March, April, November 2010~2012 using the constructed forecasting models, the seasonal autoregressive integrated moving average model showed an average error rate of 6,66%, 5.26%, 3.61% respectively and the exponential smoothing model showed an average error rate of 3.82%, 4.07%, 3.59% respectively.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.3
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• pp.526-539
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• 2015

Stirling engines are regarded as an efficient and promising power system for underwater devices. Currently, many researches on one-dimensional model is used to evaluate thermodynamic performance of Stirling engine, but in which there are still some aspects which cannot be modeled with proper mathematical models such as mechanical loss or auxiliary power. In this paper, a four-cylinder double-acting Stirling engine for Unmanned Underwater Vehicles (UUVs) is discussed. And a one-dimensional model incorporated with empirical equations of mechanical loss and auxiliary power obtained from experiments is derived while referring to the Stirling engine computer model of National Aeronautics and Space Administration (NASA). The P-40 Stirling engine with sufficient testing results from NASA is utilized to validate the accuracy of this one-dimensional model. It shows that the maximum error of output power of theoretical analysis results is less than 18% over testing results, and the maximum error of input power is no more than 9%. Finally, a Stirling engine for UUVs is designed with Schmidt analysis method and the modified one-dimensional model, and the results indicate this designed engine is capable of showing desired output power.

• Journal of Power Electronics
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• v.1 no.1
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• pp.48-55
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• 2001

This paper describes a switching-level simulation model and scaled hardware model for SSSC, which is useful for analyzing the dynamic interaction between the SSC and the power transmission system. A detailed simulation model with EMTP was developed to verity the SSSC operation with control system, and its increasing capability of power transmission through the line for a typical one-machine infinite-bus system. The simulation results of the developed model are compared with the experimental results froma scaled model fo 2KVA rating for evaluation the whole system operation.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.376-385
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• 2009

Increasing the importance of Green IT brings low-power consumption requirements for embedded software into relief. This paper focus on the power analysis techniques of embedded software along with the trend. We survey the existing research on the power analysis techniques performed during the last decade, and find out some features or characteristics from the analysis approaches of those techniques. Also we summarize those characteristics into a systematic model, and then apply the model to embedded software development process using spider diagram. Our suggestion gives such benefits as improving the understanding of power analysis techniques, guiding the choice of an appropriate technique to their Power analysis, and forecasting the direction of technology changes in embedded software power analysis.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1821-1830
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• 2018

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.