• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1496-1503
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• 2009

The United Nations Framework Convention on Climate Change (UNFCCC) is an international environmental treaty to stabilize greenhouse gas concentrations in the atmosphere. In order to fulfil the commitments of the countries in an economically efficient way, the UNFCCC adapted the emission trading scheme in the Kyoto Protocol. If the UNFCCC's scheme is enforced in the country, considerable changes in electric power industry are expected due to the imposed greenhouse gas emission reduction. This paper proposes a game theoretic model of the case when generation companies participate in both competitive electricity market and emission market simultaneously. The model is designed such that generation companies select strategically between power quantity and greenhouse gas reduction to maximize their profits in both markets. Demand function and Environmental Welfare of emission trading market is proposed in this model. From the simulation results using the proposed model the impact of the emission trading on generation companies seems very severe in case that the emission prices are significantly high.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.809-812
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• 2007

Automatic generation method of structural analysis model data for a truss bridge is presented through 3-D bridge information modeling based on Industry Foundation Classes(IFC). The mapping schema is proposed between a steel bridge information model based on STEP and a truss bridge information model based on the IFC. The geometry information from mapping is presented by IFC model, and SAP 2000 that can import the IFC file performs the structural analysis. Numerical analysis for a truss bridge is performed in this paper.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1447-1456
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• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.6
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• pp.723-732
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• 2022

Cracking ammonia inside solid oxide fuel cell (SOFC) stack is a compact and simple way. To prevent sharp temperature fluctuation and increase cell efficiency, the decomposition reaction should be spread on whole cell area. This leading to a question that, how does anode thickness affect the conversion rate of ammonia and the cell voltage? Since the 0D model of SOFC is useful for system level simulation, how accurate is it to use equilibrium solver for internal ammonia cracking reaction? The 1D model of ammonia fed SOFC was used to simulate the diffusion and reaction of ammonia inside the anode electrode, then the partial pressure of hydrogen and steam at triple phase boundary was used for cell voltage calculation. The result shows that, the ammonia conversion rate increases and reaches saturated value as anode thickness increase, and the saturated thickness is bigger for lower operating temperature. The similar cell voltage between 1D and 0D models can be reached with NH₃ conversion rate above 90%. The 0D model and 1D model of SOFC showed similar conversion rate at temperature over 750℃.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.4
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• pp.169-179
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• 2017

In this study, we applied the wave generation method by relaxation method to the SWASH model, which is a non - hydrostatic numerical model, for stable and accurate wave generation of linear and nonlinear waves. To validate the relaxation wave generation method, we were simulated various wave, including the linear wave and nonliner wave and compared with analytical solution. As a result, the incident wave was successfully generated and propagated in all cases from Stokes waves to cnoidal wave. Also, we were confirmed that the wave height and the waveform were in good agreement with the analytical solution.

• Journal of the Korean Solar Energy Society
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• v.33 no.6
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.